Xiuwen Zheng
| Snapshot Date: 2021-10-14 04:50:12 -0400 (Thu, 14 Oct 2021) |
| git_url: https://git.bioconductor.org/packages/HIBAG |
| git_branch: RELEASE_3_13 |
| git_last_commit: dead91d |
| git_last_commit_date: 2021-05-19 12:11:05 -0400 (Wed, 19 May 2021) |
| Back to Multiple platform build/check report for BioC 3.13 |
|
This page was generated on 2021-10-15 15:06:07 -0400 (Fri, 15 Oct 2021).
|
To the developers/maintainers of the HIBAG package: - Please allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/HIBAG.git to reflect on this report. See How and When does the builder pull? When will my changes propagate? here for more information. - Make sure to use the following settings in order to reproduce any error or warning you see on this page. |
| Package 855/2041 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| HIBAG 1.28.0 (landing page) Xiuwen Zheng
| nebbiolo1 | Linux (Ubuntu 20.04.2 LTS) / x86_64 | OK | OK | OK | | ||||||||
| tokay2 | Windows Server 2012 R2 Standard / x64 | OK | OK | OK | OK | | ||||||||
| machv2 | macOS 10.14.6 Mojave / x86_64 | OK | OK | OK | OK | | ||||||||
| Package: HIBAG |
| Version: 1.28.0 |
| Command: C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:HIBAG.install-out.txt --library=C:\Users\biocbuild\bbs-3.13-bioc\R\library --no-vignettes --timings HIBAG_1.28.0.tar.gz |
| StartedAt: 2021-10-15 00:21:12 -0400 (Fri, 15 Oct 2021) |
| EndedAt: 2021-10-15 00:24:00 -0400 (Fri, 15 Oct 2021) |
| EllapsedTime: 168.1 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: HIBAG.Rcheck |
| Warnings: 0 |
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###
### Running command:
###
### C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:HIBAG.install-out.txt --library=C:\Users\biocbuild\bbs-3.13-bioc\R\library --no-vignettes --timings HIBAG_1.28.0.tar.gz
###
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* using log directory 'C:/Users/biocbuild/bbs-3.13-bioc/meat/HIBAG.Rcheck'
* using R version 4.1.1 (2021-08-10)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'HIBAG/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'HIBAG' version '1.28.0'
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'HIBAG' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking 'build' directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* loading checks for arch 'i386'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* loading checks for arch 'x64'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of 'data' directory ... OK
* checking data for non-ASCII characters ... OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking compilation flags in Makevars ... OK
* checking for GNU extensions in Makefiles ... NOTE
GNU make is a SystemRequirements.
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking use of PKG_*FLAGS in Makefiles ... OK
* checking compiled code ... NOTE
Note: information on .o files for i386 is not available
Note: information on .o files for x64 is not available
File 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/libs/i386/HIBAG.dll':
Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran)
Found 'exit', possibly from 'exit' (C), 'stop' (Fortran)
Found 'printf', possibly from 'printf' (C)
File 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/libs/x64/HIBAG.dll':
Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran)
Found 'exit', possibly from 'exit' (C), 'stop' (Fortran)
Found 'printf', possibly from 'printf' (C)
Compiled code should not call entry points which might terminate R nor
write to stdout/stderr instead of to the console, nor use Fortran I/O
nor system RNGs. The detected symbols are linked into the code but
might come from libraries and not actually be called.
See 'Writing portable packages' in the 'Writing R Extensions' manual.
* checking installed files from 'inst/doc' ... OK
* checking files in 'vignettes' ... OK
* checking examples ...
** running examples for arch 'i386' ... OK
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
hlaGenoLD 0.96 0 11.83
** running examples for arch 'x64' ... OK
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
** running tests for arch 'i386' ...
Running 'runTests.R'
OK
** running tests for arch 'x64' ...
Running 'runTests.R'
OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in 'inst/doc' ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: 2 NOTEs
See
'C:/Users/biocbuild/bbs-3.13-bioc/meat/HIBAG.Rcheck/00check.log'
for details.
HIBAG.Rcheck/00install.out
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###
### Running command:
###
### C:\cygwin\bin\curl.exe -O http://155.52.207.165/BBS/3.13/bioc/src/contrib/HIBAG_1.28.0.tar.gz && rm -rf HIBAG.buildbin-libdir && mkdir HIBAG.buildbin-libdir && C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=HIBAG.buildbin-libdir HIBAG_1.28.0.tar.gz && C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD INSTALL HIBAG_1.28.0.zip && rm HIBAG_1.28.0.tar.gz HIBAG_1.28.0.zip
###
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% Total % Received % Xferd Average Speed Time Time Time Current
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100 583k 100 583k 0 0 1407k 0 --:--:-- --:--:-- --:--:-- 1407k
install for i386
* installing *source* package 'HIBAG' ...
** using staged installation
** libs
"C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"c:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c HIBAG.cpp -o HIBAG.o
"C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"c:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c LibHLA.cpp -o LibHLA.o
"C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"c:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c LibHLA_ext_avx.cpp -o LibHLA_ext_avx.o
"C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"c:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c LibHLA_ext_avx2.cpp -o LibHLA_ext_avx2.o
"C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"c:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign LibHLA_ext_avx512bw.cpp -c -o LibHLA_ext_avx512bw.o
"C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"c:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign LibHLA_ext_avx512f.cpp -c -o LibHLA_ext_avx512f.o
"C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"c:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c LibHLA_ext_sse2.cpp -o LibHLA_ext_sse2.o
"C:/rtools40/mingw32/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"c:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c LibHLA_ext_sse4_2.cpp -o LibHLA_ext_sse4_2.o
C:/rtools40/mingw32/bin/g++ -shared -s -static-libgcc -o HIBAG.dll tmp.def HIBAG.o LibHLA.o LibHLA_ext_avx.o LibHLA_ext_avx2.o LibHLA_ext_avx512bw.o LibHLA_ext_avx512f.o LibHLA_ext_sse2.o LibHLA_ext_sse4_2.o -LC:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/lib/i386 -ltbb -ltbbmalloc -Lc:/extsoft/lib/i386 -Lc:/extsoft/lib -LC:/Users/BIOCBU~1/BBS-3~1.13-/R/bin/i386 -lR
installing to C:/Users/biocbuild/bbs-3.13-bioc/meat/HIBAG.buildbin-libdir/00LOCK-HIBAG/00new/HIBAG/libs/i386
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
converting help for package 'HIBAG'
finding HTML links ... done
HIBAG-package html
HLA_Type_Table html
HapMap_CEU_Geno html
hlaAASeqClass html
hlaAllele html
hlaAlleleClass html
hlaAlleleDigit html
hlaAlleleSubset html
hlaAlleleToVCF html
hlaAssocTest html
hlaAttrBagClass html
hlaAttrBagObj html
hlaAttrBagging html
hlaBED2Geno html
hlaCheckAllele html
hlaCheckSNPs html
hlaClose html
hlaCombineAllele html
hlaCombineModelObj html
hlaCompareAllele html
hlaConvSequence html
hlaDistance html
hlaFlankingSNP html
hlaGDS2Geno html
hlaGeno2PED html
hlaGenoAFreq html
hlaGenoCombine html
hlaGenoLD html
hlaGenoMFreq html
hlaGenoMRate html
hlaGenoMRate_Samp html
hlaGenoSubset html
hlaGenoSwitchStrand html
hlaLDMatrix html
hlaLociInfo html
hlaMakeSNPGeno html
hlaModelFiles html
hlaModelFromObj html
hlaOutOfBag html
hlaParallelAttrBagging html
hlaPredMerge html
hlaPredict html
hlaPublish html
hlaReport html
hlaReportPlot html
hlaSNPGenoClass html
hlaSNPID html
hlaSampleAllele html
hlaSetKernelTarget html
hlaSplitAllele html
hlaSubModelObj html
hlaUniqueAllele html
plot.hlaAttrBagObj html
print.hlaAttrBagClass html
summary.hlaAlleleClass html
summary.hlaSNPGenoClass html
** building package indices
** installing vignettes
'HIBAG.Rmd'
'HLA_Association.Rmd'
'Implementation.Rmd'
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
install for x64
* installing *source* package 'HIBAG' ...
** libs
"C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"C:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c HIBAG.cpp -o HIBAG.o
"C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"C:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c LibHLA.cpp -o LibHLA.o
"C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"C:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c LibHLA_ext_avx.cpp -o LibHLA_ext_avx.o
"C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"C:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c LibHLA_ext_avx2.cpp -o LibHLA_ext_avx2.o
"C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"C:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -ffixed-xmm16 -ffixed-xmm17 -ffixed-xmm18 -ffixed-xmm19 -ffixed-xmm20 -ffixed-xmm21 -ffixed-xmm22 -ffixed-xmm23 -ffixed-xmm24 -ffixed-xmm25 -ffixed-xmm26 -ffixed-xmm27 -ffixed-xmm28 -ffixed-xmm29 -ffixed-xmm30 -ffixed-xmm31 LibHLA_ext_avx512bw.cpp -c -o LibHLA_ext_avx512bw.o
"C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"C:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -ffixed-xmm16 -ffixed-xmm17 -ffixed-xmm18 -ffixed-xmm19 -ffixed-xmm20 -ffixed-xmm21 -ffixed-xmm22 -ffixed-xmm23 -ffixed-xmm24 -ffixed-xmm25 -ffixed-xmm26 -ffixed-xmm27 -ffixed-xmm28 -ffixed-xmm29 -ffixed-xmm30 -ffixed-xmm31 LibHLA_ext_avx512f.cpp -c -o LibHLA_ext_avx512f.o
"C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"C:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c LibHLA_ext_sse2.cpp -o LibHLA_ext_sse2.o
"C:/rtools40/mingw64/bin/"g++ -std=gnu++11 -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG -I'C:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/include' -I"C:/extsoft/include" -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c LibHLA_ext_sse4_2.cpp -o LibHLA_ext_sse4_2.o
C:/rtools40/mingw64/bin/g++ -shared -s -static-libgcc -o HIBAG.dll tmp.def HIBAG.o LibHLA.o LibHLA_ext_avx.o LibHLA_ext_avx2.o LibHLA_ext_avx512bw.o LibHLA_ext_avx512f.o LibHLA_ext_sse2.o LibHLA_ext_sse4_2.o -LC:/Users/biocbuild/bbs-3.13-bioc/R/library/RcppParallel/lib/x64 -ltbb -ltbbmalloc -LC:/extsoft/lib/x64 -LC:/extsoft/lib -LC:/Users/BIOCBU~1/BBS-3~1.13-/R/bin/x64 -lR
installing to C:/Users/biocbuild/bbs-3.13-bioc/meat/HIBAG.buildbin-libdir/HIBAG/libs/x64
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'HIBAG' as HIBAG_1.28.0.zip
* DONE (HIBAG)
* installing to library 'C:/Users/biocbuild/bbs-3.13-bioc/R/library'
package 'HIBAG' successfully unpacked and MD5 sums checked
|
HIBAG.Rcheck/tests_i386/runTests.Rout
R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> #############################################################
> #
> # DESCRIPTION: Unit tests in the HIBAG package
> #
>
> # load the HIBAG package
> library(HIBAG)
HIBAG (HLA Genotype Imputation with Attribute Bagging)
Kernel Version: v1.5 (32-bit, AVX2)
>
>
> #############################################################
>
> # a list of HLA genes
> hla.list <- c("A", "B", "C", "DQA1", "DQB1", "DRB1")
>
> # pre-defined lower bound of prediction accuracy
> hla.acc <- c(0.9, 0.8, 0.8, 0.8, 0.8, 0.7)
>
>
> for (hla.idx in seq_along(hla.list))
+ {
+ hla.id <- hla.list[hla.idx]
+
+ # make a "hlaAlleleClass" object
+ hla <- hlaAllele(HLA_Type_Table$sample.id,
+ H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")],
+ H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")],
+ locus=hla.id, assembly="hg19")
+
+ # divide HLA types randomly
+ set.seed(100)
+ hlatab <- hlaSplitAllele(hla, train.prop=0.5)
+
+ # SNP predictors within the flanking region on each side
+ region <- 500 # kb
+ snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id,
+ HapMap_CEU_Geno$snp.position,
+ hla.id, region*1000, assembly="hg19")
+
+ # training and validation genotypes
+ train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
+ snp.sel=match(snpid, HapMap_CEU_Geno$snp.id),
+ samp.sel=match(hlatab$training$value$sample.id,
+ HapMap_CEU_Geno$sample.id))
+ test.geno <- hlaGenoSubset(HapMap_CEU_Geno,
+ samp.sel=match(hlatab$validation$value$sample.id,
+ HapMap_CEU_Geno$sample.id))
+
+
+ # train a HIBAG model
+ set.seed(100)
+ model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=10)
+ summary(model)
+
+ # validation
+ pred <- hlaPredict(model, test.geno, type="response")
+ summary(pred)
+
+ # compare
+ comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
+ call.threshold=0)
+ print(comp$overall)
+
+ # check
+ if (comp$overall$acc.haplo < hla.acc[hla.idx])
+ stop("HLA - ", hla.id, ", 'acc.haplo' should be >= ", hla.acc[hla.idx], ".")
+
+ cat("\n\n")
+ }
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:09
=== building individual classifier 1, out-of-bag (11/32.4%) ===
[1] 2021-10-15 00:23:09, oob acc: 77.27%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
[2] 2021-10-15 00:23:09, oob acc: 88.46%, # of SNPs: 12, # of haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
[3] 2021-10-15 00:23:09, oob acc: 85.71%, # of SNPs: 11, # of haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
[4] 2021-10-15 00:23:09, oob acc: 75.00%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 5, out-of-bag (17/50.0%) ===
[5] 2021-10-15 00:23:09, oob acc: 79.41%, # of SNPs: 13, # of haplo: 34
=== building individual classifier 6, out-of-bag (11/32.4%) ===
[6] 2021-10-15 00:23:09, oob acc: 100.00%, # of SNPs: 19, # of haplo: 72
=== building individual classifier 7, out-of-bag (9/26.5%) ===
[7] 2021-10-15 00:23:09, oob acc: 100.00%, # of SNPs: 17, # of haplo: 37
=== building individual classifier 8, out-of-bag (13/38.2%) ===
[8] 2021-10-15 00:23:09, oob acc: 84.62%, # of SNPs: 14, # of haplo: 58
=== building individual classifier 9, out-of-bag (14/41.2%) ===
[9] 2021-10-15 00:23:10, oob acc: 89.29%, # of SNPs: 13, # of haplo: 34
=== building individual classifier 10, out-of-bag (13/38.2%) ===
[10] 2021-10-15 00:23:10, oob acc: 80.77%, # of SNPs: 14, # of haplo: 24
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004109150 0.0004156612 0.0004583766 0.0050417085 0.0096582452 0.0232052136
Max. Mean SD
0.4987174317 0.0470514279 0.1161981828
Accuracy with training data: 98.53%
Out-of-bag accuracy: 86.05%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 10
total # of SNPs used: 93
avg. # of SNPs in an individual classifier: 13.90
(sd: 2.38, min: 11, max: 19, median: 13.00)
avg. # of haplotypes in an individual classifier: 36.70
(sd: 17.93, min: 14, max: 72, median: 34.00)
avg. out-of-bag accuracy: 86.05%
(sd: 8.68%, min: 75.00%, max: 100.00%, median: 85.16%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004109150 0.0004156612 0.0004583766 0.0050417085 0.0096582452 0.0232052136
Max. Mean SD
0.4987174317 0.0470514279 0.1161981828
Genome assembly: hg19
HIBAG model for HLA-A:
10 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:10) 0%
Predicting (2021-10-15 00:23:10) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 3 (11.5%) 4 (15.4%) 18 (69.2%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.002746 0.006607 0.031587 0.023928 0.498717
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 25 51 0.9615385 0.9807692 0
n.call call.rate
1 26 1
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 1 monomorphic SNP
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 340
# of samples: 28
# of unique HLA alleles: 22
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:10
=== building individual classifier 1, out-of-bag (12/42.9%) ===
[1] 2021-10-15 00:23:10, oob acc: 58.33%, # of SNPs: 17, # of haplo: 52
=== building individual classifier 2, out-of-bag (11/39.3%) ===
[2] 2021-10-15 00:23:10, oob acc: 63.64%, # of SNPs: 18, # of haplo: 51
=== building individual classifier 3, out-of-bag (13/46.4%) ===
[3] 2021-10-15 00:23:10, oob acc: 50.00%, # of SNPs: 15, # of haplo: 29
=== building individual classifier 4, out-of-bag (11/39.3%) ===
[4] 2021-10-15 00:23:10, oob acc: 59.09%, # of SNPs: 12, # of haplo: 57
=== building individual classifier 5, out-of-bag (11/39.3%) ===
[5] 2021-10-15 00:23:10, oob acc: 63.64%, # of SNPs: 15, # of haplo: 86
=== building individual classifier 6, out-of-bag (12/42.9%) ===
[6] 2021-10-15 00:23:10, oob acc: 79.17%, # of SNPs: 18, # of haplo: 66
=== building individual classifier 7, out-of-bag (12/42.9%) ===
[7] 2021-10-15 00:23:10, oob acc: 70.83%, # of SNPs: 15, # of haplo: 86
=== building individual classifier 8, out-of-bag (9/32.1%) ===
[8] 2021-10-15 00:23:10, oob acc: 77.78%, # of SNPs: 16, # of haplo: 117
=== building individual classifier 9, out-of-bag (9/32.1%) ===
[9] 2021-10-15 00:23:11, oob acc: 77.78%, # of SNPs: 18, # of haplo: 92
=== building individual classifier 10, out-of-bag (9/32.1%) ===
[10] 2021-10-15 00:23:11, oob acc: 61.11%, # of SNPs: 15, # of haplo: 72
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
9.567411e-05 9.622439e-05 1.011769e-04 3.071775e-03 7.279682e-03 1.186415e-02
Max. Mean SD
1.196521e-01 1.281211e-02 2.267322e-02
Accuracy with training data: 100.00%
Out-of-bag accuracy: 66.14%
Gene: HLA-B
Training dataset: 28 samples X 340 SNPs
# of HLA alleles: 22
# of individual classifiers: 10
total # of SNPs used: 118
avg. # of SNPs in an individual classifier: 15.90
(sd: 1.91, min: 12, max: 18, median: 15.50)
avg. # of haplotypes in an individual classifier: 70.80
(sd: 25.28, min: 29, max: 117, median: 69.00)
avg. out-of-bag accuracy: 66.14%
(sd: 9.84%, min: 50.00%, max: 79.17%, median: 63.64%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
9.567411e-05 9.622439e-05 1.011769e-04 3.071775e-03 7.279682e-03 1.186415e-02
Max. Mean SD
1.196521e-01 1.281211e-02 2.267322e-02
Genome assembly: hg19
HIBAG model for HLA-B:
10 individual classifiers
340 SNPs
22 unique HLA alleles: 07:02, 08:01, 13:02, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 15
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:11) 0%
Predicting (2021-10-15 00:23:11) 100%
Gene: HLA-B
Range: [31321649bp, 31324989bp] on hg19
# of samples: 15
# of unique HLA alleles: 9
# of unique HLA genotypes: 12
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
3 (20.0%) 5 (33.3%) 3 (20.0%) 4 (26.7%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
2.000e-08 4.068e-05 2.934e-03 1.789e-02 6.076e-03 1.326e-01
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 15 11 25 0.7333333 0.8333333 0
n.call call.rate
1 15 1
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 2 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 354
# of samples: 36
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:11
=== building individual classifier 1, out-of-bag (13/36.1%) ===
[1] 2021-10-15 00:23:11, oob acc: 80.77%, # of SNPs: 19, # of haplo: 40
=== building individual classifier 2, out-of-bag (11/30.6%) ===
[2] 2021-10-15 00:23:11, oob acc: 90.91%, # of SNPs: 32, # of haplo: 32
=== building individual classifier 3, out-of-bag (14/38.9%) ===
[3] 2021-10-15 00:23:11, oob acc: 89.29%, # of SNPs: 19, # of haplo: 43
=== building individual classifier 4, out-of-bag (13/36.1%) ===
[4] 2021-10-15 00:23:11, oob acc: 84.62%, # of SNPs: 19, # of haplo: 72
=== building individual classifier 5, out-of-bag (10/27.8%) ===
[5] 2021-10-15 00:23:11, oob acc: 90.00%, # of SNPs: 19, # of haplo: 66
=== building individual classifier 6, out-of-bag (10/27.8%) ===
[6] 2021-10-15 00:23:11, oob acc: 95.00%, # of SNPs: 21, # of haplo: 59
=== building individual classifier 7, out-of-bag (16/44.4%) ===
[7] 2021-10-15 00:23:12, oob acc: 90.62%, # of SNPs: 18, # of haplo: 25
=== building individual classifier 8, out-of-bag (14/38.9%) ===
[8] 2021-10-15 00:23:12, oob acc: 89.29%, # of SNPs: 23, # of haplo: 57
=== building individual classifier 9, out-of-bag (13/36.1%) ===
[9] 2021-10-15 00:23:12, oob acc: 84.62%, # of SNPs: 18, # of haplo: 39
=== building individual classifier 10, out-of-bag (14/38.9%) ===
[10] 2021-10-15 00:23:12, oob acc: 89.29%, # of SNPs: 35, # of haplo: 62
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0007653736 0.0007682927 0.0007945643 0.0017428621 0.0044732464 0.0093175730
Max. Mean SD
0.0703539734 0.0088728477 0.0132051834
Accuracy with training data: 100.00%
Out-of-bag accuracy: 88.44%
Gene: HLA-C
Training dataset: 36 samples X 354 SNPs
# of HLA alleles: 17
# of individual classifiers: 10
total # of SNPs used: 135
avg. # of SNPs in an individual classifier: 22.30
(sd: 6.13, min: 18, max: 35, median: 19.00)
avg. # of haplotypes in an individual classifier: 49.50
(sd: 15.74, min: 25, max: 72, median: 50.00)
avg. out-of-bag accuracy: 88.44%
(sd: 4.04%, min: 80.77%, max: 95.00%, median: 89.29%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0007653736 0.0007682927 0.0007945643 0.0017428621 0.0044732464 0.0093175730
Max. Mean SD
0.0703539734 0.0088728477 0.0132051834
Genome assembly: hg19
HIBAG model for HLA-C:
10 individual classifiers
354 SNPs
17 unique HLA alleles: 01:02, 02:02, 03:03, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 24
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:12) 0%
Predicting (2021-10-15 00:23:12) 100%
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 24
# of unique HLA alleles: 14
# of unique HLA genotypes: 19
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
2 (8.3%) 3 (12.5%) 6 (25.0%) 13 (54.2%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0000000 0.0002058 0.0058893 0.0035911 0.0468290
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 24 16 39 0.6666667 0.8125 0
n.call call.rate
1 24 1
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 4 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 345
# of samples: 31
# of unique HLA alleles: 7
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:12
=== building individual classifier 1, out-of-bag (11/35.5%) ===
[1] 2021-10-15 00:23:12, oob acc: 95.45%, # of SNPs: 11, # of haplo: 22
=== building individual classifier 2, out-of-bag (11/35.5%) ===
[2] 2021-10-15 00:23:12, oob acc: 100.00%, # of SNPs: 13, # of haplo: 22
=== building individual classifier 3, out-of-bag (15/48.4%) ===
[3] 2021-10-15 00:23:12, oob acc: 83.33%, # of SNPs: 15, # of haplo: 23
=== building individual classifier 4, out-of-bag (14/45.2%) ===
[4] 2021-10-15 00:23:12, oob acc: 82.14%, # of SNPs: 8, # of haplo: 14
=== building individual classifier 5, out-of-bag (13/41.9%) ===
[5] 2021-10-15 00:23:12, oob acc: 88.46%, # of SNPs: 11, # of haplo: 34
=== building individual classifier 6, out-of-bag (10/32.3%) ===
[6] 2021-10-15 00:23:12, oob acc: 90.00%, # of SNPs: 11, # of haplo: 21
=== building individual classifier 7, out-of-bag (13/41.9%) ===
[7] 2021-10-15 00:23:12, oob acc: 92.31%, # of SNPs: 14, # of haplo: 23
=== building individual classifier 8, out-of-bag (13/41.9%) ===
[8] 2021-10-15 00:23:12, oob acc: 96.15%, # of SNPs: 11, # of haplo: 16
=== building individual classifier 9, out-of-bag (14/45.2%) ===
[9] 2021-10-15 00:23:12, oob acc: 89.29%, # of SNPs: 12, # of haplo: 19
=== building individual classifier 10, out-of-bag (11/35.5%) ===
[10] 2021-10-15 00:23:12, oob acc: 86.36%, # of SNPs: 8, # of haplo: 13
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.001972961 0.001998819 0.002231547 0.005363515 0.008831104 0.018431530
Max. Mean SD
0.537093886 0.028877632 0.094687228
Accuracy with training data: 96.77%
Out-of-bag accuracy: 90.35%
Gene: HLA-DQA1
Training dataset: 31 samples X 345 SNPs
# of HLA alleles: 7
# of individual classifiers: 10
total # of SNPs used: 80
avg. # of SNPs in an individual classifier: 11.40
(sd: 2.27, min: 8, max: 15, median: 11.00)
avg. # of haplotypes in an individual classifier: 20.70
(sd: 5.96, min: 13, max: 34, median: 21.50)
avg. out-of-bag accuracy: 90.35%
(sd: 5.72%, min: 82.14%, max: 100.00%, median: 89.64%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.001972961 0.001998819 0.002231547 0.005363515 0.008831104 0.018431530
Max. Mean SD
0.537093886 0.028877632 0.094687228
Genome assembly: hg19
HIBAG model for HLA-DQA1:
10 individual classifiers
345 SNPs
7 unique HLA alleles: 01:01, 01:02, 01:03, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 29
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:12) 0%
Predicting (2021-10-15 00:23:12) 100%
Gene: HLA-DQA1
Range: [32605169bp, 32612152bp] on hg19
# of samples: 29
# of unique HLA alleles: 6
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
5 (17.2%) 5 (17.2%) 2 (6.9%) 17 (58.6%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000001 0.0019253 0.0069908 0.0532601 0.0167536 0.5404845
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 29 21 49 0.7241379 0.8448276 0
n.call call.rate
1 29 1
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 6 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 350
# of samples: 34
# of unique HLA alleles: 12
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:12
=== building individual classifier 1, out-of-bag (11/32.4%) ===
[1] 2021-10-15 00:23:12, oob acc: 86.36%, # of SNPs: 13, # of haplo: 34
=== building individual classifier 2, out-of-bag (13/38.2%) ===
[2] 2021-10-15 00:23:12, oob acc: 76.92%, # of SNPs: 21, # of haplo: 42
=== building individual classifier 3, out-of-bag (13/38.2%) ===
[3] 2021-10-15 00:23:12, oob acc: 80.77%, # of SNPs: 10, # of haplo: 17
=== building individual classifier 4, out-of-bag (13/38.2%) ===
[4] 2021-10-15 00:23:13, oob acc: 92.31%, # of SNPs: 22, # of haplo: 78
=== building individual classifier 5, out-of-bag (13/38.2%) ===
[5] 2021-10-15 00:23:13, oob acc: 92.31%, # of SNPs: 11, # of haplo: 40
=== building individual classifier 6, out-of-bag (14/41.2%) ===
[6] 2021-10-15 00:23:13, oob acc: 71.43%, # of SNPs: 8, # of haplo: 22
=== building individual classifier 7, out-of-bag (14/41.2%) ===
[7] 2021-10-15 00:23:13, oob acc: 71.43%, # of SNPs: 14, # of haplo: 53
=== building individual classifier 8, out-of-bag (11/32.4%) ===
[8] 2021-10-15 00:23:13, oob acc: 86.36%, # of SNPs: 14, # of haplo: 40
=== building individual classifier 9, out-of-bag (14/41.2%) ===
[9] 2021-10-15 00:23:13, oob acc: 100.00%, # of SNPs: 16, # of haplo: 56
=== building individual classifier 10, out-of-bag (13/38.2%) ===
[10] 2021-10-15 00:23:13, oob acc: 88.46%, # of SNPs: 14, # of haplo: 34
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003282346 0.0003687353 0.0007332412 0.0038570393 0.0073528147 0.0148594626
Max. Mean SD
0.3073781820 0.0225078064 0.0573939534
Accuracy with training data: 98.53%
Out-of-bag accuracy: 84.64%
Gene: HLA-DQB1
Training dataset: 34 samples X 350 SNPs
# of HLA alleles: 12
# of individual classifiers: 10
total # of SNPs used: 99
avg. # of SNPs in an individual classifier: 14.30
(sd: 4.45, min: 8, max: 22, median: 14.00)
avg. # of haplotypes in an individual classifier: 41.60
(sd: 17.55, min: 17, max: 78, median: 40.00)
avg. out-of-bag accuracy: 84.64%
(sd: 9.41%, min: 71.43%, max: 100.00%, median: 86.36%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003282346 0.0003687353 0.0007332412 0.0038570393 0.0073528147 0.0148594626
Max. Mean SD
0.3073781820 0.0225078064 0.0573939534
Genome assembly: hg19
HIBAG model for HLA-DQB1:
10 individual classifiers
350 SNPs
12 unique HLA alleles: 02:01, 02:02, 03:01, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:13) 0%
Predicting (2021-10-15 00:23:13) 100%
Gene: HLA-DQB1
Range: [32627241bp, 32634466bp] on hg19
# of samples: 26
# of unique HLA alleles: 10
# of unique HLA genotypes: 17
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
3 (11.5%) 7 (26.9%) 5 (19.2%) 11 (42.3%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0002253 0.0018486 0.0308488 0.0099906 0.4023552
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 21 46 0.8076923 0.8846154 0
n.call call.rate
1 26 1
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 5 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 18
# of SNPs: 322
# of samples: 35
# of unique HLA alleles: 20
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:13
=== building individual classifier 1, out-of-bag (15/42.9%) ===
[1] 2021-10-15 00:23:13, oob acc: 70.00%, # of SNPs: 17, # of haplo: 77
=== building individual classifier 2, out-of-bag (16/45.7%) ===
[2] 2021-10-15 00:23:14, oob acc: 68.75%, # of SNPs: 22, # of haplo: 119
=== building individual classifier 3, out-of-bag (15/42.9%) ===
[3] 2021-10-15 00:23:14, oob acc: 73.33%, # of SNPs: 19, # of haplo: 33
=== building individual classifier 4, out-of-bag (13/37.1%) ===
[4] 2021-10-15 00:23:14, oob acc: 84.62%, # of SNPs: 18, # of haplo: 67
=== building individual classifier 5, out-of-bag (11/31.4%) ===
[5] 2021-10-15 00:23:14, oob acc: 86.36%, # of SNPs: 24, # of haplo: 127
=== building individual classifier 6, out-of-bag (12/34.3%) ===
[6] 2021-10-15 00:23:14, oob acc: 66.67%, # of SNPs: 18, # of haplo: 102
=== building individual classifier 7, out-of-bag (10/28.6%) ===
[7] 2021-10-15 00:23:15, oob acc: 75.00%, # of SNPs: 15, # of haplo: 71
=== building individual classifier 8, out-of-bag (15/42.9%) ===
[8] 2021-10-15 00:23:15, oob acc: 70.00%, # of SNPs: 15, # of haplo: 32
=== building individual classifier 9, out-of-bag (12/34.3%) ===
[9] 2021-10-15 00:23:15, oob acc: 91.67%, # of SNPs: 20, # of haplo: 93
=== building individual classifier 10, out-of-bag (15/42.9%) ===
[10] 2021-10-15 00:23:15, oob acc: 66.67%, # of SNPs: 15, # of haplo: 57
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.041155e-05 3.593240e-05 1.756200e-04 1.611725e-03 2.836751e-03 7.180633e-03
Max. Mean SD
4.558788e-01 4.152181e-02 1.239405e-01
Accuracy with training data: 94.29%
Out-of-bag accuracy: 75.31%
Gene: HLA-DRB1
Training dataset: 35 samples X 322 SNPs
# of HLA alleles: 20
# of individual classifiers: 10
total # of SNPs used: 129
avg. # of SNPs in an individual classifier: 18.30
(sd: 3.06, min: 15, max: 24, median: 18.00)
avg. # of haplotypes in an individual classifier: 77.80
(sd: 32.72, min: 32, max: 127, median: 74.00)
avg. out-of-bag accuracy: 75.31%
(sd: 9.00%, min: 66.67%, max: 91.67%, median: 71.67%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.041155e-05 3.593240e-05 1.756200e-04 1.611725e-03 2.836751e-03 7.180633e-03
Max. Mean SD
4.558788e-01 4.152181e-02 1.239405e-01
Genome assembly: hg19
HIBAG model for HLA-DRB1:
10 individual classifiers
322 SNPs
20 unique HLA alleles: 01:01, 01:03, 03:01, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 25
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:15) 0%
Predicting (2021-10-15 00:23:15) 100%
Gene: HLA-DRB1
Range: [32546546bp, 32557613bp] on hg19
# of samples: 25
# of unique HLA alleles: 10
# of unique HLA genotypes: 17
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
4 (16.0%) 5 (20.0%) 9 (36.0%) 7 (28.0%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0001451 0.0007388 0.0088345 0.0026166 0.1725407
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 25 16 40 0.64 0.8 0
n.call call.rate
1 25 1
>
>
>
> #############################################################
>
> {
+ function.list <- readRDS(
+ system.file("Meta", "Rd.rds", package="HIBAG"))$Name
+
+ sapply(function.list, FUN = function(func.name)
+ {
+ args <- list(
+ topic = func.name,
+ package = "HIBAG",
+ echo = FALSE,
+ verbose = FALSE,
+ ask = FALSE
+ )
+ suppressWarnings(do.call(example, args))
+ NULL
+ })
+ invisible()
+ }
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:15
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 211, loss: 196.4, oob acc: 54.55%, # of haplo: 13
2, SNP: 66, loss: 173.548, oob acc: 63.64%, # of haplo: 13
3, SNP: 177, loss: 136.352, oob acc: 68.18%, # of haplo: 13
4, SNP: 108, loss: 95.8359, oob acc: 72.73%, # of haplo: 13
5, SNP: 127, loss: 67.3216, oob acc: 77.27%, # of haplo: 13
6, SNP: 95, loss: 47.5888, oob acc: 77.27%, # of haplo: 13
7, SNP: 33, loss: 37.2631, oob acc: 77.27%, # of haplo: 16
8, SNP: 6, loss: 29.7419, oob acc: 77.27%, # of haplo: 18
9, SNP: 208, loss: 25.6913, oob acc: 77.27%, # of haplo: 19
10, SNP: 225, loss: 25.3087, oob acc: 77.27%, # of haplo: 21
11, SNP: 11, loss: 24.8356, oob acc: 77.27%, # of haplo: 23
12, SNP: 151, loss: 19.4134, oob acc: 77.27%, # of haplo: 23
13, SNP: 199, loss: 17.011, oob acc: 77.27%, # of haplo: 23
[1] 2021-10-15 00:23:15, oob acc: 77.27%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
1, SNP: 160, loss: 221.236, oob acc: 76.92%, # of haplo: 17
2, SNP: 145, loss: 173.538, oob acc: 80.77%, # of haplo: 23
3, SNP: 177, loss: 128.58, oob acc: 84.62%, # of haplo: 31
4, SNP: 111, loss: 79.6877, oob acc: 84.62%, # of haplo: 31
5, SNP: 207, loss: 52.5557, oob acc: 88.46%, # of haplo: 32
6, SNP: 245, loss: 41.8731, oob acc: 88.46%, # of haplo: 34
7, SNP: 230, loss: 31.7937, oob acc: 88.46%, # of haplo: 38
8, SNP: 151, loss: 20.4566, oob acc: 88.46%, # of haplo: 36
9, SNP: 14, loss: 19.5805, oob acc: 88.46%, # of haplo: 42
10, SNP: 132, loss: 19.5101, oob acc: 88.46%, # of haplo: 42
11, SNP: 221, loss: 19.485, oob acc: 88.46%, # of haplo: 44
12, SNP: 251, loss: 18.5695, oob acc: 88.46%, # of haplo: 48
[2] 2021-10-15 00:23:15, oob acc: 88.46%, # of SNPs: 12, # of haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 191, loss: 193.067, oob acc: 57.14%, # of haplo: 11
2, SNP: 264, loss: 150.427, oob acc: 64.29%, # of haplo: 12
3, SNP: 132, loss: 93.4067, oob acc: 67.86%, # of haplo: 12
4, SNP: 128, loss: 39.8353, oob acc: 71.43%, # of haplo: 12
5, SNP: 160, loss: 28.2998, oob acc: 75.00%, # of haplo: 12
6, SNP: 144, loss: 13.635, oob acc: 75.00%, # of haplo: 12
7, SNP: 111, loss: 6.04609, oob acc: 75.00%, # of haplo: 12
8, SNP: 40, loss: 6.04583, oob acc: 82.14%, # of haplo: 14
9, SNP: 141, loss: 6.04583, oob acc: 85.71%, # of haplo: 14
10, SNP: 73, loss: 2.9038, oob acc: 85.71%, # of haplo: 14
11, SNP: 199, loss: 2.20025, oob acc: 85.71%, # of haplo: 14
[3] 2021-10-15 00:23:15, oob acc: 85.71%, # of SNPs: 11, # of haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
1, SNP: 147, loss: 158.631, oob acc: 50.00%, # of haplo: 12
2, SNP: 152, loss: 140.375, oob acc: 55.00%, # of haplo: 13
3, SNP: 78, loss: 115.887, oob acc: 60.00%, # of haplo: 16
4, SNP: 115, loss: 77.8082, oob acc: 60.00%, # of haplo: 18
5, SNP: 148, loss: 62.6831, oob acc: 65.00%, # of haplo: 18
6, SNP: 13, loss: 46.5657, oob acc: 75.00%, # of haplo: 20
7, SNP: 109, loss: 31.0312, oob acc: 75.00%, # of haplo: 20
8, SNP: 176, loss: 22.5073, oob acc: 75.00%, # of haplo: 21
9, SNP: 145, loss: 20.9122, oob acc: 75.00%, # of haplo: 21
10, SNP: 128, loss: 20.6728, oob acc: 75.00%, # of haplo: 21
11, SNP: 73, loss: 14.6217, oob acc: 75.00%, # of haplo: 22
12, SNP: 151, loss: 10.2879, oob acc: 75.00%, # of haplo: 23
13, SNP: 199, loss: 8.74645, oob acc: 75.00%, # of haplo: 23
[4] 2021-10-15 00:23:15, oob acc: 75.00%, # of SNPs: 13, # of haplo: 23
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Accuracy with training data: 97.06%
Out-of-bag accuracy: 81.61%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 38
avg. # of SNPs in an individual classifier: 12.25
(sd: 0.96, min: 11, max: 13, median: 12.50)
avg. # of haplotypes in an individual classifier: 27.00
(sd: 14.63, min: 14, max: 48, median: 23.00)
avg. out-of-bag accuracy: 81.61%
(sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:15) 0%
Predicting (2021-10-15 00:23:15) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 4 (15.4%) 4 (15.4%) 17 (65.4%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.002097 0.007711 0.032190 0.028032 0.471142
Dosages:
$dosage - num [1:14, 1:26] 2.50e-01 2.16e-04 2.50e-06 7.31e-01 1.11e-14 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:14] "01:01" "02:01" "02:06" "03:01" ...
..$ : chr [1:26] "NA11881" "NA11992" "NA11994" "NA12249" ...
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:15) 0%
Predicting (2021-10-15 00:23:15) 100%
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed' (the individual-major mode)
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam'
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim'
Import 3932 SNPs within the xMHC region on chromosome 6
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 90
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:15) 0%
Predicting (2021-10-15 00:23:15) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
using the default genome assembly (assembly="hg19")
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 12
# of unique HLA genotypes: 28
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 100
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 32 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 40
# of SNPs: 1532
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:15
=== building individual classifier 1, out-of-bag (23/38.3%) ===
[1] 2021-10-15 00:23:16, oob acc: 78.26%, # of SNPs: 16, # of haplo: 93
=== building individual classifier 2, out-of-bag (24/40.0%) ===
[2] 2021-10-15 00:23:17, oob acc: 93.75%, # of SNPs: 21, # of haplo: 88
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.481068e-19 2.724336e-19 4.913754e-19 1.389214e-04 6.397680e-04 2.980482e-03
Max. Mean SD
1.226562e-01 7.012898e-03 2.176036e-02
Accuracy with training data: 98.33%
Out-of-bag accuracy: 86.01%
Gene: HLA-A
Training dataset: 60 samples X 1532 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 36
avg. # of SNPs in an individual classifier: 18.50
(sd: 3.54, min: 16, max: 21, median: 18.50)
avg. # of haplotypes in an individual classifier: 90.50
(sd: 3.54, min: 88, max: 93, median: 90.50)
avg. out-of-bag accuracy: 86.01%
(sd: 10.95%, min: 78.26%, max: 93.75%, median: 86.01%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.481068e-19 2.724336e-19 4.913754e-19 1.389214e-04 6.397680e-04 2.980482e-03
Max. Mean SD
1.226562e-01 7.012898e-03 2.176036e-02
Genome assembly: hg19
HIBAG model for HLA-A:
2 individual classifiers
1532 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 60
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:17) 0%
Predicting (2021-10-15 00:23:17) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 13
# of unique HLA genotypes: 28
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (1.7%) 10 (16.7%) 5 (8.3%) 44 (73.3%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0001389 0.0006398 0.0070129 0.0029805 0.1226562
Dosages:
$dosage - num [1:14, 1:60] 1.00 1.80e-10 7.81e-18 5.00e-06 1.25e-06 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:14] "01:01" "02:01" "02:06" "03:01" ...
..$ : chr [1:60] "NA11882" "NA11881" "NA11993" "NA11992" ...
Convert to dosage VCF format:
# of samples: 4
# of unique HLA alleles: 5
output: <connection>
##fileformat=VCFv4.0
##fileDate=20211015
##source=HIBAG
##FILTER=<ID=PASS,Description="All filters passed">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=DS,Number=1,Type=Float,Description="Dosage of HLA allele">
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA11882 NA11881 NA11993 NA11992
6 29911954 HLA-A*01:01 A P_0101 . PASS . GT:DS 1/0:1.0000e+00 0/0:5.1764e-14 0/0:2.3978e-11 1/0:1.0000e+00
6 29911954 HLA-A*02:01 A P_0201 . PASS . GT:DS 0/0:1.7996e-10 0/0:2.3569e-14 0/0:8.4571e-07 0/1:1.0000e+00
6 29911954 HLA-A*03:01 A P_0301 . PASS . GT:DS 0/0:5.0000e-06 1/0:9.9999e-01 0/0:3.8461e-01 0/0:1.0557e-16
6 29911954 HLA-A*26:01 A P_2601 . PASS . GT:DS 0/0:7.8140e-18 0/1:5.0000e-01 1/0:7.5000e-01 0/0:2.4148e-13
6 29911954 HLA-A*29:02 A P_2902 . PASS . GT:DS 0/1:5.0000e-01 0/0:1.1875e-35 0/1:5.0000e-01 0/0:5.7690e-34
dominant model:
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042*
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000
02:01 25 35 52.0 48.6 0.0000 1.000 1.000
02:06 59 1 50.8 0.0 0.0000 1.000 1.000
03:01 51 9 49.0 55.6 0.0000 1.000 1.000
11:01 55 5 50.9 40.0 0.0000 1.000 1.000
23:01 58 2 50.0 50.0 0.0000 1.000 1.000
24:03 59 1 50.8 0.0 0.0000 1.000 1.000
25:01 55 5 52.7 20.0 0.8727 0.350 0.353
26:01 57 3 52.6 0.0 1.4035 0.236 0.237
29:02 56 4 51.8 25.0 0.2679 0.605 0.612
31:01 57 3 49.1 66.7 0.0000 1.000 1.000
32:01 56 4 46.4 100.0 2.4107 0.121 0.112
68:01 57 3 52.6 0.0 1.4035 0.236 0.237
additive model:
[-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p
01:01 95 25 50.5 48.0 0.0000 1.000 1.000
02:01 77 43 48.1 53.5 0.1450 0.703 0.704
02:06 119 1 50.4 0.0 0.0000 1.000 1.000
03:01 111 9 49.5 55.6 0.0000 1.000 1.000
11:01 115 5 50.4 40.0 0.0000 1.000 1.000
23:01 117 3 50.4 33.3 0.0000 1.000 1.000
24:02 109 11 46.8 81.8 3.6030 0.058 0.053
24:03 119 1 50.4 0.0 0.0000 1.000 1.000
25:01 115 5 51.3 20.0 0.8348 0.361 0.364
26:01 117 3 51.3 0.0 1.3675 0.242 0.244
29:02 116 4 50.9 25.0 0.2586 0.611 0.619
31:01 117 3 49.6 66.7 0.0000 1.000 1.000
32:01 116 4 48.3 100.0 2.3276 0.127 0.119
68:01 117 3 51.3 0.0 1.3675 0.242 0.244
recessive model:
[-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p
01:01 59 1 50.8 0 0.000 1.000 1.000
02:01 52 8 46.2 75 1.298 0.255 0.254
02:06 60 0 50.0 . . . .
03:01 60 0 50.0 . . . .
11:01 60 0 50.0 . . . .
23:01 59 1 50.8 0 0.000 1.000 1.000
24:02 60 0 50.0 . . . .
24:03 60 0 50.0 . . . .
25:01 60 0 50.0 . . . .
26:01 60 0 50.0 . . . .
29:02 60 0 50.0 . . . .
31:01 60 0 50.0 . . . .
32:01 60 0 50.0 . . . .
68:01 60 0 50.0 . . . .
genotype model:
[-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042*
-----
01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000
02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271
02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000
11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000
23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000
24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353
26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612
31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000
32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112
68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
dominant model:
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p
01:01 36 24 -0.14684 -0.117427 0.909
02:01 25 35 -0.32331 -0.000618 0.190
02:06 59 1 -0.14024 0.170057 .
03:01 51 9 -0.05600 -0.583178 0.147
11:01 55 5 -0.19188 0.489815 0.287
23:01 58 2 -0.15400 0.413687 0.281
24:02 49 11 -0.10486 -0.269664 0.537
24:03 59 1 -0.11409 -1.373118 .
25:01 55 5 -0.12237 -0.274749 0.742
26:01 57 3 -0.12473 -0.331558 0.690
29:02 56 4 -0.13044 -0.199941 0.789
31:01 57 3 -0.10097 -0.783003 0.607
32:01 56 4 -0.07702 -0.947791 0.092
68:01 57 3 -0.16915 0.512457 0.196
genotype model:
[-/-] [-/h] [h/h] avg.[-/-] avg.[-/h] avg.[h/h] anova.p
01:01 36 23 1 -0.14684 -0.08833 -0.78655 0.784
02:01 25 27 8 -0.32331 -0.02341 0.07631 0.446
02:06 59 1 0 -0.14024 0.17006 . 0.756
03:01 51 9 0 -0.05600 -0.58318 . 0.138
11:01 55 5 0 -0.19188 0.48981 . 0.137
23:01 58 1 1 -0.15400 0.10762 0.71975 0.663
24:02 49 11 0 -0.10486 -0.26966 . 0.618
24:03 59 1 0 -0.11409 -1.37312 . 0.205
25:01 55 5 0 -0.12237 -0.27475 . 0.742
26:01 57 3 0 -0.12473 -0.33156 . 0.725
29:02 56 4 0 -0.13044 -0.19994 . 0.892
31:01 57 3 0 -0.10097 -0.78300 . 0.243
32:01 56 4 0 -0.07702 -0.94779 . 0.086
68:01 57 3 0 -0.16915 0.51246 . 0.243
Logistic regression (dominant model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.792e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -8.777e-16
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.372e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.624e-01
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.418e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 2.874e-15
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.495e+00
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.170e+00
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.282e-01
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.771e+01
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
h.2.5% h.97.5% h.pval
24:02 0.1585 3.4251 0.032*
-----
01:01 -1.0330 1.0330 1.000
02:01 -1.1643 0.8899 0.793
02:06 -2868.1268 2836.9268 0.991
03:01 -1.1624 1.6872 0.718
11:01 -2.3074 1.4237 0.643
23:01 -2.8192 2.8192 1.000
24:03 -2868.1268 2836.9268 0.991
25:01 -3.7498 0.7588 0.194
26:01 -2731.9621 2698.6192 0.990
29:02 -3.4931 1.1530 0.324
31:01 -1.7277 3.1842 0.561
32:01 -3859.2763 3894.6947 0.993
68:01 -2731.9621 2698.6192 0.990
Logistic regression (dominant model) with 60 individuals:
glm(case ~ h + pc1, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.793e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -2.268e-04
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.370e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.562e+01
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.686e-01
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.451e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 -3.062e-03
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.501e+00
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.189e+00
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.289e-01
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.781e+01
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.673e+01
h.2.5% h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02 0.1587 3.4264 0.032* 0.011111 -0.5249 0.5471 0.968
-----
01:01 -1.0334 1.0330 1.000 -0.005807 -0.5126 0.5010 0.982
02:01 -1.1652 0.8913 0.794 -0.002618 -0.5102 0.5049 0.992
02:06 -2868.1460 2836.9076 0.991 -0.028534 -0.5374 0.4803 0.912
03:01 -1.1813 1.7185 0.717 0.011958 -0.5044 0.5283 0.964
11:01 -2.3225 1.4322 0.642 0.008025 -0.5026 0.5186 0.975
23:01 -2.8348 2.8287 0.998 -0.005857 -0.5148 0.5031 0.982
24:03 -2868.1286 2836.9250 0.991 -0.011249 -0.5182 0.4957 0.965
25:01 -3.7579 0.7568 0.193 -0.025685 -0.5490 0.4976 0.923
26:01 -2731.8901 2698.5450 0.990 -0.014069 -0.5297 0.5015 0.957
29:02 -3.5309 1.1526 0.320 0.033234 -0.4796 0.5461 0.899
31:01 -1.7274 3.1851 0.561 -0.008320 -0.5153 0.4987 0.974
32:01 -3845.6317 3881.2510 0.993 -0.125426 -0.6671 0.4162 0.650
68:01 -2721.2124 2687.7497 0.990 -0.086589 -0.6512 0.4781 0.764
Logistic regression (dominant model) with 60 individuals:
glm(case ~ h + pc1, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est_OR
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 6.005e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 9.998e-01
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 8.720e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 1.647e-07
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 1.308e+00
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 6.407e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 9.969e-01
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 1.676e-07
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 2.230e-01
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.744e-08
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 3.045e-01
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 2.073e+00
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 5.428e+07
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.416e-08
h.2.5%_OR h.97.5%_OR h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02 1.17200 30.766 0.032* 0.011111 -0.5249 0.5471 0.968
-----
01:01 0.35579 2.809 1.000 -0.005807 -0.5126 0.5010 0.982
02:01 0.31185 2.438 0.794 -0.002618 -0.5102 0.5049 0.992
02:06 0.00000 Inf 0.991 -0.028534 -0.5374 0.4803 0.912
03:01 0.30687 5.576 0.717 0.011958 -0.5044 0.5283 0.964
11:01 0.09803 4.188 0.642 0.008025 -0.5026 0.5186 0.975
23:01 0.05873 16.923 0.998 -0.005857 -0.5148 0.5031 0.982
24:03 0.00000 Inf 0.991 -0.011249 -0.5182 0.4957 0.965
25:01 0.02333 2.131 0.193 -0.025685 -0.5490 0.4976 0.923
26:01 0.00000 Inf 0.990 -0.014069 -0.5297 0.5015 0.957
29:02 0.02928 3.167 0.320 0.033234 -0.4796 0.5461 0.899
31:01 0.17774 24.171 0.561 -0.008320 -0.5153 0.4987 0.974
32:01 0.00000 Inf 0.993 -0.125426 -0.6671 0.4162 0.650
68:01 0.00000 Inf 0.990 -0.086589 -0.6512 0.4781 0.764
Linear regression (dominant model) with 60 individuals:
glm(y ~ h, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% h.97.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.02941 -0.4805 0.5393
02:01 25 35 -0.32331 -0.000618 0.190 0.32269 -0.1772 0.8226
02:06 59 1 -0.14024 0.170057 . 0.31030 -1.6397 2.2603
03:01 51 9 -0.05600 -0.583178 0.147 -0.52718 -1.2136 0.1592
11:01 55 5 -0.19188 0.489815 0.287 0.68170 -0.2051 1.5685
23:01 58 2 -0.15400 0.413687 0.281 0.56768 -0.8165 1.9518
24:02 49 11 -0.10486 -0.269664 0.537 -0.16481 -0.8091 0.4795
24:03 59 1 -0.11409 -1.373118 . -1.25903 -3.1835 0.6655
25:01 55 5 -0.12237 -0.274749 0.742 -0.15237 -1.0555 0.7507
26:01 57 3 -0.12473 -0.331558 0.690 -0.20683 -1.3519 0.9383
29:02 56 4 -0.13044 -0.199941 0.789 -0.06950 -1.0709 0.9319
31:01 57 3 -0.10097 -0.783003 0.607 -0.68203 -1.8149 0.4508
32:01 56 4 -0.07702 -0.947791 0.092 -0.87077 -1.8470 0.1054
68:01 57 3 -0.16915 0.512457 0.196 0.68161 -0.4512 1.8145
h.pval
01:01 0.910
02:01 0.211
02:06 0.756
03:01 0.138
11:01 0.137
23:01 0.425
24:02 0.618
24:03 0.205
25:01 0.742
26:01 0.725
29:02 0.892
31:01 0.243
32:01 0.086
68:01 0.243
Linear regression (dominant model) with 60 individuals:
glm(y ~ h + pc1, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773
02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891
02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722
03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884
11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520
23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598
24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034
24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708
25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388
26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460
29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496
31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254
32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862
68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749
h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386
02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414
02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371
03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539
11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474
23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343
24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393
24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395
25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396
26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390
29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372
31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371
32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210
68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294
Linear regression (dominant model) with 60 individuals:
glm(y ~ h + pc1, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773
02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891
02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722
03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884
11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520
23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598
24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034
24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708
25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388
26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460
29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496
31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254
32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862
68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749
h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386
02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414
02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371
03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539
11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474
23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343
24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393
24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395
25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396
26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390
29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372
31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371
32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210
68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294
Logistic regression (additive model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p h.est h.2.5%
24:02 109 11 46.8 81.8 3.6030 0.058 0.053 1.7918 0.1585
-----
01:01 95 25 50.5 48.0 0.0000 1.000 1.000 -0.1207 -1.0843
02:01 77 43 48.1 53.5 0.1450 0.703 0.704 0.2137 -0.5289
02:06 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268
03:01 111 9 49.5 55.6 0.0000 1.000 1.000 0.2624 -1.1624
11:01 115 5 50.4 40.0 0.0000 1.000 1.000 -0.4418 -2.3074
23:01 117 3 50.4 33.3 0.0000 1.000 1.000 -0.4323 -2.3435
24:03 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268
25:01 115 5 51.3 20.0 0.8348 0.361 0.364 -1.4955 -3.7498
26:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621
29:02 116 4 50.9 25.0 0.2586 0.611 0.619 -1.1701 -3.4931
31:01 117 3 49.6 66.7 0.0000 1.000 1.000 0.7282 -1.7277
32:01 116 4 48.3 100.0 2.3276 0.127 0.119 17.7092 -3859.2763
68:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621
h.97.5% h.pval
24:02 3.4251 0.032*
-----
01:01 0.8430 0.806
02:01 0.9563 0.573
02:06 2836.9268 0.991
03:01 1.6872 0.718
11:01 1.4237 0.643
23:01 1.4789 0.658
24:03 2836.9268 0.991
25:01 0.7588 0.194
26:01 2698.6192 0.990
29:02 1.1530 0.324
31:01 3.1842 0.561
32:01 3894.6947 0.993
68:01 2698.6192 0.990
Logistic regression (recessive model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p h.est
01:01 59 1 50.8 0 0.000 1.000 1.000 -15.600
02:01 52 8 46.2 75 1.298 0.255 0.254 1.253
02:06 60 0 50.0 . . . . .
03:01 60 0 50.0 . . . . .
11:01 60 0 50.0 . . . . .
23:01 59 1 50.8 0 0.000 1.000 1.000 -15.600
24:02 60 0 50.0 . . . . .
24:03 60 0 50.0 . . . . .
25:01 60 0 50.0 . . . . .
26:01 60 0 50.0 . . . . .
29:02 60 0 50.0 . . . . .
31:01 60 0 50.0 . . . . .
32:01 60 0 50.0 . . . . .
68:01 60 0 50.0 . . . . .
h.2.5% h.97.5% h.pval
01:01 -2868.1268 2836.927 0.991
02:01 -0.4379 2.943 0.146
02:06 . . .
03:01 . . .
11:01 . . .
23:01 -2868.1268 2836.927 0.991
24:02 . . .
24:03 . . .
25:01 . . .
26:01 . . .
29:02 . . .
31:01 . . .
32:01 . . .
68:01 . . .
Logistic regression (genotype model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042*
-----
01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000
02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271
02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000
11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000
23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000
24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353
26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612
31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000
32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112
68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
h1.est h1.2.5% h1.97.5% h1.pval h2.est h2.2.5% h2.97.5%
24:02 1.79176 0.1585 3.4251 0.032* . . .
-----
01:01 0.08701 -0.9600 1.1340 0.871 -15.566 -2868.0929 2836.961
02:01 -0.45474 -1.5524 0.6430 0.417 1.019 -0.7637 2.801
02:06 -15.59997 -2868.1268 2836.9268 0.991 . . .
03:01 0.26236 -1.1624 1.6872 0.718 . . .
11:01 -0.44183 -2.3074 1.4237 0.643 . . .
23:01 16.56607 -4686.4552 4719.5873 0.994 -16.566 -4719.5873 4686.455
24:03 -15.59997 -2868.1268 2836.9268 0.991 . . .
25:01 -1.49549 -3.7498 0.7588 0.194 . . .
26:01 -16.67143 -2731.9621 2698.6192 0.990 . . .
29:02 -1.17007 -3.4931 1.1530 0.324 . . .
31:01 0.72824 -1.7277 3.1842 0.561 . . .
32:01 17.70917 -3859.2763 3894.6947 0.993 . . .
68:01 -16.67143 -2731.9621 2698.6192 0.990 . . .
h2.pval
24:02 .
-----
01:01 0.991
02:01 0.263
02:06 .
03:01 .
11:01 .
23:01 0.994
24:03 .
25:01 .
26:01 .
29:02 .
31:01 .
32:01 .
68:01 .
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:18
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 211, loss: 196.4, oob acc: 54.55%, # of haplo: 13
2, SNP: 66, loss: 173.548, oob acc: 63.64%, # of haplo: 13
3, SNP: 177, loss: 136.352, oob acc: 68.18%, # of haplo: 13
4, SNP: 108, loss: 95.8359, oob acc: 72.73%, # of haplo: 13
5, SNP: 127, loss: 67.3216, oob acc: 77.27%, # of haplo: 13
6, SNP: 95, loss: 47.5888, oob acc: 77.27%, # of haplo: 13
7, SNP: 33, loss: 37.2631, oob acc: 77.27%, # of haplo: 16
8, SNP: 6, loss: 29.7419, oob acc: 77.27%, # of haplo: 18
9, SNP: 208, loss: 25.6913, oob acc: 77.27%, # of haplo: 19
10, SNP: 225, loss: 25.3087, oob acc: 77.27%, # of haplo: 21
11, SNP: 11, loss: 24.8356, oob acc: 77.27%, # of haplo: 23
12, SNP: 151, loss: 19.4134, oob acc: 77.27%, # of haplo: 23
13, SNP: 199, loss: 17.011, oob acc: 77.27%, # of haplo: 23
[1] 2021-10-15 00:23:18, oob acc: 77.27%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
1, SNP: 160, loss: 221.236, oob acc: 76.92%, # of haplo: 17
2, SNP: 145, loss: 173.538, oob acc: 80.77%, # of haplo: 23
3, SNP: 177, loss: 128.58, oob acc: 84.62%, # of haplo: 31
4, SNP: 111, loss: 79.6877, oob acc: 84.62%, # of haplo: 31
5, SNP: 207, loss: 52.5557, oob acc: 88.46%, # of haplo: 32
6, SNP: 245, loss: 41.8731, oob acc: 88.46%, # of haplo: 34
7, SNP: 230, loss: 31.7937, oob acc: 88.46%, # of haplo: 38
8, SNP: 151, loss: 20.4566, oob acc: 88.46%, # of haplo: 36
9, SNP: 14, loss: 19.5805, oob acc: 88.46%, # of haplo: 42
10, SNP: 132, loss: 19.5101, oob acc: 88.46%, # of haplo: 42
11, SNP: 221, loss: 19.485, oob acc: 88.46%, # of haplo: 44
12, SNP: 251, loss: 18.5695, oob acc: 88.46%, # of haplo: 48
[2] 2021-10-15 00:23:18, oob acc: 88.46%, # of SNPs: 12, # of haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 191, loss: 193.067, oob acc: 57.14%, # of haplo: 11
2, SNP: 264, loss: 150.427, oob acc: 64.29%, # of haplo: 12
3, SNP: 132, loss: 93.4067, oob acc: 67.86%, # of haplo: 12
4, SNP: 128, loss: 39.8353, oob acc: 71.43%, # of haplo: 12
5, SNP: 160, loss: 28.2998, oob acc: 75.00%, # of haplo: 12
6, SNP: 144, loss: 13.635, oob acc: 75.00%, # of haplo: 12
7, SNP: 111, loss: 6.04609, oob acc: 75.00%, # of haplo: 12
8, SNP: 40, loss: 6.04583, oob acc: 82.14%, # of haplo: 14
9, SNP: 141, loss: 6.04583, oob acc: 85.71%, # of haplo: 14
10, SNP: 73, loss: 2.9038, oob acc: 85.71%, # of haplo: 14
11, SNP: 199, loss: 2.20025, oob acc: 85.71%, # of haplo: 14
[3] 2021-10-15 00:23:18, oob acc: 85.71%, # of SNPs: 11, # of haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
1, SNP: 147, loss: 158.631, oob acc: 50.00%, # of haplo: 12
2, SNP: 152, loss: 140.375, oob acc: 55.00%, # of haplo: 13
3, SNP: 78, loss: 115.887, oob acc: 60.00%, # of haplo: 16
4, SNP: 115, loss: 77.8082, oob acc: 60.00%, # of haplo: 18
5, SNP: 148, loss: 62.6831, oob acc: 65.00%, # of haplo: 18
6, SNP: 13, loss: 46.5657, oob acc: 75.00%, # of haplo: 20
7, SNP: 109, loss: 31.0312, oob acc: 75.00%, # of haplo: 20
8, SNP: 176, loss: 22.5073, oob acc: 75.00%, # of haplo: 21
9, SNP: 145, loss: 20.9122, oob acc: 75.00%, # of haplo: 21
10, SNP: 128, loss: 20.6728, oob acc: 75.00%, # of haplo: 21
11, SNP: 73, loss: 14.6217, oob acc: 75.00%, # of haplo: 22
12, SNP: 151, loss: 10.2879, oob acc: 75.00%, # of haplo: 23
13, SNP: 199, loss: 8.74645, oob acc: 75.00%, # of haplo: 23
[4] 2021-10-15 00:23:18, oob acc: 75.00%, # of SNPs: 13, # of haplo: 23
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Accuracy with training data: 97.06%
Out-of-bag accuracy: 81.61%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 38
avg. # of SNPs in an individual classifier: 12.25
(sd: 0.96, min: 11, max: 13, median: 12.50)
avg. # of haplotypes in an individual classifier: 27.00
(sd: 14.63, min: 14, max: 48, median: 23.00)
avg. out-of-bag accuracy: 81.61%
(sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:18) 0%
Predicting (2021-10-15 00:23:18) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 4 (15.4%) 4 (15.4%) 17 (65.4%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.002097 0.007711 0.032190 0.028032 0.471142
Dosages:
$dosage - num [1:14, 1:26] 2.50e-01 2.16e-04 2.50e-06 7.31e-01 1.11e-14 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:14] "01:01" "02:01" "02:06" "03:01" ...
..$ : chr [1:26] "NA11881" "NA11992" "NA11994" "NA12249" ...
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:18) 0%
Predicting (2021-10-15 00:23:18) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 4 (15.4%) 4 (15.4%) 17 (65.4%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.002097 0.007711 0.032190 0.028032 0.471142
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed' (the individual-major mode)
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam'
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim'
Import 3932 SNPs within the xMHC region on chromosome 6
SNP genotypes:
90 samples X 3932 SNPs
SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
A/G C/T G/T A/C C/G A/T
1567 1510 348 332 111 64
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed' (the individual-major mode)
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam'
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim'
Import 5316 SNPs from chromosome 6
SNP genotypes:
90 samples X 5316 SNPs
SNPs range from 25651262bp to 33426848bp on hg19
Missing rate per SNP:
min: 0, max: 0.1, mean: 0.0882054, median: 0.1, sd: 0.030674
Missing rate per sample:
min: 0, max: 0.863619, mean: 0.0882054, median: 0.00131678, sd: 0.259735
Minor allele frequency:
min: 0, max: 0.5, mean: 0.201867, median: 0.179012, sd: 0.155475
Allelic information:
A/G C/T G/T A/C C/G A/T
2102 2046 480 471 134 83
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 1 monomorphic SNP
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77
# of samples: 60
# of unique HLA alleles: 12
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:18
=== building individual classifier 1, out-of-bag (25/41.7%) ===
[1] 2021-10-15 00:23:18, oob acc: 98.00%, # of SNPs: 13, # of haplo: 20
=== building individual classifier 2, out-of-bag (22/36.7%) ===
[2] 2021-10-15 00:23:18, oob acc: 90.91%, # of SNPs: 15, # of haplo: 21
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.196909e-03 1.419916e-02 2.996040e-02
Max. Mean SD
4.735980e-01 4.413724e-02 1.070518e-01
Accuracy with training data: 95.00%
Out-of-bag accuracy: 94.45%
Gene: HLA-DQB1
Training dataset: 60 samples X 77 SNPs
# of HLA alleles: 12
# of individual classifiers: 2
total # of SNPs used: 20
avg. # of SNPs in an individual classifier: 14.00
(sd: 1.41, min: 13, max: 15, median: 14.00)
avg. # of haplotypes in an individual classifier: 20.50
(sd: 0.71, min: 20, max: 21, median: 20.50)
avg. out-of-bag accuracy: 94.45%
(sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.196909e-03 1.419916e-02 2.996040e-02
Max. Mean SD
4.735980e-01 4.413724e-02 1.070518e-01
Genome assembly: hg19
The HIBAG model:
There are 77 SNP predictors in total.
There are 2 individual classifiers.
Summarize the missing fractions of SNP predictors per classifier:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 60
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 0
# of unique HLA genotypes: 0
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 200
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Build a HIBAG model with 1 individual classifier:
MAF threshold: NaN
excluding 9 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:18
=== building individual classifier 1, out-of-bag (23/38.3%) ===
[1] 2021-10-15 00:23:18, oob acc: 86.96%, # of SNPs: 12, # of haplo: 32
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
4.166789e-14 4.261245e-14 5.111347e-14 2.589270e-03 1.608934e-02 5.868848e-02
Max. Mean SD
6.267394e-01 6.664806e-02 1.405453e-01
Accuracy with training data: 94.17%
Out-of-bag accuracy: 86.96%
Build a HIBAG model with 1 individual classifier:
MAF threshold: NaN
excluding 9 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:18
=== building individual classifier 1, out-of-bag (24/40.0%) ===
[1] 2021-10-15 00:23:19, oob acc: 87.50%, # of SNPs: 15, # of haplo: 40
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.894066e-24 9.219565e-20 9.218854e-19 2.189685e-03 7.704546e-03 2.406258e-02
Max. Mean SD
2.755151e-01 2.949891e-02 6.162169e-02
Accuracy with training data: 95.00%
Out-of-bag accuracy: 87.50%
Gene: HLA-A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 24
avg. # of SNPs in an individual classifier: 13.50
(sd: 2.12, min: 12, max: 15, median: 13.50)
avg. # of haplotypes in an individual classifier: 36.00
(sd: 5.66, min: 32, max: 40, median: 36.00)
avg. out-of-bag accuracy: 87.23%
(sd: 0.38%, min: 86.96%, max: 87.50%, median: 87.23%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
9.233104e-13 5.204084e-10 5.195775e-09 2.309655e-03 1.448839e-02 3.746431e-02
Max. Mean SD
4.511273e-01 4.807348e-02 1.006148e-01
Genome assembly: hg19
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:19
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 211, loss: 196.4, oob acc: 54.55%, # of haplo: 13
2, SNP: 66, loss: 173.548, oob acc: 63.64%, # of haplo: 13
3, SNP: 177, loss: 136.352, oob acc: 68.18%, # of haplo: 13
4, SNP: 108, loss: 95.8359, oob acc: 72.73%, # of haplo: 13
5, SNP: 127, loss: 67.3216, oob acc: 77.27%, # of haplo: 13
6, SNP: 95, loss: 47.5888, oob acc: 77.27%, # of haplo: 13
7, SNP: 33, loss: 37.2631, oob acc: 77.27%, # of haplo: 16
8, SNP: 6, loss: 29.7419, oob acc: 77.27%, # of haplo: 18
9, SNP: 208, loss: 25.6913, oob acc: 77.27%, # of haplo: 19
10, SNP: 225, loss: 25.3087, oob acc: 77.27%, # of haplo: 21
11, SNP: 11, loss: 24.8356, oob acc: 77.27%, # of haplo: 23
12, SNP: 151, loss: 19.4134, oob acc: 77.27%, # of haplo: 23
13, SNP: 199, loss: 17.011, oob acc: 77.27%, # of haplo: 23
[1] 2021-10-15 00:23:19, oob acc: 77.27%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
1, SNP: 160, loss: 221.236, oob acc: 76.92%, # of haplo: 17
2, SNP: 145, loss: 173.538, oob acc: 80.77%, # of haplo: 23
3, SNP: 177, loss: 128.58, oob acc: 84.62%, # of haplo: 31
4, SNP: 111, loss: 79.6877, oob acc: 84.62%, # of haplo: 31
5, SNP: 207, loss: 52.5557, oob acc: 88.46%, # of haplo: 32
6, SNP: 245, loss: 41.8731, oob acc: 88.46%, # of haplo: 34
7, SNP: 230, loss: 31.7937, oob acc: 88.46%, # of haplo: 38
8, SNP: 151, loss: 20.4566, oob acc: 88.46%, # of haplo: 36
9, SNP: 14, loss: 19.5805, oob acc: 88.46%, # of haplo: 42
10, SNP: 132, loss: 19.5101, oob acc: 88.46%, # of haplo: 42
11, SNP: 221, loss: 19.485, oob acc: 88.46%, # of haplo: 44
12, SNP: 251, loss: 18.5695, oob acc: 88.46%, # of haplo: 48
[2] 2021-10-15 00:23:19, oob acc: 88.46%, # of SNPs: 12, # of haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 191, loss: 193.067, oob acc: 57.14%, # of haplo: 11
2, SNP: 264, loss: 150.427, oob acc: 64.29%, # of haplo: 12
3, SNP: 132, loss: 93.4067, oob acc: 67.86%, # of haplo: 12
4, SNP: 128, loss: 39.8353, oob acc: 71.43%, # of haplo: 12
5, SNP: 160, loss: 28.2998, oob acc: 75.00%, # of haplo: 12
6, SNP: 144, loss: 13.635, oob acc: 75.00%, # of haplo: 12
7, SNP: 111, loss: 6.04609, oob acc: 75.00%, # of haplo: 12
8, SNP: 40, loss: 6.04583, oob acc: 82.14%, # of haplo: 14
9, SNP: 141, loss: 6.04583, oob acc: 85.71%, # of haplo: 14
10, SNP: 73, loss: 2.9038, oob acc: 85.71%, # of haplo: 14
11, SNP: 199, loss: 2.20025, oob acc: 85.71%, # of haplo: 14
[3] 2021-10-15 00:23:19, oob acc: 85.71%, # of SNPs: 11, # of haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
1, SNP: 147, loss: 158.631, oob acc: 50.00%, # of haplo: 12
2, SNP: 152, loss: 140.375, oob acc: 55.00%, # of haplo: 13
3, SNP: 78, loss: 115.887, oob acc: 60.00%, # of haplo: 16
4, SNP: 115, loss: 77.8082, oob acc: 60.00%, # of haplo: 18
5, SNP: 148, loss: 62.6831, oob acc: 65.00%, # of haplo: 18
6, SNP: 13, loss: 46.5657, oob acc: 75.00%, # of haplo: 20
7, SNP: 109, loss: 31.0312, oob acc: 75.00%, # of haplo: 20
8, SNP: 176, loss: 22.5073, oob acc: 75.00%, # of haplo: 21
9, SNP: 145, loss: 20.9122, oob acc: 75.00%, # of haplo: 21
10, SNP: 128, loss: 20.6728, oob acc: 75.00%, # of haplo: 21
11, SNP: 73, loss: 14.6217, oob acc: 75.00%, # of haplo: 22
12, SNP: 151, loss: 10.2879, oob acc: 75.00%, # of haplo: 23
13, SNP: 199, loss: 8.74645, oob acc: 75.00%, # of haplo: 23
[4] 2021-10-15 00:23:19, oob acc: 75.00%, # of SNPs: 13, # of haplo: 23
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Accuracy with training data: 97.06%
Out-of-bag accuracy: 81.61%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 38
avg. # of SNPs in an individual classifier: 12.25
(sd: 0.96, min: 11, max: 13, median: 12.50)
avg. # of haplotypes in an individual classifier: 27.00
(sd: 14.63, min: 14, max: 48, median: 23.00)
avg. out-of-bag accuracy: 81.61%
(sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:19) 0%
Predicting (2021-10-15 00:23:19) 100%
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 09:01
Allelic ambiguity: 09:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num * - A D E F G H I K L M N Q R S T V W Y
1 120 120 . . . . . . . . . . . . . . . . . . .
9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17
44 120 . 25 . . . . . . . . . . . . 95 . . . . .
56 120 . 117 . . . . . . . . . . . . 3 . . . . .
62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . .
63 120 . 105 . . . . . . . . . . 11 4 . . . . . .
65 120 . 105 . . . . 15 . . . . . . . . . . . . .
66 120 . 61 . . . . . . . 59 . . . . . . . . . .
67 120 . 25 . . . . . . . . . . . . . . . 95 . .
70 120 . 99 . . . . . . . . . . . 21 . . . . . .
73 120 . 117 . . . . . . 3 . . . . . . . . . . .
74 120 . 76 . . . . . 44 . . . . . . . . . . . .
76 120 . 32 . . 24 . . . . . . . . . . . . 64 . .
77 120 . 47 . 64 . . . . . . . . . . . 9 . . . .
79 120 . 96 . . . . . . . . . . . . 24 . . . . .
80 120 . 96 . . . . . . 24 . . . . . . . . . . .
81 120 . 96 24 . . . . . . . . . . . . . . . . .
82 120 . 96 . . . . . . . . 24 . . . . . . . . .
83 120 . 96 . . . . . . . . . . . . 24 . . . . .
90 120 . 38 82 . . . . . . . . . . . . . . . . .
95 120 . 61 . . . . . . . . 15 . . . . . . 44 . .
97 120 . 39 . . . . . . . . . 29 . . 52 . . . . .
99 120 . 105 . . . 15 . . . . . . . . . . . . . .
105 120 . 42 . . . . . . . . . . . . . 78 . . . .
107 120 . 76 . . . . . . . . . . . . . . . . 44 .
109 120 . 116 . . . . . . . . 4 . . . . . . . . .
114 120 . 46 . . . . . 59 . . . . . 15 . . . . . .
116 120 . 61 . . . . . . . . . . . . . . . . . 59
127 120 . 58 . . . . . . . 62 . . . . . . . . . .
142 120 . 73 . . . . . . . . . . . . . . 47 . . .
144 120 . 98 . . . . . . . . . . . 22 . . . . . .
145 120 . 73 . . . . . 47 . . . . . . . . . . . .
149 120 . 112 . . . . . . . . . . . . . . 8 . . .
150 120 . 25 95 . . . . . . . . . . . . . . . . .
151 120 . 106 . . . . . . . . . . . . 14 . . . . .
152 120 . 30 . . 17 . . . . . . . . . . . . 73 . .
156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 .
158 120 . 25 95 . . . . . . . . . . . . . . . . .
161 120 . 111 . 9 . . . . . . . . . . . . . . . .
163 120 . 38 . . . . . . . . . . . . . . 82 . . .
166 120 . 39 . . 81 . . . . . . . . . . . . . . .
167 120 . 39 . . . . . . . . . . . . . . . . 81 .
183 120 120 . . . . . . . . . . . . . . . . . . .
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num * - A D E F G H I K L M N Q R S T V W Y
-23 120 120 . . . . . . . . . . . . . . . . . . .
-22 120 120 . . . . . . . . . . . . . . . . . . .
-21 120 120 . . . . . . . . . . . . . . . . . . .
-20 120 120 . . . . . . . . . . . . . . . . . . .
-19 120 120 . . . . . . . . . . . . . . . . . . .
-18 120 120 . . . . . . . . . . . . . . . . . . .
-17 120 120 . . . . . . . . . . . . . . . . . . .
-16 120 120 . . . . . . . . . . . . . . . . . . .
-15 120 120 . . . . . . . . . . . . . . . . . . .
-14 120 120 . . . . . . . . . . . . . . . . . . .
-13 120 120 . . . . . . . . . . . . . . . . . . .
-12 120 120 . . . . . . . . . . . . . . . . . . .
-11 120 120 . . . . . . . . . . . . . . . . . . .
-10 120 120 . . . . . . . . . . . . . . . . . . .
-9 120 120 . . . . . . . . . . . . . . . . . . .
-8 120 120 . . . . . . . . . . . . . . . . . . .
-7 120 120 . . . . . . . . . . . . . . . . . . .
-6 120 120 . . . . . . . . . . . . . . . . . . .
-5 120 120 . . . . . . . . . . . . . . . . . . .
-4 120 120 . . . . . . . . . . . . . . . . . . .
-3 120 120 . . . . . . . . . . . . . . . . . . .
-2 120 120 . . . . . . . . . . . . . . . . . . .
-1 120 120 . . . . . . . . . . . . . . . . . . .
. 120 120 . . . . . . . . . . . . . . . . . . .
1 120 120 . . . . . . . . . . . . . . . . . . .
9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17
44 120 . 25 . . . . . . . . . . . . 95 . . . . .
56 120 . 117 . . . . . . . . . . . . 3 . . . . .
62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . .
63 120 . 105 . . . . . . . . . . 11 4 . . . . . .
65 120 . 105 . . . . 15 . . . . . . . . . . . . .
66 120 . 61 . . . . . . . 59 . . . . . . . . . .
67 120 . 25 . . . . . . . . . . . . . . . 95 . .
70 120 . 99 . . . . . . . . . . . 21 . . . . . .
73 120 . 117 . . . . . . 3 . . . . . . . . . . .
74 120 . 76 . . . . . 44 . . . . . . . . . . . .
76 120 . 32 . . 24 . . . . . . . . . . . . 64 . .
77 120 . 47 . 64 . . . . . . . . . . . 9 . . . .
79 120 . 96 . . . . . . . . . . . . 24 . . . . .
80 120 . 96 . . . . . . 24 . . . . . . . . . . .
81 120 . 96 24 . . . . . . . . . . . . . . . . .
82 120 . 96 . . . . . . . . 24 . . . . . . . . .
83 120 . 96 . . . . . . . . . . . . 24 . . . . .
90 120 . 38 82 . . . . . . . . . . . . . . . . .
95 120 . 61 . . . . . . . . 15 . . . . . . 44 . .
97 120 . 39 . . . . . . . . . 29 . . 52 . . . . .
99 120 . 105 . . . 15 . . . . . . . . . . . . . .
105 120 . 42 . . . . . . . . . . . . . 78 . . . .
107 120 . 76 . . . . . . . . . . . . . . . . 44 .
109 120 . 116 . . . . . . . . 4 . . . . . . . . .
114 120 . 46 . . . . . 59 . . . . . 15 . . . . . .
116 120 . 61 . . . . . . . . . . . . . . . . . 59
127 120 . 58 . . . . . . . 62 . . . . . . . . . .
142 120 . 73 . . . . . . . . . . . . . . 47 . . .
144 120 . 98 . . . . . . . . . . . 22 . . . . . .
145 120 . 73 . . . . . 47 . . . . . . . . . . . .
149 120 . 112 . . . . . . . . . . . . . . 8 . . .
150 120 . 25 95 . . . . . . . . . . . . . . . . .
151 120 . 106 . . . . . . . . . . . . 14 . . . . .
152 120 . 30 . . 17 . . . . . . . . . . . . 73 . .
156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 .
158 120 . 25 95 . . . . . . . . . . . . . . . . .
161 120 . 111 . 9 . . . . . . . . . . . . . . . .
163 120 . 38 . . . . . . . . . . . . . . 82 . . .
166 120 . 39 . . 81 . . . . . . . . . . . . . . .
167 120 . 39 . . . . . . . . . . . . . . . . 81 .
183 120 120 . . . . . . . . . . . . . . . . . . .
184 120 120 . . . . . . . . . . . . . . . . . . .
185 120 120 . . . . . . . . . . . . . . . . . . .
186 120 120 . . . . . . . . . . . . . . . . . . .
187 120 120 . . . . . . . . . . . . . . . . . . .
188 120 120 . . . . . . . . . . . . . . . . . . .
189 120 120 . . . . . . . . . . . . . . . . . . .
190 120 120 . . . . . . . . . . . . . . . . . . .
191 120 120 . . . . . . . . . . . . . . . . . . .
192 120 120 . . . . . . . . . . . . . . . . . . .
193 120 120 . . . . . . . . . . . . . . . . . . .
194 120 120 . . . . . . . . . . . . . . . . . . .
195 120 120 . . . . . . . . . . . . . . . . . . .
196 120 120 . . . . . . . . . . . . . . . . . . .
197 120 120 . . . . . . . . . . . . . . . . . . .
198 120 120 . . . . . . . . . . . . . . . . . . .
199 120 120 . . . . . . . . . . . . . . . . . . .
200 120 120 . . . . . . . . . . . . . . . . . . .
201 120 120 . . . . . . . . . . . . . . . . . . .
202 120 120 . . . . . . . . . . . . . . . . . . .
203 120 120 . . . . . . . . . . . . . . . . . . .
204 120 120 . . . . . . . . . . . . . . . . . . .
205 120 120 . . . . . . . . . . . . . . . . . . .
206 120 120 . . . . . . . . . . . . . . . . . . .
207 120 120 . . . . . . . . . . . . . . . . . . .
208 120 120 . . . . . . . . . . . . . . . . . . .
209 120 120 . . . . . . . . . . . . . . . . . . .
210 120 120 . . . . . . . . . . . . . . . . . . .
211 120 120 . . . . . . . . . . . . . . . . . . .
212 120 120 . . . . . . . . . . . . . . . . . . .
213 120 120 . . . . . . . . . . . . . . . . . . .
214 120 120 . . . . . . . . . . . . . . . . . . .
215 120 120 . . . . . . . . . . . . . . . . . . .
216 120 120 . . . . . . . . . . . . . . . . . . .
217 120 120 . . . . . . . . . . . . . . . . . . .
218 120 120 . . . . . . . . . . . . . . . . . . .
219 120 120 . . . . . . . . . . . . . . . . . . .
220 120 120 . . . . . . . . . . . . . . . . . . .
221 120 120 . . . . . . . . . . . . . . . . . . .
222 120 120 . . . . . . . . . . . . . . . . . . .
223 120 120 . . . . . . . . . . . . . . . . . . .
224 120 120 . . . . . . . . . . . . . . . . . . .
225 120 120 . . . . . . . . . . . . . . . . . . .
226 120 120 . . . . . . . . . . . . . . . . . . .
227 120 120 . . . . . . . . . . . . . . . . . . .
228 120 120 . . . . . . . . . . . . . . . . . . .
229 120 120 . . . . . . . . . . . . . . . . . . .
230 120 120 . . . . . . . . . . . . . . . . . . .
231 120 120 . . . . . . . . . . . . . . . . . . .
232 120 120 . . . . . . . . . . . . . . . . . . .
233 120 120 . . . . . . . . . . . . . . . . . . .
234 120 120 . . . . . . . . . . . . . . . . . . .
235 120 120 . . . . . . . . . . . . . . . . . . .
236 120 120 . . . . . . . . . . . . . . . . . . .
237 120 120 . . . . . . . . . . . . . . . . . . .
238 120 120 . . . . . . . . . . . . . . . . . . .
239 120 120 . . . . . . . . . . . . . . . . . . .
240 120 120 . . . . . . . . . . . . . . . . . . .
241 120 120 . . . . . . . . . . . . . . . . . . .
242 120 120 . . . . . . . . . . . . . . . . . . .
243 120 120 . . . . . . . . . . . . . . . . . . .
244 120 120 . . . . . . . . . . . . . . . . . . .
245 120 120 . . . . . . . . . . . . . . . . . . .
246 120 120 . . . . . . . . . . . . . . . . . . .
247 120 120 . . . . . . . . . . . . . . . . . . .
248 120 120 . . . . . . . . . . . . . . . . . . .
249 120 120 . . . . . . . . . . . . . . . . . . .
250 120 120 . . . . . . . . . . . . . . . . . . .
251 120 120 . . . . . . . . . . . . . . . . . . .
252 120 120 . . . . . . . . . . . . . . . . . . .
253 120 120 . . . . . . . . . . . . . . . . . . .
254 120 120 . . . . . . . . . . . . . . . . . . .
255 120 120 . . . . . . . . . . . . . . . . . . .
256 120 120 . . . . . . . . . . . . . . . . . . .
257 120 120 . . . . . . . . . . . . . . . . . . .
258 120 120 . . . . . . . . . . . . . . . . . . .
259 120 120 . . . . . . . . . . . . . . . . . . .
260 120 120 . . . . . . . . . . . . . . . . . . .
261 120 120 . . . . . . . . . . . . . . . . . . .
262 120 120 . . . . . . . . . . . . . . . . . . .
263 120 120 . . . . . . . . . . . . . . . . . . .
264 120 120 . . . . . . . . . . . . . . . . . . .
265 120 120 . . . . . . . . . . . . . . . . . . .
266 120 120 . . . . . . . . . . . . . . . . . . .
267 120 120 . . . . . . . . . . . . . . . . . . .
268 120 120 . . . . . . . . . . . . . . . . . . .
269 120 120 . . . . . . . . . . . . . . . . . . .
270 120 120 . . . . . . . . . . . . . . . . . . .
271 120 120 . . . . . . . . . . . . . . . . . . .
272 120 120 . . . . . . . . . . . . . . . . . . .
273 120 120 . . . . . . . . . . . . . . . . . . .
274 120 120 . . . . . . . . . . . . . . . . . . .
275 120 120 . . . . . . . . . . . . . . . . . . .
276 120 120 . . . . . . . . . . . . . . . . . . .
277 120 120 . . . . . . . . . . . . . . . . . . .
278 120 120 . . . . . . . . . . . . . . . . . . .
279 120 120 . . . . . . . . . . . . . . . . . . .
280 120 120 . . . . . . . . . . . . . . . . . . .
281 120 120 . . . . . . . . . . . . . . . . . . .
282 120 120 . . . . . . . . . . . . . . . . . . .
283 120 120 . . . . . . . . . . . . . . . . . . .
284 120 120 . . . . . . . . . . . . . . . . . . .
285 120 120 . . . . . . . . . . . . . . . . . . .
286 120 120 . . . . . . . . . . . . . . . . . . .
287 120 120 . . . . . . . . . . . . . . . . . . .
288 120 120 . . . . . . . . . . . . . . . . . . .
289 120 120 . . . . . . . . . . . . . . . . . . .
290 120 120 . . . . . . . . . . . . . . . . . . .
291 120 120 . . . . . . . . . . . . . . . . . . .
292 120 120 . . . . . . . . . . . . . . . . . . .
293 120 120 . . . . . . . . . . . . . . . . . . .
294 120 120 . . . . . . . . . . . . . . . . . . .
295 120 120 . . . . . . . . . . . . . . . . . . .
296 120 120 . . . . . . . . . . . . . . . . . . .
297 120 120 . . . . . . . . . . . . . . . . . . .
298 120 120 . . . . . . . . . . . . . . . . . . .
299 120 120 . . . . . . . . . . . . . . . . . . .
300 120 120 . . . . . . . . . . . . . . . . . . .
301 120 120 . . . . . . . . . . . . . . . . . . .
302 120 120 . . . . . . . . . . . . . . . . . . .
303 120 120 . . . . . . . . . . . . . . . . . . .
304 120 120 . . . . . . . . . . . . . . . . . . .
305 120 120 . . . . . . . . . . . . . . . . . . .
306 120 120 . . . . . . . . . . . . . . . . . . .
307 120 120 . . . . . . . . . . . . . . . . . . .
308 120 120 . . . . . . . . . . . . . . . . . . .
309 120 120 . . . . . . . . . . . . . . . . . . .
310 120 120 . . . . . . . . . . . . . . . . . . .
311 120 120 . . . . . . . . . . . . . . . . . . .
312 120 120 . . . . . . . . . . . . . . . . . . .
313 120 120 . . . . . . . . . . . . . . . . . . .
314 120 120 . . . . . . . . . . . . . . . . . . .
315 120 120 . . . . . . . . . . . . . . . . . . .
316 120 120 . . . . . . . . . . . . . . . . . . .
317 120 120 . . . . . . . . . . . . . . . . . . .
318 120 120 . . . . . . . . . . . . . . . . . . .
319 120 120 . . . . . . . . . . . . . . . . . . .
320 120 120 . . . . . . . . . . . . . . . . . . .
321 120 120 . . . . . . . . . . . . . . . . . . .
322 120 120 . . . . . . . . . . . . . . . . . . .
323 120 120 . . . . . . . . . . . . . . . . . . .
324 120 120 . . . . . . . . . . . . . . . . . . .
325 120 120 . . . . . . . . . . . . . . . . . . .
326 120 120 . . . . . . . . . . . . . . . . . . .
327 120 120 . . . . . . . . . . . . . . . . . . .
328 120 120 . . . . . . . . . . . . . . . . . . .
329 120 120 . . . . . . . . . . . . . . . . . . .
330 120 120 . . . . . . . . . . . . . . . . . . .
331 120 120 . . . . . . . . . . . . . . . . . . .
332 120 120 . . . . . . . . . . . . . . . . . . .
333 120 120 . . . . . . . . . . . . . . . . . . .
334 120 120 . . . . . . . . . . . . . . . . . . .
335 120 120 . . . . . . . . . . . . . . . . . . .
336 120 120 . . . . . . . . . . . . . . . . . . .
337 120 120 . . . . . . . . . . . . . . . . . . .
338 120 120 . . . . . . . . . . . . . . . . . . .
339 120 120 . . . . . . . . . . . . . . . . . . .
340 120 120 . . . . . . . . . . . . . . . . . . .
341 120 120 . . . . . . . . . . . . . . . . . . .
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num * - A D E F G I K L M N P Q R S T Y
5 120 112 . . . . . . . . . . 8 . . . . . .
6 120 20 92 8 . . . . . . . . . . . . . . .
7 112 20 92 . . . . . . . . . . . . . . . .
8 112 20 92 . . . . . . . . . . . . . . . .
9 112 3 76 . . . 33 . . . . . . . . . . . .
10 112 3 109 . . . . . . . . . . . . . . . .
11 112 3 109 . . . . . . . . . . . . . . . .
12 112 3 109 . . . . . . . . . . . . . . . .
13 112 3 93 16 . . . . . . . . . . . . . . .
14 112 3 14 . . . . . . . . 95 . . . . . . .
15 112 3 109 . . . . . . . . . . . . . . . .
16 112 3 109 . . . . . . . . . . . . . . . .
17 112 3 109 . . . . . . . . . . . . . . . .
18 112 3 109 . . . . . . . . . . . . . . . .
19 112 3 109 . . . . . . . . . . . . . . . .
20 112 3 109 . . . . . . . . . . . . . . . .
26 112 . 20 . . . . . . . 76 . . . . . . . 16
28 112 . 100 . . . . . . . . . . . . . 12 . .
30 112 . 24 . . . . . . . . . . . . . 12 . 76
37 112 . 100 . . . . . 12 . . . . . . . . . .
38 112 . 29 83 . . . . . . . . . . . . . . .
45 112 . 96 . . 16 . . . . . . . . . . . . .
46 112 . 100 . . 12 . . . . . . . . . . . . .
47 112 . 100 . . . 12 . . . . . . . . . . . .
52 112 . 100 . . . . . . . 12 . . . . . . . .
53 112 . 54 . . . . . . . 58 . . . . . . . .
55 112 . 57 . . . . . . . 12 . . 43 . . . . .
56 112 . 109 . . . . . . . 3 . . . . . . . .
57 112 . 14 33 64 . . . . . . . . . . . 1 . .
66 112 . 97 . 15 . . . . . . . . . . . . . .
67 112 . 97 . . . . . 15 . . . . . . . . . .
70 112 3 50 . . 3 . . . . . . . . . 56 . . .
71 112 3 14 . 3 . . . . 12 . . . . . . . 80 .
72 112 3 109 . . . . . . . . . . . . . . . .
73 112 3 109 . . . . . . . . . . . . . . . .
74 112 3 17 12 . 80 . . . . . . . . . . . . .
75 112 3 29 . . . . . . . 80 . . . . . . . .
76 112 3 109 . . . . . . . . . . . . . . . .
77 112 3 26 . . . . . . . . . . . . . . 83 .
78 112 3 109 . . . . . . . . . . . . . . . .
79 112 3 109 . . . . . . . . . . . . . . . .
80 112 3 109 . . . . . . . . . . . . . . . .
81 112 3 109 . . . . . . . . . . . . . . . .
82 112 3 109 . . . . . . . . . . . . . . . .
83 112 3 109 . . . . . . . . . . . . . . . .
84 112 3 51 . . . . . . . . . . . 58 . . . .
85 112 3 51 . . . . . . . 58 . . . . . . . .
86 112 3 50 . . 58 . 1 . . . . . . . . . . .
87 112 3 15 . . . 36 . . . 58 . . . . . . . .
88 112 3 109 . . . . . . . . . . . . . . . .
89 112 3 51 . . . . . . . . . . . . . . 58 .
90 112 3 51 . . . . . . . . . . . . . . 58 .
91 112 3 109 . . . . . . . . . . . . . . . .
92 112 3 109 . . . . . . . . . . . . . . . .
93 112 3 109 . . . . . . . . . . . . . . . .
94 112 17 95 . . . . . . . . . . . . . . . .
95 112 112 . . . . . . . . . . . . . . . . .
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num * - A D E F G I K L M N P Q R S T Y
-31 120 112 . . . . . . . . . . 8 . . . . . .
-30 120 112 . 8 . . . . . . . . . . . . . . .
-29 112 112 . . . . . . . . . . . . . . . . .
-28 112 112 . . . . . . . . . . . . . . . . .
-27 112 112 . . . . . . . . . . . . . . . . .
-26 112 112 . . . . . . . . . . . . . . . . .
-25 112 112 . . . . . . . . . . . . . . . . .
-24 112 112 . . . . . . . . . . . . . . . . .
-23 112 112 . . . . . . . . . . . . . . . . .
-22 112 112 . . . . . . . . . . . . . . . . .
-21 112 112 . . . . . . . . . . . . . . . . .
-20 112 112 . . . . . . . . . . . . . . . . .
-19 112 112 . . . . . . . . . . . . . . . . .
-18 112 112 . . . . . . . . . . . . . . . . .
-17 112 112 . . . . . . . . . . . . . . . . .
-16 112 112 . . . . . . . . . . . . . . . . .
-15 112 112 . . . . . . . . . . . . . . . . .
-14 112 112 . . . . . . . . . . . . . . . . .
-13 112 112 . . . . . . . . . . . . . . . . .
-12 112 112 . . . . . . . . . . . . . . . . .
-11 112 112 . . . . . . . . . . . . . . . . .
-10 112 112 . . . . . . . . . . . . . . . . .
-9 112 112 . . . . . . . . . . . . . . . . .
-8 112 112 . . . . . . . . . . . . . . . . .
-7 112 112 . . . . . . . . . . . . . . . . .
-6 112 112 . . . . . . . . . . . . . . . . .
-5 112 112 . . . . . . . . . . . . . . . . .
-4 112 112 . . . . . . . . . . . . . . . . .
-3 112 112 . . . . . . . . . . . . . . . . .
-2 112 112 . . . . . . . . . . . . . . . . .
-1 112 112 . . . . . . . . . . . . . . . . .
. 112 112 . . . . . . . . . . . . . . . . .
1 112 112 . . . . . . . . . . . . . . . . .
2 112 112 . . . . . . . . . . . . . . . . .
3 112 112 . . . . . . . . . . . . . . . . .
4 112 112 . . . . . . . . . . . . . . . . .
5 112 112 . . . . . . . . . . . . . . . . .
6 112 20 92 . . . . . . . . . . . . . . . .
7 112 20 92 . . . . . . . . . . . . . . . .
8 112 20 92 . . . . . . . . . . . . . . . .
9 112 3 76 . . . 33 . . . . . . . . . . . .
10 112 3 109 . . . . . . . . . . . . . . . .
11 112 3 109 . . . . . . . . . . . . . . . .
12 112 3 109 . . . . . . . . . . . . . . . .
13 112 3 93 16 . . . . . . . . . . . . . . .
14 112 3 14 . . . . . . . . 95 . . . . . . .
15 112 3 109 . . . . . . . . . . . . . . . .
16 112 3 109 . . . . . . . . . . . . . . . .
17 112 3 109 . . . . . . . . . . . . . . . .
18 112 3 109 . . . . . . . . . . . . . . . .
19 112 3 109 . . . . . . . . . . . . . . . .
20 112 3 109 . . . . . . . . . . . . . . . .
26 112 . 20 . . . . . . . 76 . . . . . . . 16
28 112 . 100 . . . . . . . . . . . . . 12 . .
30 112 . 24 . . . . . . . . . . . . . 12 . 76
37 112 . 100 . . . . . 12 . . . . . . . . . .
38 112 . 29 83 . . . . . . . . . . . . . . .
45 112 . 96 . . 16 . . . . . . . . . . . . .
46 112 . 100 . . 12 . . . . . . . . . . . . .
47 112 . 100 . . . 12 . . . . . . . . . . . .
52 112 . 100 . . . . . . . 12 . . . . . . . .
53 112 . 54 . . . . . . . 58 . . . . . . . .
55 112 . 57 . . . . . . . 12 . . 43 . . . . .
56 112 . 109 . . . . . . . 3 . . . . . . . .
57 112 . 14 33 64 . . . . . . . . . . . 1 . .
66 112 . 97 . 15 . . . . . . . . . . . . . .
67 112 . 97 . . . . . 15 . . . . . . . . . .
70 112 3 50 . . 3 . . . . . . . . . 56 . . .
71 112 3 14 . 3 . . . . 12 . . . . . . . 80 .
72 112 3 109 . . . . . . . . . . . . . . . .
73 112 3 109 . . . . . . . . . . . . . . . .
74 112 3 17 12 . 80 . . . . . . . . . . . . .
75 112 3 29 . . . . . . . 80 . . . . . . . .
76 112 3 109 . . . . . . . . . . . . . . . .
77 112 3 26 . . . . . . . . . . . . . . 83 .
78 112 3 109 . . . . . . . . . . . . . . . .
79 112 3 109 . . . . . . . . . . . . . . . .
80 112 3 109 . . . . . . . . . . . . . . . .
81 112 3 109 . . . . . . . . . . . . . . . .
82 112 3 109 . . . . . . . . . . . . . . . .
83 112 3 109 . . . . . . . . . . . . . . . .
84 112 3 51 . . . . . . . . . . . 58 . . . .
85 112 3 51 . . . . . . . 58 . . . . . . . .
86 112 3 50 . . 58 . 1 . . . . . . . . . . .
87 112 3 15 . . . 36 . . . 58 . . . . . . . .
88 112 3 109 . . . . . . . . . . . . . . . .
89 112 3 51 . . . . . . . . . . . . . . 58 .
90 112 3 51 . . . . . . . . . . . . . . 58 .
91 112 3 109 . . . . . . . . . . . . . . . .
92 112 3 109 . . . . . . . . . . . . . . . .
93 112 3 109 . . . . . . . . . . . . . . . .
94 112 17 95 . . . . . . . . . . . . . . . .
95 112 112 . . . . . . . . . . . . . . . . .
96 112 112 . . . . . . . . . . . . . . . . .
97 112 112 . . . . . . . . . . . . . . . . .
98 112 112 . . . . . . . . . . . . . . . . .
99 112 112 . . . . . . . . . . . . . . . . .
100 112 112 . . . . . . . . . . . . . . . . .
101 112 112 . . . . . . . . . . . . . . . . .
102 112 112 . . . . . . . . . . . . . . . . .
103 112 112 . . . . . . . . . . . . . . . . .
104 112 112 . . . . . . . . . . . . . . . . .
105 112 112 . . . . . . . . . . . . . . . . .
106 112 112 . . . . . . . . . . . . . . . . .
107 112 112 . . . . . . . . . . . . . . . . .
108 112 112 . . . . . . . . . . . . . . . . .
109 112 112 . . . . . . . . . . . . . . . . .
110 112 112 . . . . . . . . . . . . . . . . .
111 112 112 . . . . . . . . . . . . . . . . .
112 112 112 . . . . . . . . . . . . . . . . .
113 112 112 . . . . . . . . . . . . . . . . .
114 112 112 . . . . . . . . . . . . . . . . .
115 112 112 . . . . . . . . . . . . . . . . .
116 112 112 . . . . . . . . . . . . . . . . .
117 112 112 . . . . . . . . . . . . . . . . .
118 112 112 . . . . . . . . . . . . . . . . .
119 112 112 . . . . . . . . . . . . . . . . .
120 112 112 . . . . . . . . . . . . . . . . .
121 112 112 . . . . . . . . . . . . . . . . .
122 112 112 . . . . . . . . . . . . . . . . .
123 112 112 . . . . . . . . . . . . . . . . .
124 112 112 . . . . . . . . . . . . . . . . .
125 112 112 . . . . . . . . . . . . . . . . .
126 112 112 . . . . . . . . . . . . . . . . .
127 112 112 . . . . . . . . . . . . . . . . .
128 112 112 . . . . . . . . . . . . . . . . .
129 112 112 . . . . . . . . . . . . . . . . .
130 112 112 . . . . . . . . . . . . . . . . .
131 112 112 . . . . . . . . . . . . . . . . .
132 112 112 . . . . . . . . . . . . . . . . .
133 112 112 . . . . . . . . . . . . . . . . .
134 112 112 . . . . . . . . . . . . . . . . .
135 112 112 . . . . . . . . . . . . . . . . .
136 112 112 . . . . . . . . . . . . . . . . .
137 112 112 . . . . . . . . . . . . . . . . .
138 112 112 . . . . . . . . . . . . . . . . .
139 112 112 . . . . . . . . . . . . . . . . .
140 112 112 . . . . . . . . . . . . . . . . .
141 112 112 . . . . . . . . . . . . . . . . .
142 112 112 . . . . . . . . . . . . . . . . .
143 112 112 . . . . . . . . . . . . . . . . .
144 112 112 . . . . . . . . . . . . . . . . .
145 112 112 . . . . . . . . . . . . . . . . .
146 112 112 . . . . . . . . . . . . . . . . .
147 112 112 . . . . . . . . . . . . . . . . .
148 112 112 . . . . . . . . . . . . . . . . .
149 112 112 . . . . . . . . . . . . . . . . .
150 112 112 . . . . . . . . . . . . . . . . .
151 112 112 . . . . . . . . . . . . . . . . .
152 112 112 . . . . . . . . . . . . . . . . .
153 112 112 . . . . . . . . . . . . . . . . .
154 112 112 . . . . . . . . . . . . . . . . .
155 112 112 . . . . . . . . . . . . . . . . .
156 112 112 . . . . . . . . . . . . . . . . .
157 112 112 . . . . . . . . . . . . . . . . .
158 112 112 . . . . . . . . . . . . . . . . .
159 112 112 . . . . . . . . . . . . . . . . .
160 112 112 . . . . . . . . . . . . . . . . .
161 112 112 . . . . . . . . . . . . . . . . .
162 112 112 . . . . . . . . . . . . . . . . .
163 112 112 . . . . . . . . . . . . . . . . .
164 112 112 . . . . . . . . . . . . . . . . .
165 112 112 . . . . . . . . . . . . . . . . .
166 112 112 . . . . . . . . . . . . . . . . .
167 112 112 . . . . . . . . . . . . . . . . .
168 112 112 . . . . . . . . . . . . . . . . .
169 112 112 . . . . . . . . . . . . . . . . .
170 112 112 . . . . . . . . . . . . . . . . .
171 112 112 . . . . . . . . . . . . . . . . .
172 112 112 . . . . . . . . . . . . . . . . .
173 112 112 . . . . . . . . . . . . . . . . .
174 112 112 . . . . . . . . . . . . . . . . .
175 112 112 . . . . . . . . . . . . . . . . .
176 112 112 . . . . . . . . . . . . . . . . .
177 112 112 . . . . . . . . . . . . . . . . .
178 112 112 . . . . . . . . . . . . . . . . .
179 112 112 . . . . . . . . . . . . . . . . .
180 112 112 . . . . . . . . . . . . . . . . .
181 112 112 . . . . . . . . . . . . . . . . .
182 112 112 . . . . . . . . . . . . . . . . .
183 112 112 . . . . . . . . . . . . . . . . .
184 112 112 . . . . . . . . . . . . . . . . .
185 112 112 . . . . . . . . . . . . . . . . .
186 112 112 . . . . . . . . . . . . . . . . .
187 112 112 . . . . . . . . . . . . . . . . .
188 112 112 . . . . . . . . . . . . . . . . .
189 112 112 . . . . . . . . . . . . . . . . .
190 112 112 . . . . . . . . . . . . . . . . .
191 112 112 . . . . . . . . . . . . . . . . .
192 112 112 . . . . . . . . . . . . . . . . .
193 112 112 . . . . . . . . . . . . . . . . .
194 112 112 . . . . . . . . . . . . . . . . .
195 112 112 . . . . . . . . . . . . . . . . .
196 112 112 . . . . . . . . . . . . . . . . .
197 112 112 . . . . . . . . . . . . . . . . .
198 112 112 . . . . . . . . . . . . . . . . .
199 112 112 . . . . . . . . . . . . . . . . .
200 112 112 . . . . . . . . . . . . . . . . .
201 112 112 . . . . . . . . . . . . . . . . .
202 112 112 . . . . . . . . . . . . . . . . .
203 112 112 . . . . . . . . . . . . . . . . .
204 112 112 . . . . . . . . . . . . . . . . .
205 112 112 . . . . . . . . . . . . . . . . .
206 112 112 . . . . . . . . . . . . . . . . .
207 112 112 . . . . . . . . . . . . . . . . .
208 112 112 . . . . . . . . . . . . . . . . .
209 112 112 . . . . . . . . . . . . . . . . .
210 112 112 . . . . . . . . . . . . . . . . .
211 112 112 . . . . . . . . . . . . . . . . .
212 112 112 . . . . . . . . . . . . . . . . .
213 112 112 . . . . . . . . . . . . . . . . .
214 112 112 . . . . . . . . . . . . . . . . .
215 112 112 . . . . . . . . . . . . . . . . .
216 112 112 . . . . . . . . . . . . . . . . .
217 112 112 . . . . . . . . . . . . . . . . .
218 112 112 . . . . . . . . . . . . . . . . .
219 112 112 . . . . . . . . . . . . . . . . .
220 112 112 . . . . . . . . . . . . . . . . .
221 112 112 . . . . . . . . . . . . . . . . .
222 112 112 . . . . . . . . . . . . . . . . .
223 112 112 . . . . . . . . . . . . . . . . .
224 112 112 . . . . . . . . . . . . . . . . .
225 112 112 . . . . . . . . . . . . . . . . .
226 112 112 . . . . . . . . . . . . . . . . .
227 112 112 . . . . . . . . . . . . . . . . .
228 112 112 . . . . . . . . . . . . . . . . .
229 112 112 . . . . . . . . . . . . . . . . .
230 112 112 . . . . . . . . . . . . . . . . .
231 112 112 . . . . . . . . . . . . . . . . .
232 112 112 . . . . . . . . . . . . . . . . .
233 112 112 . . . . . . . . . . . . . . . . .
234 112 112 . . . . . . . . . . . . . . . . .
235 112 112 . . . . . . . . . . . . . . . . .
236 112 112 . . . . . . . . . . . . . . . . .
237 112 112 . . . . . . . . . . . . . . . . .
using the default genome assembly (assembly="hg19")
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 9 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:21
=== building individual classifier 1, out-of-bag (23/38.3%) ===
[1] 2021-10-15 00:23:21, oob acc: 86.96%, # of SNPs: 12, # of haplo: 32
=== building individual classifier 2, out-of-bag (24/40.0%) ===
[2] 2021-10-15 00:23:21, oob acc: 87.50%, # of SNPs: 15, # of haplo: 40
=== building individual classifier 3, out-of-bag (24/40.0%) ===
[3] 2021-10-15 00:23:21, oob acc: 97.92%, # of SNPs: 14, # of haplo: 21
=== building individual classifier 4, out-of-bag (22/36.7%) ===
[4] 2021-10-15 00:23:22, oob acc: 95.45%, # of SNPs: 14, # of haplo: 25
=== building individual classifier 5, out-of-bag (19/31.7%) ===
[5] 2021-10-15 00:23:22, oob acc: 78.95%, # of SNPs: 14, # of haplo: 21
=== building individual classifier 6, out-of-bag (24/40.0%) ===
[6] 2021-10-15 00:23:22, oob acc: 93.75%, # of SNPs: 16, # of haplo: 22
=== building individual classifier 7, out-of-bag (24/40.0%) ===
[7] 2021-10-15 00:23:22, oob acc: 93.75%, # of SNPs: 24, # of haplo: 81
=== building individual classifier 8, out-of-bag (21/35.0%) ===
[8] 2021-10-15 00:23:22, oob acc: 92.86%, # of SNPs: 20, # of haplo: 45
=== building individual classifier 9, out-of-bag (19/31.7%) ===
[9] 2021-10-15 00:23:22, oob acc: 94.74%, # of SNPs: 16, # of haplo: 45
=== building individual classifier 10, out-of-bag (19/31.7%) ===
[10] 2021-10-15 00:23:22, oob acc: 97.37%, # of SNPs: 15, # of haplo: 40
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001493079 0.0001617044 0.0002732730 0.0039571951 0.0150509810 0.0326242837
Max. Mean SD
0.3657388922 0.0410332850 0.0799788450
Accuracy with training data: 98.33%
Out-of-bag accuracy: 91.92%
Gene: HLA-A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 10
total # of SNPs used: 95
avg. # of SNPs in an individual classifier: 16.00
(sd: 3.50, min: 12, max: 24, median: 15.00)
avg. # of haplotypes in an individual classifier: 37.20
(sd: 18.22, min: 21, max: 81, median: 36.00)
avg. out-of-bag accuracy: 91.92%
(sd: 5.83%, min: 78.95%, max: 97.92%, median: 93.75%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001493079 0.0001617044 0.0002732730 0.0039571951 0.0150509810 0.0326242837
Max. Mean SD
0.3657388922 0.0410332850 0.0799788450
Genome assembly: hg19
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
using the default genome assembly (assembly="hg19")
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
Loading required namespace: gdsfmt
Loading required namespace: SNPRelate
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU_Chr6.gds'
Import 1668 SNPs within the xMHC region on chromosome 6
2 SNPs with invalid alleles have been removed.
SNP genotypes:
165 samples X 1666 SNPs
SNPs range from 28837960bp to 33524089bp on hg18
Missing rate per SNP:
min: 0, max: 0.0484848, mean: 0.00175707, median: 0, sd: 0.00515153
Missing rate per sample:
min: 0, max: 0.0120048, mean: 0.00175707, median: 0.00120048, sd: 0.00210091
Minor allele frequency:
min: 0, max: 0.5, mean: 0.19767, median: 0.175758, sd: 0.150469
Allelic information:
A/G T/C G/A C/T T/G A/C C/A G/T A/T C/G G/C T/A
412 318 299 285 79 76 75 56 20 19 16 11
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed' (the individual-major mode)
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam'
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim'
Import 3932 SNPs within the xMHC region on chromosome 6
No allelic strand or A/B allele is flipped.
SNP genotypes:
150 samples X 1214 SNPs
SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0866667, mean: 0.0844646, median: 0.0866667, sd: 0.0128841
Missing rate per sample:
min: 0, max: 0.968699, mean: 0.0844646, median: 0.000823723, sd: 0.273119
Minor allele frequency:
min: 0, max: 0.5, mean: 0.234168, median: 0.218978, sd: 0.137855
Allelic information:
A/G C/T G/T A/C
505 496 109 104
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1197 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0657059, median: 0.0666667, sd: 0.00757446
Missing rate per sample:
min: 0, max: 0.978279, mean: 0.0657059, median: 0.000835422, sd: 0.245786
Minor allele frequency:
min: 0.101695, max: 0.5, mean: 0.278734, median: 0.267857, sd: 0.117338
Allelic information:
A/G C/T A/C G/T
511 476 105 105
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed' (the individual-major mode)
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam'
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim'
Import 3932 SNPs within the xMHC region on chromosome 6
SNP genotypes:
90 samples X 3932 SNPs
SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
A/G C/T G/T A/C C/G A/T
1567 1510 348 332 111 64
No allelic strand or A/B allele is flipped.
SNP genotypes:
60 samples X 1214 SNPs
SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0650879, median: 0.0666667, sd: 0.0097381
Missing rate per sample:
min: 0, max: 0.968699, mean: 0.0650879, median: 0.000823723, sd: 0.243373
Minor allele frequency:
min: 0, max: 0.5, mean: 0.234476, median: 0.223214, sd: 0.13833
Allelic information:
A/G C/T G/T A/C
505 496 109 104
using the default genome assembly (assembly="hg19")
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
MAF filter (>=0.01), excluding 9 SNP(s)
using the default genome assembly (assembly="hg19")
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Build a HIBAG model with 1 individual classifier:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:25
=== building individual classifier 1, out-of-bag (25/41.7%) ===
[1] 2021-10-15 00:23:25, oob acc: 92.00%, # of SNPs: 24, # of haplo: 29
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.222247e-28 1.128571e-24 1.128371e-23 6.944660e-04 8.333349e-03 3.673611e-02
Max. Mean SD
9.105734e-02 2.054649e-02 2.598603e-02
Accuracy with training data: 96.67%
Out-of-bag accuracy: 92.00%
Build a HIBAG model with 1 individual classifier:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:25
=== building individual classifier 1, out-of-bag (20/33.3%) ===
[1] 2021-10-15 00:23:25, oob acc: 97.50%, # of SNPs: 18, # of haplo: 34
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
5.014366e-13 4.671716e-10 4.667203e-09 1.640727e-03 7.504546e-03 2.126745e-02
Max. Mean SD
9.784316e-02 1.490504e-02 1.947399e-02
Accuracy with training data: 97.50%
Out-of-bag accuracy: 97.50%
Build a HIBAG model with 1 individual classifier:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:25
=== building individual classifier 1, out-of-bag (18/30.0%) ===
[1] 2021-10-15 00:23:25, oob acc: 88.89%, # of SNPs: 14, # of haplo: 38
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.222223e-18 6.603163e-16 6.583163e-15 1.944468e-03 1.020834e-02 4.122739e-02
Max. Mean SD
1.808372e-01 2.422083e-02 3.699146e-02
Accuracy with training data: 95.83%
Out-of-bag accuracy: 88.89%
Gene: HLA-C
Training dataset: 60 samples X 83 SNPs
# of HLA alleles: 17
# of individual classifiers: 3
total # of SNPs used: 40
avg. # of SNPs in an individual classifier: 18.67
(sd: 5.03, min: 14, max: 24, median: 18.00)
avg. # of haplotypes in an individual classifier: 33.67
(sd: 4.51, min: 29, max: 38, median: 34.00)
avg. out-of-bag accuracy: 92.80%
(sd: 4.36%, min: 88.89%, max: 97.50%, median: 92.00%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.708707e-13 1.229313e-05 1.229313e-04 1.860746e-03 9.050936e-03 3.332722e-02
Max. Mean SD
1.210500e-01 1.989079e-02 2.507466e-02
Genome assembly: hg19
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 1 monomorphic SNP
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77
# of samples: 60
# of unique HLA alleles: 12
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:25
=== building individual classifier 1, out-of-bag (25/41.7%) ===
[1] 2021-10-15 00:23:25, oob acc: 98.00%, # of SNPs: 13, # of haplo: 20
=== building individual classifier 2, out-of-bag (22/36.7%) ===
[2] 2021-10-15 00:23:25, oob acc: 90.91%, # of SNPs: 15, # of haplo: 21
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.196909e-03 1.419916e-02 2.996040e-02
Max. Mean SD
4.735980e-01 4.413724e-02 1.070518e-01
Accuracy with training data: 95.00%
Out-of-bag accuracy: 94.45%
Gene: HLA-DQB1
Training dataset: 60 samples X 77 SNPs
# of HLA alleles: 12
# of individual classifiers: 2
total # of SNPs used: 20
avg. # of SNPs in an individual classifier: 14.00
(sd: 1.41, min: 13, max: 15, median: 14.00)
avg. # of haplotypes in an individual classifier: 20.50
(sd: 0.71, min: 20, max: 21, median: 20.50)
avg. out-of-bag accuracy: 94.45%
(sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.196909e-03 1.419916e-02 2.996040e-02
Max. Mean SD
4.735980e-01 4.413724e-02 1.070518e-01
Genome assembly: hg19
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 9 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:25
=== building individual classifier 1, out-of-bag (23/38.3%) ===
[1] 2021-10-15 00:23:25, oob acc: 86.96%, # of SNPs: 12, # of haplo: 32
=== building individual classifier 2, out-of-bag (24/40.0%) ===
[2] 2021-10-15 00:23:25, oob acc: 87.50%, # of SNPs: 15, # of haplo: 40
=== building individual classifier 3, out-of-bag (24/40.0%) ===
[3] 2021-10-15 00:23:25, oob acc: 97.92%, # of SNPs: 14, # of haplo: 21
=== building individual classifier 4, out-of-bag (22/36.7%) ===
[4] 2021-10-15 00:23:25, oob acc: 95.45%, # of SNPs: 14, # of haplo: 25
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777
Max. Mean SD
0.3658111951 0.0404459574 0.0794719104
Accuracy with training data: 99.17%
Out-of-bag accuracy: 91.96%
Gene: HLA-A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 42
avg. # of SNPs in an individual classifier: 13.75
(sd: 1.26, min: 12, max: 15, median: 14.00)
avg. # of haplotypes in an individual classifier: 29.50
(sd: 8.35, min: 21, max: 40, median: 28.50)
avg. out-of-bag accuracy: 91.96%
(sd: 5.56%, min: 86.96%, max: 97.92%, median: 91.48%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777
Max. Mean SD
0.3658111951 0.0404459574 0.0794719104
Genome assembly: hg19
Gene: HLA-A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 42
avg. # of SNPs in an individual classifier: 13.75
(sd: 1.26, min: 12, max: 15, median: 14.00)
avg. # of haplotypes in an individual classifier: 29.50
(sd: 8.35, min: 21, max: 40, median: 28.50)
avg. out-of-bag accuracy: 91.96%
(sd: 5.56%, min: 86.96%, max: 97.92%, median: 91.48%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777
Max. Mean SD
0.3658111951 0.0404459574 0.0794719104
Genome assembly: hg19
Fri Oct 15 00:23:25 2021, passing the 1/4 classifiers.
Fri Oct 15 00:23:25 2021, passing the 2/4 classifiers.
Fri Oct 15 00:23:25 2021, passing the 3/4 classifiers.
Fri Oct 15 00:23:25 2021, passing the 4/4 classifiers.
Allele Num. Freq. CR ACC SEN SPE PPV NPV Miscall
Valid. Valid. (%) (%) (%) (%) (%) (%) (%)
----
Overall accuracy: 92.0%, Call rate: 100.0%
01:01 25 0.2083 100.0 100.0 100.0 100.0 100.0 100.0 --
02:01 43 0.3583 100.0 96.7 100.0 95.1 92.5 100.0 --
02:06 1 0.0083 25.0 97.7 0.0 100.0 -- 97.7 02:01 (100)
03:01 9 0.0750 100.0 100.0 100.0 100.0 100.0 100.0 --
11:01 5 0.0417 100.0 100.0 100.0 100.0 100.0 100.0 --
23:01 3 0.0250 100.0 98.4 75.0 100.0 100.0 98.4 24:02 (100)
24:02 11 0.0917 100.0 97.3 100.0 97.1 76.2 100.0 --
24:03 1 0.0083 100.0 97.8 0.0 100.0 -- 97.8 24:02 (75)
25:01 5 0.0417 100.0 98.4 100.0 98.3 84.7 100.0 --
26:01 3 0.0250 100.0 98.4 62.5 100.0 100.0 98.4 25:01 (83)
29:02 4 0.0333 100.0 97.8 75.0 100.0 100.0 97.8 02:01 (75)
31:01 3 0.0250 75.0 100.0 100.0 100.0 100.0 100.0 --
32:01 4 0.0333 100.0 100.0 100.0 100.0 100.0 100.0 --
68:01 3 0.0250 100.0 100.0 100.0 100.0 100.0 100.0 --
\title{Imputation Evaluation}
\documentclass[12pt]{article}
\usepackage{fullpage}
\usepackage{longtable}
\begin{document}
\maketitle
\setlength{\LTcapwidth}{6.5in}
% -------- BEGIN TABLE --------
\begin{longtable}{rrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{10}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{10}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{10}{l}{\it Overall accuracy: 92.0\%, Call rate: 100.0\%} \\
01:01 & 25 & 0.2083 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
02:01 & 43 & 0.3583 & 100.0 & 96.7 & 100.0 & 95.1 & 92.5 & 100.0 & -- \\
02:06 & 1 & 0.0083 & 25.0 & 97.7 & 0.0 & 100.0 & -- & 97.7 & 02:01 (100) \\
03:01 & 9 & 0.0750 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
11:01 & 5 & 0.0417 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
23:01 & 3 & 0.0250 & 100.0 & 98.4 & 75.0 & 100.0 & 100.0 & 98.4 & 24:02 (100) \\
24:02 & 11 & 0.0917 & 100.0 & 97.3 & 100.0 & 97.1 & 76.2 & 100.0 & -- \\
24:03 & 1 & 0.0083 & 100.0 & 97.8 & 0.0 & 100.0 & -- & 97.8 & 24:02 (75) \\
25:01 & 5 & 0.0417 & 100.0 & 98.4 & 100.0 & 98.3 & 84.7 & 100.0 & -- \\
26:01 & 3 & 0.0250 & 100.0 & 98.4 & 62.5 & 100.0 & 100.0 & 98.4 & 25:01 (83) \\
29:02 & 4 & 0.0333 & 100.0 & 97.8 & 75.0 & 100.0 & 100.0 & 97.8 & 02:01 (75) \\
31:01 & 3 & 0.0250 & 75.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
32:01 & 4 & 0.0333 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 3 & 0.0250 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
\end{longtable}
% -------- END TABLE --------
\end{document}
<!DOCTYPE html>
<html>
<head>
<title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1" CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="10">
<i> Overall accuracy: 92.0%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>25</td> <td>0.2083</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>43</td> <td>0.3583</td> <td>100.0</td> <td>96.7</td> <td>100.0</td> <td>95.1</td> <td>92.5</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0083</td> <td>25.0</td> <td>97.7</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.7</td> <td>02:01 (100)</td>
</tr>
<tr>
<td>03:01</td> <td>9</td> <td>0.0750</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>11:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>98.4</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>98.4</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>24:02</td> <td>11</td> <td>0.0917</td> <td>100.0</td> <td>97.3</td> <td>100.0</td> <td>97.1</td> <td>76.2</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0083</td> <td>100.0</td> <td>97.8</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.8</td> <td>24:02 (75)</td>
</tr>
<tr>
<td>25:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>98.4</td> <td>100.0</td> <td>98.3</td> <td>84.7</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>26:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>98.4</td> <td>62.5</td> <td>100.0</td> <td>100.0</td> <td>98.4</td> <td>25:01 (83)</td>
</tr>
<tr>
<td>29:02</td> <td>4</td> <td>0.0333</td> <td>100.0</td> <td>97.8</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>97.8</td> <td>02:01 (75)</td>
</tr>
<tr>
<td>31:01</td> <td>3</td> <td>0.0250</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>32:01</td> <td>4</td> <td>0.0333</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
</table>
</body>
</html>
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Building a HIBAG model:
4 individual classifiers
run in parallel with 1 compute node
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 2
[-] 2021-10-15 00:23:25
=== building individual classifier 1, out-of-bag (11/32.4%) ===
[1] 2021-10-15 00:23:26, oob acc: 77.27%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
[2] 2021-10-15 00:23:26, oob acc: 88.46%, # of SNPs: 12, # of haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
[3] 2021-10-15 00:23:26, oob acc: 85.71%, # of SNPs: 11, # of haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
[4] 2021-10-15 00:23:26, oob acc: 75.00%, # of SNPs: 13, # of haplo: 23
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Accuracy with training data: 97.06%
Out-of-bag accuracy: 81.61%
Building a HIBAG model:
4 individual classifiers
run in parallel with 2 compute nodes
autosave to 'tmp_model.RData'
[-] 2021-10-15 00:23:26
[1] 2021-10-15 00:23:27, worker 2, # of SNPs: 12, # of haplo: 53, oob acc: 90.9%
==Saved== #1, avg oob acc: 90.91%, sd: NA%, min: 90.91%, max: 90.91%
[2] 2021-10-15 00:23:27, worker 2, # of SNPs: 14, # of haplo: 26, oob acc: 90.9%
==Saved== #2, avg oob acc: 90.91%, sd: 0.00%, min: 90.91%, max: 90.91%
[3] 2021-10-15 00:23:27, worker 1, # of SNPs: 14, # of haplo: 70, oob acc: 90.9%
Stop "job 1".
==Saved== #3, avg oob acc: 90.91%, sd: 0.00%, min: 90.91%, max: 90.91%
[4] 2021-10-15 00:23:27, worker 1, # of SNPs: 15, # of haplo: 51, oob acc: 70.8%
Stop "job 1".
==Saved== #4, avg oob acc: 85.89%, sd: 10.04%, min: 70.83%, max: 90.91%
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001081400 0.0001260082 0.0002868224 0.0024430233 0.0080031409 0.0323120548
Max. Mean SD
0.4045891177 0.0444693277 0.0988535258
Accuracy with training data: 98.53%
Out-of-bag accuracy: 85.89%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 46
avg. # of SNPs in an individual classifier: 13.75
(sd: 1.26, min: 12, max: 15, median: 14.00)
avg. # of haplotypes in an individual classifier: 50.00
(sd: 18.13, min: 26, max: 70, median: 52.00)
avg. out-of-bag accuracy: 85.89%
(sd: 10.04%, min: 70.83%, max: 90.91%, median: 90.91%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001081400 0.0001260082 0.0002868224 0.0024430233 0.0080031409 0.0323120548
Max. Mean SD
0.4045891177 0.0444693277 0.0988535258
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:27) 0%
Predicting (2021-10-15 00:23:27) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 4 (15.4%) 5 (19.2%) 16 (61.5%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.001608 0.003090 0.029774 0.027387 0.404589
Dosages:
$dosage - num [1:14, 1:26] 2.59e-10 5.05e-09 4.01e-12 1.00 2.08e-15 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:14] "01:01" "02:01" "02:06" "03:01" ...
..$ : chr [1:26] "NA11881" "NA11992" "NA11994" "NA12249" ...
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
run in parallel with 2 compute nodes
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 4 (15.4%) 5 (19.2%) 16 (61.5%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.001608 0.003090 0.029774 0.027387 0.404589
Dosages:
$dosage - num [1:14, 1:26] 2.59e-10 5.05e-09 4.01e-12 1.00 2.08e-15 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:14] "01:01" "02:01" "02:06" "03:01" ...
..$ : chr [1:26] "NA11881" "NA11992" "NA11994" "NA12249" ...
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:27
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 235, loss: 208.912, oob acc: 59.09%, # of haplo: 14
2, SNP: 136, loss: 139.316, oob acc: 63.64%, # of haplo: 14
3, SNP: 126, loss: 96.148, oob acc: 72.73%, # of haplo: 14
4, SNP: 89, loss: 76.2917, oob acc: 77.27%, # of haplo: 15
5, SNP: 102, loss: 61.3783, oob acc: 86.36%, # of haplo: 15
6, SNP: 105, loss: 49.1567, oob acc: 90.91%, # of haplo: 15
7, SNP: 117, loss: 43.0927, oob acc: 95.45%, # of haplo: 15
8, SNP: 259, loss: 26.6243, oob acc: 95.45%, # of haplo: 17
9, SNP: 60, loss: 17.6253, oob acc: 95.45%, # of haplo: 19
10, SNP: 236, loss: 9.50329, oob acc: 95.45%, # of haplo: 20
11, SNP: 94, loss: 7.27191, oob acc: 95.45%, # of haplo: 21
12, SNP: 58, loss: 6.70503, oob acc: 95.45%, # of haplo: 27
13, SNP: 243, loss: 2.87079, oob acc: 95.45%, # of haplo: 30
14, SNP: 5, loss: 2.77321, oob acc: 95.45%, # of haplo: 31
[1] 2021-10-15 00:23:27, oob acc: 95.45%, # of SNPs: 14, # of haplo: 31
=== building individual classifier 2, out-of-bag (9/26.5%) ===
1, SNP: 149, loss: 171.797, oob acc: 66.67%, # of haplo: 13
2, SNP: 176, loss: 120.459, oob acc: 72.22%, # of haplo: 14
3, SNP: 97, loss: 80.1731, oob acc: 83.33%, # of haplo: 14
4, SNP: 56, loss: 51.5193, oob acc: 94.44%, # of haplo: 16
5, SNP: 182, loss: 34.5643, oob acc: 94.44%, # of haplo: 18
6, SNP: 121, loss: 23.0259, oob acc: 94.44%, # of haplo: 18
7, SNP: 234, loss: 15.0596, oob acc: 94.44%, # of haplo: 20
8, SNP: 148, loss: 9.66757, oob acc: 94.44%, # of haplo: 20
9, SNP: 19, loss: 4.29975, oob acc: 94.44%, # of haplo: 27
10, SNP: 226, loss: 0.481093, oob acc: 94.44%, # of haplo: 27
11, SNP: 64, loss: 0.447483, oob acc: 94.44%, # of haplo: 28
12, SNP: 240, loss: 0.365545, oob acc: 94.44%, # of haplo: 37
13, SNP: 57, loss: 0.365132, oob acc: 94.44%, # of haplo: 38
[2] 2021-10-15 00:23:28, oob acc: 94.44%, # of SNPs: 13, # of haplo: 38
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.275953e-07 1.742509e-05 1.731025e-04 2.811482e-03 8.650597e-03 1.989621e-02
Max. Mean SD
5.990492e-02 1.464043e-02 1.658610e-02
Accuracy with training data: 100.00%
Out-of-bag accuracy: 94.95%
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:28
=== building individual classifier 1, out-of-bag (14/41.2%) ===
1, SNP: 118, loss: 190.304, oob acc: 53.57%, # of haplo: 13
2, SNP: 175, loss: 157.208, oob acc: 60.71%, # of haplo: 15
3, SNP: 103, loss: 128.429, oob acc: 64.29%, # of haplo: 15
4, SNP: 182, loss: 66.6054, oob acc: 71.43%, # of haplo: 15
5, SNP: 152, loss: 58.8041, oob acc: 78.57%, # of haplo: 15
6, SNP: 111, loss: 30.086, oob acc: 82.14%, # of haplo: 15
7, SNP: 130, loss: 15.3177, oob acc: 89.29%, # of haplo: 19
8, SNP: 229, loss: 9.99758, oob acc: 89.29%, # of haplo: 28
9, SNP: 185, loss: 7.40712, oob acc: 89.29%, # of haplo: 29
10, SNP: 199, loss: 6.21341, oob acc: 89.29%, # of haplo: 29
11, SNP: 217, loss: 1.38739, oob acc: 89.29%, # of haplo: 30
[1] 2021-10-15 00:23:28, oob acc: 89.29%, # of SNPs: 11, # of haplo: 30
=== building individual classifier 2, out-of-bag (13/38.2%) ===
1, SNP: 101, loss: 154.355, oob acc: 46.15%, # of haplo: 16
2, SNP: 102, loss: 139.148, oob acc: 61.54%, # of haplo: 22
3, SNP: 132, loss: 95.2502, oob acc: 73.08%, # of haplo: 23
4, SNP: 147, loss: 76.9692, oob acc: 76.92%, # of haplo: 34
5, SNP: 53, loss: 68.3851, oob acc: 88.46%, # of haplo: 51
6, SNP: 186, loss: 41.8787, oob acc: 88.46%, # of haplo: 53
7, SNP: 128, loss: 33.5437, oob acc: 92.31%, # of haplo: 53
8, SNP: 14, loss: 23.3103, oob acc: 92.31%, # of haplo: 55
9, SNP: 219, loss: 18.3628, oob acc: 92.31%, # of haplo: 57
10, SNP: 149, loss: 17.9413, oob acc: 92.31%, # of haplo: 89
11, SNP: 73, loss: 16.3172, oob acc: 92.31%, # of haplo: 90
12, SNP: 70, loss: 16.1056, oob acc: 92.31%, # of haplo: 90
13, SNP: 199, loss: 12.3057, oob acc: 92.31%, # of haplo: 90
14, SNP: 203, loss: 12.2013, oob acc: 92.31%, # of haplo: 90
15, SNP: 151, loss: 11.1795, oob acc: 92.31%, # of haplo: 90
[2] 2021-10-15 00:23:28, oob acc: 92.31%, # of SNPs: 15, # of haplo: 90
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002703521 0.0002971139 0.0005379705 0.0036521203 0.0131584084 0.0415528465
Max. Mean SD
0.5087413114 0.0420589840 0.0891771528
Accuracy with training data: 97.06%
Out-of-bag accuracy: 90.80%
HIBAG model for HLA-A:
2 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
by voting from all individual classifiers
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:28) 0%
Predicting (2021-10-15 00:23:28) 100%
HIBAG model for HLA-A:
2 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
by voting from all individual classifiers
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:28) 0%
Predicting (2021-10-15 00:23:28) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:28
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 235, loss: 208.912, oob acc: 59.09%, # of haplo: 14
2, SNP: 136, loss: 139.316, oob acc: 63.64%, # of haplo: 14
3, SNP: 126, loss: 96.148, oob acc: 72.73%, # of haplo: 14
4, SNP: 89, loss: 76.2917, oob acc: 77.27%, # of haplo: 15
5, SNP: 102, loss: 61.3783, oob acc: 86.36%, # of haplo: 15
6, SNP: 105, loss: 49.1567, oob acc: 90.91%, # of haplo: 15
7, SNP: 117, loss: 43.0927, oob acc: 95.45%, # of haplo: 15
8, SNP: 259, loss: 26.6243, oob acc: 95.45%, # of haplo: 17
9, SNP: 60, loss: 17.6253, oob acc: 95.45%, # of haplo: 19
10, SNP: 236, loss: 9.50329, oob acc: 95.45%, # of haplo: 20
11, SNP: 94, loss: 7.27191, oob acc: 95.45%, # of haplo: 21
12, SNP: 58, loss: 6.70503, oob acc: 95.45%, # of haplo: 27
13, SNP: 243, loss: 2.87079, oob acc: 95.45%, # of haplo: 30
14, SNP: 5, loss: 2.77321, oob acc: 95.45%, # of haplo: 31
[1] 2021-10-15 00:23:28, oob acc: 95.45%, # of SNPs: 14, # of haplo: 31
=== building individual classifier 2, out-of-bag (9/26.5%) ===
1, SNP: 149, loss: 171.797, oob acc: 66.67%, # of haplo: 13
2, SNP: 176, loss: 120.459, oob acc: 72.22%, # of haplo: 14
3, SNP: 97, loss: 80.1731, oob acc: 83.33%, # of haplo: 14
4, SNP: 56, loss: 51.5193, oob acc: 94.44%, # of haplo: 16
5, SNP: 182, loss: 34.5643, oob acc: 94.44%, # of haplo: 18
6, SNP: 121, loss: 23.0259, oob acc: 94.44%, # of haplo: 18
7, SNP: 234, loss: 15.0596, oob acc: 94.44%, # of haplo: 20
8, SNP: 148, loss: 9.66757, oob acc: 94.44%, # of haplo: 20
9, SNP: 19, loss: 4.29975, oob acc: 94.44%, # of haplo: 27
10, SNP: 226, loss: 0.481093, oob acc: 94.44%, # of haplo: 27
11, SNP: 64, loss: 0.447483, oob acc: 94.44%, # of haplo: 28
12, SNP: 240, loss: 0.365545, oob acc: 94.44%, # of haplo: 37
13, SNP: 57, loss: 0.365132, oob acc: 94.44%, # of haplo: 38
[2] 2021-10-15 00:23:28, oob acc: 94.44%, # of SNPs: 13, # of haplo: 38
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 118, loss: 190.304, oob acc: 53.57%, # of haplo: 13
2, SNP: 175, loss: 157.208, oob acc: 60.71%, # of haplo: 15
3, SNP: 103, loss: 128.429, oob acc: 64.29%, # of haplo: 15
4, SNP: 182, loss: 66.6054, oob acc: 71.43%, # of haplo: 15
5, SNP: 152, loss: 58.8041, oob acc: 78.57%, # of haplo: 15
6, SNP: 111, loss: 30.086, oob acc: 82.14%, # of haplo: 15
7, SNP: 130, loss: 15.3177, oob acc: 89.29%, # of haplo: 19
8, SNP: 229, loss: 9.99758, oob acc: 89.29%, # of haplo: 28
9, SNP: 185, loss: 7.40712, oob acc: 89.29%, # of haplo: 29
10, SNP: 199, loss: 6.21341, oob acc: 89.29%, # of haplo: 29
11, SNP: 217, loss: 1.38739, oob acc: 89.29%, # of haplo: 30
[3] 2021-10-15 00:23:28, oob acc: 89.29%, # of SNPs: 11, # of haplo: 30
=== building individual classifier 4, out-of-bag (13/38.2%) ===
1, SNP: 101, loss: 154.355, oob acc: 46.15%, # of haplo: 16
2, SNP: 102, loss: 139.148, oob acc: 61.54%, # of haplo: 22
3, SNP: 132, loss: 95.2502, oob acc: 73.08%, # of haplo: 23
4, SNP: 147, loss: 76.9692, oob acc: 76.92%, # of haplo: 34
5, SNP: 53, loss: 68.3851, oob acc: 88.46%, # of haplo: 51
6, SNP: 186, loss: 41.8787, oob acc: 88.46%, # of haplo: 53
7, SNP: 128, loss: 33.5437, oob acc: 92.31%, # of haplo: 53
8, SNP: 14, loss: 23.3103, oob acc: 92.31%, # of haplo: 55
9, SNP: 219, loss: 18.3628, oob acc: 92.31%, # of haplo: 57
10, SNP: 149, loss: 17.9413, oob acc: 92.31%, # of haplo: 89
11, SNP: 73, loss: 16.3172, oob acc: 92.31%, # of haplo: 90
12, SNP: 70, loss: 16.1056, oob acc: 92.31%, # of haplo: 90
13, SNP: 199, loss: 12.3057, oob acc: 92.31%, # of haplo: 90
14, SNP: 203, loss: 12.2013, oob acc: 92.31%, # of haplo: 90
15, SNP: 151, loss: 11.1795, oob acc: 92.31%, # of haplo: 90
[4] 2021-10-15 00:23:28, oob acc: 92.31%, # of SNPs: 15, # of haplo: 90
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Accuracy with training data: 97.06%
Out-of-bag accuracy: 92.87%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 49
avg. # of SNPs in an individual classifier: 13.25
(sd: 1.71, min: 11, max: 15, median: 13.50)
avg. # of haplotypes in an individual classifier: 47.25
(sd: 28.72, min: 30, max: 90, median: 34.50)
avg. out-of-bag accuracy: 92.87%
(sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:28) 0%
Predicting (2021-10-15 00:23:28) 100%
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 1 monomorphic SNP
# of SNPs randomly sampled as candidates for each selection: 13
# of SNPs: 158
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:28
=== building individual classifier 1, out-of-bag (24/40.0%) ===
1, SNP: 141, loss: 378.06, oob acc: 52.08%, # of haplo: 14
2, SNP: 74, loss: 262.497, oob acc: 58.33%, # of haplo: 15
3, SNP: 78, loss: 162.497, oob acc: 68.75%, # of haplo: 19
4, SNP: 118, loss: 70.0426, oob acc: 72.92%, # of haplo: 23
5, SNP: 82, loss: 45.8279, oob acc: 83.33%, # of haplo: 23
6, SNP: 95, loss: 35.4414, oob acc: 89.58%, # of haplo: 27
7, SNP: 89, loss: 32.6134, oob acc: 89.58%, # of haplo: 35
8, SNP: 83, loss: 31.7921, oob acc: 89.58%, # of haplo: 51
9, SNP: 151, loss: 31.0653, oob acc: 89.58%, # of haplo: 55
10, SNP: 94, loss: 31.0246, oob acc: 89.58%, # of haplo: 55
11, SNP: 111, loss: 18.9027, oob acc: 89.58%, # of haplo: 56
12, SNP: 139, loss: 18.4248, oob acc: 89.58%, # of haplo: 59
13, SNP: 93, loss: 17.0195, oob acc: 91.67%, # of haplo: 58
14, SNP: 15, loss: 14.1692, oob acc: 91.67%, # of haplo: 60
[1] 2021-10-15 00:23:28, oob acc: 91.67%, # of SNPs: 14, # of haplo: 60
=== building individual classifier 2, out-of-bag (19/31.7%) ===
1, SNP: 94, loss: 403.365, oob acc: 63.16%, # of haplo: 15
2, SNP: 82, loss: 294.053, oob acc: 71.05%, # of haplo: 18
3, SNP: 57, loss: 226.142, oob acc: 76.32%, # of haplo: 23
4, SNP: 155, loss: 197.199, oob acc: 84.21%, # of haplo: 29
5, SNP: 44, loss: 132.804, oob acc: 86.84%, # of haplo: 40
6, SNP: 30, loss: 122.507, oob acc: 92.11%, # of haplo: 40
7, SNP: 109, loss: 72.0179, oob acc: 92.11%, # of haplo: 41
8, SNP: 72, loss: 59.3281, oob acc: 92.11%, # of haplo: 41
9, SNP: 36, loss: 54.939, oob acc: 94.74%, # of haplo: 43
10, SNP: 127, loss: 48.1392, oob acc: 94.74%, # of haplo: 43
11, SNP: 53, loss: 44.7676, oob acc: 94.74%, # of haplo: 43
12, SNP: 148, loss: 43.047, oob acc: 94.74%, # of haplo: 44
13, SNP: 152, loss: 40.2104, oob acc: 94.74%, # of haplo: 45
14, SNP: 125, loss: 39.8862, oob acc: 94.74%, # of haplo: 45
15, SNP: 78, loss: 39.5652, oob acc: 94.74%, # of haplo: 45
16, SNP: 3, loss: 39.0621, oob acc: 94.74%, # of haplo: 47
17, SNP: 141, loss: 37.6822, oob acc: 94.74%, # of haplo: 47
18, SNP: 1, loss: 36.5022, oob acc: 94.74%, # of haplo: 50
[2] 2021-10-15 00:23:29, oob acc: 94.74%, # of SNPs: 18, # of haplo: 50
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02
Max. Mean SD
4.790185e-01 5.479747e-02 1.101559e-01
Accuracy with training data: 96.67%
Out-of-bag accuracy: 93.20%
Gene: HLA-A
Training dataset: 60 samples X 158 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 28
avg. # of SNPs in an individual classifier: 16.00
(sd: 2.83, min: 14, max: 18, median: 16.00)
avg. # of haplotypes in an individual classifier: 55.00
(sd: 7.07, min: 50, max: 60, median: 55.00)
avg. out-of-bag accuracy: 93.20%
(sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02
Max. Mean SD
4.790185e-01 5.479747e-02 1.101559e-01
Genome assembly: hg19
Remove 130 unused SNPs.
Gene: HLA-A
Training dataset: 60 samples X 28 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 28
avg. # of SNPs in an individual classifier: 16.00
(sd: 2.83, min: 14, max: 18, median: 16.00)
avg. # of haplotypes in an individual classifier: 55.00
(sd: 7.07, min: 50, max: 60, median: 55.00)
avg. out-of-bag accuracy: 93.20%
(sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02
Max. Mean SD
4.790185e-01 5.479747e-02 1.101559e-01
Genome assembly: hg19
Platform: Illumina 1M Duo
Information: Training set -- HapMap Phase II
HIBAG model for HLA-A:
2 individual classifiers
158 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 60
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:29) 0%
Predicting (2021-10-15 00:23:29) 100%
HIBAG model for HLA-A:
2 individual classifiers
28 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: Illumina 1M Duo
No allelic strand or A/B allele is flipped.
# of samples: 60
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:29) 0%
Predicting (2021-10-15 00:23:29) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:29
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 235, loss: 208.912, oob acc: 59.09%, # of haplo: 14
2, SNP: 136, loss: 139.316, oob acc: 63.64%, # of haplo: 14
3, SNP: 126, loss: 96.148, oob acc: 72.73%, # of haplo: 14
4, SNP: 89, loss: 76.2917, oob acc: 77.27%, # of haplo: 15
5, SNP: 102, loss: 61.3783, oob acc: 86.36%, # of haplo: 15
6, SNP: 105, loss: 49.1567, oob acc: 90.91%, # of haplo: 15
7, SNP: 117, loss: 43.0927, oob acc: 95.45%, # of haplo: 15
8, SNP: 259, loss: 26.6243, oob acc: 95.45%, # of haplo: 17
9, SNP: 60, loss: 17.6253, oob acc: 95.45%, # of haplo: 19
10, SNP: 236, loss: 9.50329, oob acc: 95.45%, # of haplo: 20
11, SNP: 94, loss: 7.27191, oob acc: 95.45%, # of haplo: 21
12, SNP: 58, loss: 6.70503, oob acc: 95.45%, # of haplo: 27
13, SNP: 243, loss: 2.87079, oob acc: 95.45%, # of haplo: 30
14, SNP: 5, loss: 2.77321, oob acc: 95.45%, # of haplo: 31
[1] 2021-10-15 00:23:29, oob acc: 95.45%, # of SNPs: 14, # of haplo: 31
=== building individual classifier 2, out-of-bag (9/26.5%) ===
1, SNP: 149, loss: 171.797, oob acc: 66.67%, # of haplo: 13
2, SNP: 176, loss: 120.459, oob acc: 72.22%, # of haplo: 14
3, SNP: 97, loss: 80.1731, oob acc: 83.33%, # of haplo: 14
4, SNP: 56, loss: 51.5193, oob acc: 94.44%, # of haplo: 16
5, SNP: 182, loss: 34.5643, oob acc: 94.44%, # of haplo: 18
6, SNP: 121, loss: 23.0259, oob acc: 94.44%, # of haplo: 18
7, SNP: 234, loss: 15.0596, oob acc: 94.44%, # of haplo: 20
8, SNP: 148, loss: 9.66757, oob acc: 94.44%, # of haplo: 20
9, SNP: 19, loss: 4.29975, oob acc: 94.44%, # of haplo: 27
10, SNP: 226, loss: 0.481093, oob acc: 94.44%, # of haplo: 27
11, SNP: 64, loss: 0.447483, oob acc: 94.44%, # of haplo: 28
12, SNP: 240, loss: 0.365545, oob acc: 94.44%, # of haplo: 37
13, SNP: 57, loss: 0.365132, oob acc: 94.44%, # of haplo: 38
[2] 2021-10-15 00:23:29, oob acc: 94.44%, # of SNPs: 13, # of haplo: 38
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 118, loss: 190.304, oob acc: 53.57%, # of haplo: 13
2, SNP: 175, loss: 157.208, oob acc: 60.71%, # of haplo: 15
3, SNP: 103, loss: 128.429, oob acc: 64.29%, # of haplo: 15
4, SNP: 182, loss: 66.6054, oob acc: 71.43%, # of haplo: 15
5, SNP: 152, loss: 58.8041, oob acc: 78.57%, # of haplo: 15
6, SNP: 111, loss: 30.086, oob acc: 82.14%, # of haplo: 15
7, SNP: 130, loss: 15.3177, oob acc: 89.29%, # of haplo: 19
8, SNP: 229, loss: 9.99758, oob acc: 89.29%, # of haplo: 28
9, SNP: 185, loss: 7.40712, oob acc: 89.29%, # of haplo: 29
10, SNP: 199, loss: 6.21341, oob acc: 89.29%, # of haplo: 29
11, SNP: 217, loss: 1.38739, oob acc: 89.29%, # of haplo: 30
[3] 2021-10-15 00:23:29, oob acc: 89.29%, # of SNPs: 11, # of haplo: 30
=== building individual classifier 4, out-of-bag (13/38.2%) ===
1, SNP: 101, loss: 154.355, oob acc: 46.15%, # of haplo: 16
2, SNP: 102, loss: 139.148, oob acc: 61.54%, # of haplo: 22
3, SNP: 132, loss: 95.2502, oob acc: 73.08%, # of haplo: 23
4, SNP: 147, loss: 76.9692, oob acc: 76.92%, # of haplo: 34
5, SNP: 53, loss: 68.3851, oob acc: 88.46%, # of haplo: 51
6, SNP: 186, loss: 41.8787, oob acc: 88.46%, # of haplo: 53
7, SNP: 128, loss: 33.5437, oob acc: 92.31%, # of haplo: 53
8, SNP: 14, loss: 23.3103, oob acc: 92.31%, # of haplo: 55
9, SNP: 219, loss: 18.3628, oob acc: 92.31%, # of haplo: 57
10, SNP: 149, loss: 17.9413, oob acc: 92.31%, # of haplo: 89
11, SNP: 73, loss: 16.3172, oob acc: 92.31%, # of haplo: 90
12, SNP: 70, loss: 16.1056, oob acc: 92.31%, # of haplo: 90
13, SNP: 199, loss: 12.3057, oob acc: 92.31%, # of haplo: 90
14, SNP: 203, loss: 12.2013, oob acc: 92.31%, # of haplo: 90
15, SNP: 151, loss: 11.1795, oob acc: 92.31%, # of haplo: 90
[4] 2021-10-15 00:23:29, oob acc: 92.31%, # of SNPs: 15, # of haplo: 90
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Accuracy with training data: 97.06%
Out-of-bag accuracy: 92.87%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 49
avg. # of SNPs in an individual classifier: 13.25
(sd: 1.71, min: 11, max: 15, median: 13.50)
avg. # of haplotypes in an individual classifier: 47.25
(sd: 28.72, min: 30, max: 90, median: 34.50)
avg. out-of-bag accuracy: 92.87%
(sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:29) 0%
Predicting (2021-10-15 00:23:29) 100%
Allele Num. Freq. Num. Freq. CR ACC SEN SPE PPV NPV Miscall
Train Train Valid. Valid. (%) (%) (%) (%) (%) (%) (%)
----
Overall accuracy: 88.5%, Call rate: 100.0%
01:01 13 0.1912 12 0.2308 100.0 96.2 100.0 95.0 85.7 100.0 --
02:01 25 0.3676 18 0.3462 100.0 98.1 94.4 100.0 100.0 97.1 01:01 (100)
02:06 1 0.0147 0 0 -- -- -- -- -- -- --
03:01 4 0.0588 5 0.0962 100.0 98.1 100.0 97.9 83.3 100.0 --
11:01 2 0.0294 3 0.0577 100.0 100.0 100.0 100.0 100.0 100.0 --
23:01 1 0.0147 2 0.0385 100.0 96.2 0.0 100.0 -- 96.2 24:02 (100)
24:02 6 0.0882 5 0.0962 100.0 92.3 60.0 95.7 60.0 95.7 01:01 (50)
24:03 1 0.0147 0 0 -- -- -- -- -- -- --
25:01 4 0.0588 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
26:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
29:02 3 0.0441 1 0.0192 100.0 98.1 0.0 100.0 -- 98.1 03:01 (50)
31:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
32:01 2 0.0294 2 0.0385 100.0 100.0 100.0 100.0 100.0 100.0 --
68:01 2 0.0294 1 0.0192 100.0 98.1 100.0 98.0 50.0 100.0 --
\title{Imputation Evaluation}
\documentclass[12pt]{article}
\usepackage{fullpage}
\usepackage{longtable}
\begin{document}
\maketitle
\setlength{\LTcapwidth}{6.5in}
% -------- BEGIN TABLE --------
\begin{longtable}{rrrrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{12}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{12}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{12}{l}{\it Overall accuracy: 88.5\%, Call rate: 100.0\%} \\
01:01 & 13 & 0.1912 & 12 & 0.2308 & 100.0 & 96.2 & 100.0 & 95.0 & 85.7 & 100.0 & -- \\
02:01 & 25 & 0.3676 & 18 & 0.3462 & 100.0 & 98.1 & 94.4 & 100.0 & 100.0 & 97.1 & 01:01 (100) \\
02:06 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
03:01 & 4 & 0.0588 & 5 & 0.0962 & 100.0 & 98.1 & 100.0 & 97.9 & 83.3 & 100.0 & -- \\
11:01 & 2 & 0.0294 & 3 & 0.0577 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
23:01 & 1 & 0.0147 & 2 & 0.0385 & 100.0 & 96.2 & 0.0 & 100.0 & -- & 96.2 & 24:02 (100) \\
24:02 & 6 & 0.0882 & 5 & 0.0962 & 100.0 & 92.3 & 60.0 & 95.7 & 60.0 & 95.7 & 01:01 (50) \\
24:03 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
25:01 & 4 & 0.0588 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
26:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
29:02 & 3 & 0.0441 & 1 & 0.0192 & 100.0 & 98.1 & 0.0 & 100.0 & -- & 98.1 & 03:01 (50) \\
31:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
32:01 & 2 & 0.0294 & 2 & 0.0385 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 100.0 & 98.0 & 50.0 & 100.0 & -- \\
\end{longtable}
% -------- END TABLE --------
\end{document}
<!DOCTYPE html>
<html>
<head>
<title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1" CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Train</th> <th>Freq. Train</th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="12">
<i> Overall accuracy: 88.5%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>13</td> <td>0.1912</td> <td>12</td> <td>0.2308</td> <td>100.0</td> <td>96.2</td> <td>100.0</td> <td>95.0</td> <td>85.7</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>25</td> <td>0.3676</td> <td>18</td> <td>0.3462</td> <td>100.0</td> <td>98.1</td> <td>94.4</td> <td>100.0</td> <td>100.0</td> <td>97.1</td> <td>01:01 (100)</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>03:01</td> <td>4</td> <td>0.0588</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>97.9</td> <td>83.3</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>11:01</td> <td>2</td> <td>0.0294</td> <td>3</td> <td>0.0577</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>1</td> <td>0.0147</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>96.2</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>96.2</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>24:02</td> <td>6</td> <td>0.0882</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>92.3</td> <td>60.0</td> <td>95.7</td> <td>60.0</td> <td>95.7</td> <td>01:01 (50)</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>25:01</td> <td>4</td> <td>0.0588</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>26:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>29:02</td> <td>3</td> <td>0.0441</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>98.1</td> <td>03:01 (50)</td>
</tr>
<tr>
<td>31:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>32:01</td> <td>2</td> <td>0.0294</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>98.0</td> <td>50.0</td> <td>100.0</td> <td>--</td>
</tr>
</table>
</body>
</html>
**Overall accuracy: 88.5%, Call rate: 100.0%**
| Allele | # Train | Freq. Train | # Valid. | Freq. Valid. | CR (%) | ACC (%) | SEN (%) | SPE (%) | PPV (%) | NPV (%) | Miscall (%) |
|:--|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|:--|
| 01:01 | 13 | 0.1912 | 12 | 0.2308 | 100.0 | 96.2 | 100.0 | 95.0 | 85.7 | 100.0 | -- |
| 02:01 | 25 | 0.3676 | 18 | 0.3462 | 100.0 | 98.1 | 94.4 | 100.0 | 100.0 | 97.1 | 01:01 (100) |
| 02:06 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 03:01 | 4 | 0.0588 | 5 | 0.0962 | 100.0 | 98.1 | 100.0 | 97.9 | 83.3 | 100.0 | -- |
| 11:01 | 2 | 0.0294 | 3 | 0.0577 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 23:01 | 1 | 0.0147 | 2 | 0.0385 | 100.0 | 96.2 | 0.0 | 100.0 | -- | 96.2 | 24:02 (100) |
| 24:02 | 6 | 0.0882 | 5 | 0.0962 | 100.0 | 92.3 | 60.0 | 95.7 | 60.0 | 95.7 | 01:01 (50) |
| 24:03 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 25:01 | 4 | 0.0588 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 26:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 29:02 | 3 | 0.0441 | 1 | 0.0192 | 100.0 | 98.1 | 0.0 | 100.0 | -- | 98.1 | 03:01 (50) |
| 31:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 32:01 | 2 | 0.0294 | 2 | 0.0385 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 68:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 98.1 | 100.0 | 98.0 | 50.0 | 100.0 | -- |
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:29
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 235, loss: 208.912, oob acc: 59.09%, # of haplo: 14
2, SNP: 136, loss: 139.316, oob acc: 63.64%, # of haplo: 14
3, SNP: 126, loss: 96.148, oob acc: 72.73%, # of haplo: 14
4, SNP: 89, loss: 76.2917, oob acc: 77.27%, # of haplo: 15
5, SNP: 102, loss: 61.3783, oob acc: 86.36%, # of haplo: 15
6, SNP: 105, loss: 49.1567, oob acc: 90.91%, # of haplo: 15
7, SNP: 117, loss: 43.0927, oob acc: 95.45%, # of haplo: 15
8, SNP: 259, loss: 26.6243, oob acc: 95.45%, # of haplo: 17
9, SNP: 60, loss: 17.6253, oob acc: 95.45%, # of haplo: 19
10, SNP: 236, loss: 9.50329, oob acc: 95.45%, # of haplo: 20
11, SNP: 94, loss: 7.27191, oob acc: 95.45%, # of haplo: 21
12, SNP: 58, loss: 6.70503, oob acc: 95.45%, # of haplo: 27
13, SNP: 243, loss: 2.87079, oob acc: 95.45%, # of haplo: 30
14, SNP: 5, loss: 2.77321, oob acc: 95.45%, # of haplo: 31
[1] 2021-10-15 00:23:29, oob acc: 95.45%, # of SNPs: 14, # of haplo: 31
=== building individual classifier 2, out-of-bag (9/26.5%) ===
1, SNP: 149, loss: 171.797, oob acc: 66.67%, # of haplo: 13
2, SNP: 176, loss: 120.459, oob acc: 72.22%, # of haplo: 14
3, SNP: 97, loss: 80.1731, oob acc: 83.33%, # of haplo: 14
4, SNP: 56, loss: 51.5193, oob acc: 94.44%, # of haplo: 16
5, SNP: 182, loss: 34.5643, oob acc: 94.44%, # of haplo: 18
6, SNP: 121, loss: 23.0259, oob acc: 94.44%, # of haplo: 18
7, SNP: 234, loss: 15.0596, oob acc: 94.44%, # of haplo: 20
8, SNP: 148, loss: 9.66757, oob acc: 94.44%, # of haplo: 20
9, SNP: 19, loss: 4.29975, oob acc: 94.44%, # of haplo: 27
10, SNP: 226, loss: 0.481093, oob acc: 94.44%, # of haplo: 27
11, SNP: 64, loss: 0.447483, oob acc: 94.44%, # of haplo: 28
12, SNP: 240, loss: 0.365545, oob acc: 94.44%, # of haplo: 37
13, SNP: 57, loss: 0.365132, oob acc: 94.44%, # of haplo: 38
[2] 2021-10-15 00:23:29, oob acc: 94.44%, # of SNPs: 13, # of haplo: 38
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 118, loss: 190.304, oob acc: 53.57%, # of haplo: 13
2, SNP: 175, loss: 157.208, oob acc: 60.71%, # of haplo: 15
3, SNP: 103, loss: 128.429, oob acc: 64.29%, # of haplo: 15
4, SNP: 182, loss: 66.6054, oob acc: 71.43%, # of haplo: 15
5, SNP: 152, loss: 58.8041, oob acc: 78.57%, # of haplo: 15
6, SNP: 111, loss: 30.086, oob acc: 82.14%, # of haplo: 15
7, SNP: 130, loss: 15.3177, oob acc: 89.29%, # of haplo: 19
8, SNP: 229, loss: 9.99758, oob acc: 89.29%, # of haplo: 28
9, SNP: 185, loss: 7.40712, oob acc: 89.29%, # of haplo: 29
10, SNP: 199, loss: 6.21341, oob acc: 89.29%, # of haplo: 29
11, SNP: 217, loss: 1.38739, oob acc: 89.29%, # of haplo: 30
[3] 2021-10-15 00:23:29, oob acc: 89.29%, # of SNPs: 11, # of haplo: 30
=== building individual classifier 4, out-of-bag (13/38.2%) ===
1, SNP: 101, loss: 154.355, oob acc: 46.15%, # of haplo: 16
2, SNP: 102, loss: 139.148, oob acc: 61.54%, # of haplo: 22
3, SNP: 132, loss: 95.2502, oob acc: 73.08%, # of haplo: 23
4, SNP: 147, loss: 76.9692, oob acc: 76.92%, # of haplo: 34
5, SNP: 53, loss: 68.3851, oob acc: 88.46%, # of haplo: 51
6, SNP: 186, loss: 41.8787, oob acc: 88.46%, # of haplo: 53
7, SNP: 128, loss: 33.5437, oob acc: 92.31%, # of haplo: 53
8, SNP: 14, loss: 23.3103, oob acc: 92.31%, # of haplo: 55
9, SNP: 219, loss: 18.3628, oob acc: 92.31%, # of haplo: 57
10, SNP: 149, loss: 17.9413, oob acc: 92.31%, # of haplo: 89
11, SNP: 73, loss: 16.3172, oob acc: 92.31%, # of haplo: 90
12, SNP: 70, loss: 16.1056, oob acc: 92.31%, # of haplo: 90
13, SNP: 199, loss: 12.3057, oob acc: 92.31%, # of haplo: 90
14, SNP: 203, loss: 12.2013, oob acc: 92.31%, # of haplo: 90
15, SNP: 151, loss: 11.1795, oob acc: 92.31%, # of haplo: 90
[4] 2021-10-15 00:23:29, oob acc: 92.31%, # of SNPs: 15, # of haplo: 90
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Accuracy with training data: 97.06%
Out-of-bag accuracy: 92.87%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 49
avg. # of SNPs in an individual classifier: 13.25
(sd: 1.71, min: 11, max: 15, median: 13.50)
avg. # of haplotypes in an individual classifier: 47.25
(sd: 28.72, min: 30, max: 90, median: 34.50)
avg. out-of-bag accuracy: 92.87%
(sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:29) 0%
Predicting (2021-10-15 00:23:29) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 8
# of SNPs: 51
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:30
=== building individual classifier 1, out-of-bag (24/40.0%) ===
1, SNP: 13, loss: 391.274, oob acc: 41.67%, # of haplo: 17
2, SNP: 2, loss: 321.685, oob acc: 52.08%, # of haplo: 18
3, SNP: 36, loss: 232.846, oob acc: 58.33%, # of haplo: 19
4, SNP: 28, loss: 178.077, oob acc: 62.50%, # of haplo: 20
5, SNP: 35, loss: 107.151, oob acc: 68.75%, # of haplo: 20
6, SNP: 3, loss: 72.2736, oob acc: 72.92%, # of haplo: 23
7, SNP: 19, loss: 50.8439, oob acc: 77.08%, # of haplo: 25
8, SNP: 4, loss: 47.2744, oob acc: 83.33%, # of haplo: 29
9, SNP: 42, loss: 47.0092, oob acc: 85.42%, # of haplo: 37
10, SNP: 33, loss: 41.5486, oob acc: 85.42%, # of haplo: 41
11, SNP: 5, loss: 39.769, oob acc: 85.42%, # of haplo: 51
12, SNP: 10, loss: 34.0977, oob acc: 85.42%, # of haplo: 51
13, SNP: 37, loss: 32.3969, oob acc: 85.42%, # of haplo: 52
14, SNP: 7, loss: 28.1492, oob acc: 85.42%, # of haplo: 52
15, SNP: 15, loss: 27.2163, oob acc: 85.42%, # of haplo: 55
[1] 2021-10-15 00:23:30, oob acc: 85.42%, # of SNPs: 15, # of haplo: 55
=== building individual classifier 2, out-of-bag (17/28.3%) ===
1, SNP: 18, loss: 453.852, oob acc: 44.12%, # of haplo: 17
2, SNP: 4, loss: 358.517, oob acc: 50.00%, # of haplo: 18
3, SNP: 49, loss: 258.495, oob acc: 52.94%, # of haplo: 18
4, SNP: 5, loss: 172.555, oob acc: 67.65%, # of haplo: 21
5, SNP: 42, loss: 144.905, oob acc: 76.47%, # of haplo: 21
6, SNP: 38, loss: 98.7462, oob acc: 79.41%, # of haplo: 21
7, SNP: 36, loss: 83.4743, oob acc: 82.35%, # of haplo: 24
8, SNP: 19, loss: 60.2385, oob acc: 88.24%, # of haplo: 24
9, SNP: 46, loss: 49.1775, oob acc: 88.24%, # of haplo: 24
10, SNP: 20, loss: 42.3205, oob acc: 88.24%, # of haplo: 24
11, SNP: 12, loss: 41.1299, oob acc: 91.18%, # of haplo: 25
12, SNP: 1, loss: 33.8332, oob acc: 91.18%, # of haplo: 25
13, SNP: 37, loss: 32.8313, oob acc: 91.18%, # of haplo: 26
14, SNP: 7, loss: 38.8398, oob acc: 94.12%, # of haplo: 27
15, SNP: 15, loss: 35.0817, oob acc: 94.12%, # of haplo: 32
16, SNP: 39, loss: 33.7063, oob acc: 94.12%, # of haplo: 30
[2] 2021-10-15 00:23:30, oob acc: 94.12%, # of SNPs: 16, # of haplo: 30
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
3.464062e-10 4.686489e-10 1.568833e-09 3.197938e-03 1.266674e-02 3.773631e-02
Max. Mean SD
9.739941e-02 2.429599e-02 2.696412e-02
Accuracy with training data: 95.83%
Out-of-bag accuracy: 89.77%
Gene: HLA-C
Training dataset: 60 samples X 51 SNPs
# of HLA alleles: 17
# of individual classifiers: 2
total # of SNPs used: 23
avg. # of SNPs in an individual classifier: 15.50
(sd: 0.71, min: 15, max: 16, median: 15.50)
avg. # of haplotypes in an individual classifier: 42.50
(sd: 17.68, min: 30, max: 55, median: 42.50)
avg. out-of-bag accuracy: 89.77%
(sd: 6.15%, min: 85.42%, max: 94.12%, median: 89.77%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
3.464062e-10 4.686489e-10 1.568833e-09 3.197938e-03 1.266674e-02 3.773631e-02
Max. Mean SD
9.739941e-02 2.429599e-02 2.696412e-02
Genome assembly: hg19
Gene: HLA-C
Training dataset: 60 samples X 51 SNPs
# of HLA alleles: 17
# of individual classifiers: 1
total # of SNPs used: 15
avg. # of SNPs in an individual classifier: 15.00
(sd: NA, min: 15, max: 15, median: 15.00)
avg. # of haplotypes in an individual classifier: 55.00
(sd: NA, min: 55, max: 55, median: 55.00)
avg. out-of-bag accuracy: 85.42%
(sd: NA%, min: 85.42%, max: 85.42%, median: 85.42%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
3.464062e-10 4.686489e-10 1.568833e-09 3.197938e-03 1.266674e-02 3.773631e-02
Max. Mean SD
9.739941e-02 2.429599e-02 2.696412e-02
Genome assembly: hg19
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:30
=== building individual classifier 1, out-of-bag (24/40.0%) ===
1, SNP: 44, loss: 396.506, oob acc: 41.67%, # of haplo: 17
2, SNP: 58, loss: 291.148, oob acc: 50.00%, # of haplo: 17
3, SNP: 80, loss: 211.469, oob acc: 56.25%, # of haplo: 20
4, SNP: 18, loss: 138.615, oob acc: 60.42%, # of haplo: 20
5, SNP: 29, loss: 111.977, oob acc: 62.50%, # of haplo: 22
6, SNP: 62, loss: 90.976, oob acc: 68.75%, # of haplo: 24
7, SNP: 13, loss: 70.2962, oob acc: 72.92%, # of haplo: 24
8, SNP: 14, loss: 54.5685, oob acc: 77.08%, # of haplo: 22
9, SNP: 72, loss: 35.1951, oob acc: 77.08%, # of haplo: 24
10, SNP: 3, loss: 23.2868, oob acc: 79.17%, # of haplo: 24
11, SNP: 70, loss: 21.089, oob acc: 79.17%, # of haplo: 28
12, SNP: 5, loss: 20.9664, oob acc: 79.17%, # of haplo: 29
13, SNP: 40, loss: 20.9662, oob acc: 83.33%, # of haplo: 37
14, SNP: 24, loss: 20.2385, oob acc: 85.42%, # of haplo: 38
15, SNP: 2, loss: 20.2383, oob acc: 87.50%, # of haplo: 39
16, SNP: 74, loss: 20.0601, oob acc: 87.50%, # of haplo: 40
17, SNP: 57, loss: 17.8846, oob acc: 87.50%, # of haplo: 41
18, SNP: 56, loss: 14.7377, oob acc: 89.58%, # of haplo: 43
19, SNP: 27, loss: 10.7709, oob acc: 89.58%, # of haplo: 43
[1] 2021-10-15 00:23:30, oob acc: 89.58%, # of SNPs: 19, # of haplo: 43
=== building individual classifier 2, out-of-bag (17/28.3%) ===
1, SNP: 66, loss: 434.369, oob acc: 44.12%, # of haplo: 19
2, SNP: 28, loss: 337.451, oob acc: 58.82%, # of haplo: 21
3, SNP: 30, loss: 302.194, oob acc: 73.53%, # of haplo: 21
4, SNP: 59, loss: 209.932, oob acc: 73.53%, # of haplo: 21
5, SNP: 69, loss: 146.631, oob acc: 82.35%, # of haplo: 21
6, SNP: 73, loss: 96.4111, oob acc: 91.18%, # of haplo: 21
7, SNP: 70, loss: 81.5466, oob acc: 91.18%, # of haplo: 21
8, SNP: 3, loss: 71.8294, oob acc: 91.18%, # of haplo: 22
9, SNP: 5, loss: 66.5825, oob acc: 94.12%, # of haplo: 23
10, SNP: 27, loss: 46.6959, oob acc: 94.12%, # of haplo: 23
11, SNP: 72, loss: 39.0572, oob acc: 94.12%, # of haplo: 23
12, SNP: 6, loss: 35.0674, oob acc: 94.12%, # of haplo: 24
13, SNP: 78, loss: 34.8741, oob acc: 94.12%, # of haplo: 32
14, SNP: 82, loss: 33.4558, oob acc: 94.12%, # of haplo: 38
15, SNP: 57, loss: 30.709, oob acc: 94.12%, # of haplo: 41
16, SNP: 32, loss: 26.6513, oob acc: 94.12%, # of haplo: 42
17, SNP: 23, loss: 26.6236, oob acc: 94.12%, # of haplo: 46
18, SNP: 2, loss: 25.7938, oob acc: 94.12%, # of haplo: 56
[2] 2021-10-15 00:23:30, oob acc: 94.12%, # of SNPs: 18, # of haplo: 56
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.736136e-08 5.138167e-06 5.122542e-05 1.574056e-03 6.457411e-03 3.142989e-02
Max. Mean SD
8.812257e-02 1.848522e-02 2.222954e-02
Accuracy with training data: 96.67%
Out-of-bag accuracy: 91.85%
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:30
=== building individual classifier 1, out-of-bag (24/40.0%) ===
1, SNP: 44, loss: 396.506, oob acc: 41.67%, # of haplo: 17
2, SNP: 58, loss: 291.148, oob acc: 50.00%, # of haplo: 17
3, SNP: 80, loss: 211.469, oob acc: 56.25%, # of haplo: 20
4, SNP: 18, loss: 138.615, oob acc: 60.42%, # of haplo: 20
5, SNP: 29, loss: 111.977, oob acc: 62.50%, # of haplo: 22
6, SNP: 62, loss: 90.976, oob acc: 68.75%, # of haplo: 24
7, SNP: 13, loss: 70.2962, oob acc: 72.92%, # of haplo: 24
8, SNP: 14, loss: 54.5685, oob acc: 77.08%, # of haplo: 22
9, SNP: 72, loss: 35.1951, oob acc: 77.08%, # of haplo: 24
10, SNP: 3, loss: 23.2868, oob acc: 79.17%, # of haplo: 24
11, SNP: 70, loss: 21.089, oob acc: 79.17%, # of haplo: 28
12, SNP: 5, loss: 20.9664, oob acc: 79.17%, # of haplo: 29
13, SNP: 40, loss: 20.9662, oob acc: 83.33%, # of haplo: 37
14, SNP: 24, loss: 20.2385, oob acc: 85.42%, # of haplo: 38
15, SNP: 2, loss: 20.2383, oob acc: 87.50%, # of haplo: 39
16, SNP: 74, loss: 20.0601, oob acc: 87.50%, # of haplo: 40
17, SNP: 57, loss: 17.8846, oob acc: 87.50%, # of haplo: 41
18, SNP: 56, loss: 14.7377, oob acc: 89.58%, # of haplo: 43
19, SNP: 27, loss: 10.7709, oob acc: 89.58%, # of haplo: 43
[1] 2021-10-15 00:23:30, oob acc: 89.58%, # of SNPs: 19, # of haplo: 43
=== building individual classifier 2, out-of-bag (17/28.3%) ===
1, SNP: 66, loss: 434.369, oob acc: 44.12%, # of haplo: 19
2, SNP: 28, loss: 337.451, oob acc: 58.82%, # of haplo: 21
3, SNP: 30, loss: 302.194, oob acc: 73.53%, # of haplo: 21
4, SNP: 59, loss: 209.932, oob acc: 73.53%, # of haplo: 21
5, SNP: 69, loss: 146.631, oob acc: 82.35%, # of haplo: 21
6, SNP: 73, loss: 96.4111, oob acc: 91.18%, # of haplo: 21
7, SNP: 70, loss: 81.5466, oob acc: 91.18%, # of haplo: 21
8, SNP: 3, loss: 71.8294, oob acc: 91.18%, # of haplo: 22
9, SNP: 5, loss: 66.5825, oob acc: 94.12%, # of haplo: 23
10, SNP: 27, loss: 46.6959, oob acc: 94.12%, # of haplo: 23
11, SNP: 72, loss: 39.0572, oob acc: 94.12%, # of haplo: 23
12, SNP: 6, loss: 35.0674, oob acc: 94.12%, # of haplo: 24
13, SNP: 78, loss: 34.8741, oob acc: 94.12%, # of haplo: 32
14, SNP: 82, loss: 33.4558, oob acc: 94.12%, # of haplo: 38
15, SNP: 57, loss: 30.709, oob acc: 94.12%, # of haplo: 41
16, SNP: 32, loss: 26.6513, oob acc: 94.12%, # of haplo: 42
17, SNP: 23, loss: 26.6236, oob acc: 94.12%, # of haplo: 46
18, SNP: 2, loss: 25.7938, oob acc: 94.12%, # of haplo: 56
[2] 2021-10-15 00:23:30, oob acc: 94.12%, # of SNPs: 18, # of haplo: 56
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.736136e-08 5.138167e-06 5.122542e-05 1.574056e-03 6.457411e-03 3.142989e-02
Max. Mean SD
8.812257e-02 1.848522e-02 2.222954e-02
Accuracy with training data: 96.67%
Out-of-bag accuracy: 91.85%
Gene: HLA-C
Training dataset: 60 samples X 83 SNPs
# of HLA alleles: 17
# of individual classifiers: 2
total # of SNPs used: 30
avg. # of SNPs in an individual classifier: 18.50
(sd: 0.71, min: 18, max: 19, median: 18.50)
avg. # of haplotypes in an individual classifier: 49.50
(sd: 9.19, min: 43, max: 56, median: 49.50)
avg. out-of-bag accuracy: 91.85%
(sd: 3.21%, min: 89.58%, max: 94.12%, median: 91.85%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.736136e-08 5.138167e-06 5.122542e-05 1.574056e-03 6.457411e-03 3.142989e-02
Max. Mean SD
8.812257e-02 1.848522e-02 2.222954e-02
Genome assembly: hg19
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
>
> proc.time()
user system elapsed
19.89 0.45 21.70
|
HIBAG.Rcheck/tests_x64/runTests.Rout
R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
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> #############################################################
> #
> # DESCRIPTION: Unit tests in the HIBAG package
> #
>
> # load the HIBAG package
> library(HIBAG)
HIBAG (HLA Genotype Imputation with Attribute Bagging)
Kernel Version: v1.5 (32-bit, AVX2)
>
>
> #############################################################
>
> # a list of HLA genes
> hla.list <- c("A", "B", "C", "DQA1", "DQB1", "DRB1")
>
> # pre-defined lower bound of prediction accuracy
> hla.acc <- c(0.9, 0.8, 0.8, 0.8, 0.8, 0.7)
>
>
> for (hla.idx in seq_along(hla.list))
+ {
+ hla.id <- hla.list[hla.idx]
+
+ # make a "hlaAlleleClass" object
+ hla <- hlaAllele(HLA_Type_Table$sample.id,
+ H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")],
+ H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")],
+ locus=hla.id, assembly="hg19")
+
+ # divide HLA types randomly
+ set.seed(100)
+ hlatab <- hlaSplitAllele(hla, train.prop=0.5)
+
+ # SNP predictors within the flanking region on each side
+ region <- 500 # kb
+ snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id,
+ HapMap_CEU_Geno$snp.position,
+ hla.id, region*1000, assembly="hg19")
+
+ # training and validation genotypes
+ train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
+ snp.sel=match(snpid, HapMap_CEU_Geno$snp.id),
+ samp.sel=match(hlatab$training$value$sample.id,
+ HapMap_CEU_Geno$sample.id))
+ test.geno <- hlaGenoSubset(HapMap_CEU_Geno,
+ samp.sel=match(hlatab$validation$value$sample.id,
+ HapMap_CEU_Geno$sample.id))
+
+
+ # train a HIBAG model
+ set.seed(100)
+ model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=10)
+ summary(model)
+
+ # validation
+ pred <- hlaPredict(model, test.geno, type="response")
+ summary(pred)
+
+ # compare
+ comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
+ call.threshold=0)
+ print(comp$overall)
+
+ # check
+ if (comp$overall$acc.haplo < hla.acc[hla.idx])
+ stop("HLA - ", hla.id, ", 'acc.haplo' should be >= ", hla.acc[hla.idx], ".")
+
+ cat("\n\n")
+ }
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:32
=== building individual classifier 1, out-of-bag (11/32.4%) ===
[1] 2021-10-15 00:23:32, oob acc: 77.27%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
[2] 2021-10-15 00:23:32, oob acc: 88.46%, # of SNPs: 12, # of haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
[3] 2021-10-15 00:23:32, oob acc: 85.71%, # of SNPs: 11, # of haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
[4] 2021-10-15 00:23:32, oob acc: 75.00%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 5, out-of-bag (17/50.0%) ===
[5] 2021-10-15 00:23:32, oob acc: 79.41%, # of SNPs: 13, # of haplo: 34
=== building individual classifier 6, out-of-bag (11/32.4%) ===
[6] 2021-10-15 00:23:32, oob acc: 100.00%, # of SNPs: 19, # of haplo: 72
=== building individual classifier 7, out-of-bag (9/26.5%) ===
[7] 2021-10-15 00:23:32, oob acc: 100.00%, # of SNPs: 17, # of haplo: 37
=== building individual classifier 8, out-of-bag (13/38.2%) ===
[8] 2021-10-15 00:23:32, oob acc: 84.62%, # of SNPs: 14, # of haplo: 58
=== building individual classifier 9, out-of-bag (14/41.2%) ===
[9] 2021-10-15 00:23:32, oob acc: 89.29%, # of SNPs: 13, # of haplo: 34
=== building individual classifier 10, out-of-bag (13/38.2%) ===
[10] 2021-10-15 00:23:32, oob acc: 80.77%, # of SNPs: 14, # of haplo: 24
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004109150 0.0004156612 0.0004583766 0.0050417085 0.0096582452 0.0232052136
Max. Mean SD
0.4987174317 0.0470514279 0.1161981828
Accuracy with training data: 98.53%
Out-of-bag accuracy: 86.05%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 10
total # of SNPs used: 93
avg. # of SNPs in an individual classifier: 13.90
(sd: 2.38, min: 11, max: 19, median: 13.00)
avg. # of haplotypes in an individual classifier: 36.70
(sd: 17.93, min: 14, max: 72, median: 34.00)
avg. out-of-bag accuracy: 86.05%
(sd: 8.68%, min: 75.00%, max: 100.00%, median: 85.16%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004109150 0.0004156612 0.0004583766 0.0050417085 0.0096582452 0.0232052136
Max. Mean SD
0.4987174317 0.0470514279 0.1161981828
Genome assembly: hg19
HIBAG model for HLA-A:
10 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:32) 0%
Predicting (2021-10-15 00:23:32) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 3 (11.5%) 4 (15.4%) 18 (69.2%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.002746 0.006607 0.031587 0.023928 0.498717
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 25 51 0.9615385 0.9807692 0
n.call call.rate
1 26 1
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 1 monomorphic SNP
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 340
# of samples: 28
# of unique HLA alleles: 22
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:32
=== building individual classifier 1, out-of-bag (12/42.9%) ===
[1] 2021-10-15 00:23:32, oob acc: 58.33%, # of SNPs: 17, # of haplo: 52
=== building individual classifier 2, out-of-bag (11/39.3%) ===
[2] 2021-10-15 00:23:32, oob acc: 63.64%, # of SNPs: 18, # of haplo: 51
=== building individual classifier 3, out-of-bag (13/46.4%) ===
[3] 2021-10-15 00:23:32, oob acc: 50.00%, # of SNPs: 15, # of haplo: 29
=== building individual classifier 4, out-of-bag (11/39.3%) ===
[4] 2021-10-15 00:23:33, oob acc: 59.09%, # of SNPs: 12, # of haplo: 57
=== building individual classifier 5, out-of-bag (11/39.3%) ===
[5] 2021-10-15 00:23:33, oob acc: 63.64%, # of SNPs: 15, # of haplo: 86
=== building individual classifier 6, out-of-bag (12/42.9%) ===
[6] 2021-10-15 00:23:33, oob acc: 79.17%, # of SNPs: 18, # of haplo: 66
=== building individual classifier 7, out-of-bag (12/42.9%) ===
[7] 2021-10-15 00:23:33, oob acc: 70.83%, # of SNPs: 15, # of haplo: 86
=== building individual classifier 8, out-of-bag (9/32.1%) ===
[8] 2021-10-15 00:23:33, oob acc: 77.78%, # of SNPs: 16, # of haplo: 117
=== building individual classifier 9, out-of-bag (9/32.1%) ===
[9] 2021-10-15 00:23:33, oob acc: 77.78%, # of SNPs: 18, # of haplo: 92
=== building individual classifier 10, out-of-bag (9/32.1%) ===
[10] 2021-10-15 00:23:33, oob acc: 61.11%, # of SNPs: 15, # of haplo: 72
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
9.567411e-05 9.622439e-05 1.011769e-04 3.071775e-03 7.279682e-03 1.186415e-02
Max. Mean SD
1.196521e-01 1.281211e-02 2.267322e-02
Accuracy with training data: 100.00%
Out-of-bag accuracy: 66.14%
Gene: HLA-B
Training dataset: 28 samples X 340 SNPs
# of HLA alleles: 22
# of individual classifiers: 10
total # of SNPs used: 118
avg. # of SNPs in an individual classifier: 15.90
(sd: 1.91, min: 12, max: 18, median: 15.50)
avg. # of haplotypes in an individual classifier: 70.80
(sd: 25.28, min: 29, max: 117, median: 69.00)
avg. out-of-bag accuracy: 66.14%
(sd: 9.84%, min: 50.00%, max: 79.17%, median: 63.64%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
9.567411e-05 9.622439e-05 1.011769e-04 3.071775e-03 7.279682e-03 1.186415e-02
Max. Mean SD
1.196521e-01 1.281211e-02 2.267322e-02
Genome assembly: hg19
HIBAG model for HLA-B:
10 individual classifiers
340 SNPs
22 unique HLA alleles: 07:02, 08:01, 13:02, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 15
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:33) 0%
Predicting (2021-10-15 00:23:33) 100%
Gene: HLA-B
Range: [31321649bp, 31324989bp] on hg19
# of samples: 15
# of unique HLA alleles: 9
# of unique HLA genotypes: 12
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
3 (20.0%) 5 (33.3%) 3 (20.0%) 4 (26.7%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
2.000e-08 4.068e-05 2.934e-03 1.789e-02 6.076e-03 1.326e-01
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 15 11 25 0.7333333 0.8333333 0
n.call call.rate
1 15 1
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 2 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 354
# of samples: 36
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:33
=== building individual classifier 1, out-of-bag (13/36.1%) ===
[1] 2021-10-15 00:23:33, oob acc: 80.77%, # of SNPs: 19, # of haplo: 40
=== building individual classifier 2, out-of-bag (11/30.6%) ===
[2] 2021-10-15 00:23:33, oob acc: 90.91%, # of SNPs: 32, # of haplo: 32
=== building individual classifier 3, out-of-bag (14/38.9%) ===
[3] 2021-10-15 00:23:33, oob acc: 89.29%, # of SNPs: 19, # of haplo: 43
=== building individual classifier 4, out-of-bag (13/36.1%) ===
[4] 2021-10-15 00:23:34, oob acc: 84.62%, # of SNPs: 19, # of haplo: 72
=== building individual classifier 5, out-of-bag (10/27.8%) ===
[5] 2021-10-15 00:23:34, oob acc: 90.00%, # of SNPs: 19, # of haplo: 66
=== building individual classifier 6, out-of-bag (10/27.8%) ===
[6] 2021-10-15 00:23:34, oob acc: 95.00%, # of SNPs: 21, # of haplo: 59
=== building individual classifier 7, out-of-bag (16/44.4%) ===
[7] 2021-10-15 00:23:34, oob acc: 90.62%, # of SNPs: 18, # of haplo: 25
=== building individual classifier 8, out-of-bag (14/38.9%) ===
[8] 2021-10-15 00:23:34, oob acc: 89.29%, # of SNPs: 23, # of haplo: 57
=== building individual classifier 9, out-of-bag (13/36.1%) ===
[9] 2021-10-15 00:23:34, oob acc: 84.62%, # of SNPs: 18, # of haplo: 39
=== building individual classifier 10, out-of-bag (14/38.9%) ===
[10] 2021-10-15 00:23:34, oob acc: 89.29%, # of SNPs: 35, # of haplo: 62
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0007653736 0.0007682927 0.0007945643 0.0017428621 0.0044732464 0.0093175730
Max. Mean SD
0.0703539734 0.0088728477 0.0132051834
Accuracy with training data: 100.00%
Out-of-bag accuracy: 88.44%
Gene: HLA-C
Training dataset: 36 samples X 354 SNPs
# of HLA alleles: 17
# of individual classifiers: 10
total # of SNPs used: 135
avg. # of SNPs in an individual classifier: 22.30
(sd: 6.13, min: 18, max: 35, median: 19.00)
avg. # of haplotypes in an individual classifier: 49.50
(sd: 15.74, min: 25, max: 72, median: 50.00)
avg. out-of-bag accuracy: 88.44%
(sd: 4.04%, min: 80.77%, max: 95.00%, median: 89.29%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0007653736 0.0007682927 0.0007945643 0.0017428621 0.0044732464 0.0093175730
Max. Mean SD
0.0703539734 0.0088728477 0.0132051834
Genome assembly: hg19
HIBAG model for HLA-C:
10 individual classifiers
354 SNPs
17 unique HLA alleles: 01:02, 02:02, 03:03, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 24
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:34) 0%
Predicting (2021-10-15 00:23:34) 100%
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 24
# of unique HLA alleles: 14
# of unique HLA genotypes: 19
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
2 (8.3%) 3 (12.5%) 6 (25.0%) 13 (54.2%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0000000 0.0002058 0.0058893 0.0035911 0.0468290
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 24 16 39 0.6666667 0.8125 0
n.call call.rate
1 24 1
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 4 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 345
# of samples: 31
# of unique HLA alleles: 7
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:34
=== building individual classifier 1, out-of-bag (11/35.5%) ===
[1] 2021-10-15 00:23:34, oob acc: 95.45%, # of SNPs: 11, # of haplo: 22
=== building individual classifier 2, out-of-bag (11/35.5%) ===
[2] 2021-10-15 00:23:34, oob acc: 100.00%, # of SNPs: 13, # of haplo: 22
=== building individual classifier 3, out-of-bag (15/48.4%) ===
[3] 2021-10-15 00:23:34, oob acc: 83.33%, # of SNPs: 15, # of haplo: 23
=== building individual classifier 4, out-of-bag (14/45.2%) ===
[4] 2021-10-15 00:23:34, oob acc: 82.14%, # of SNPs: 8, # of haplo: 14
=== building individual classifier 5, out-of-bag (13/41.9%) ===
[5] 2021-10-15 00:23:34, oob acc: 88.46%, # of SNPs: 11, # of haplo: 34
=== building individual classifier 6, out-of-bag (10/32.3%) ===
[6] 2021-10-15 00:23:34, oob acc: 90.00%, # of SNPs: 11, # of haplo: 21
=== building individual classifier 7, out-of-bag (13/41.9%) ===
[7] 2021-10-15 00:23:34, oob acc: 92.31%, # of SNPs: 14, # of haplo: 23
=== building individual classifier 8, out-of-bag (13/41.9%) ===
[8] 2021-10-15 00:23:34, oob acc: 96.15%, # of SNPs: 11, # of haplo: 16
=== building individual classifier 9, out-of-bag (14/45.2%) ===
[9] 2021-10-15 00:23:34, oob acc: 89.29%, # of SNPs: 12, # of haplo: 19
=== building individual classifier 10, out-of-bag (11/35.5%) ===
[10] 2021-10-15 00:23:34, oob acc: 86.36%, # of SNPs: 8, # of haplo: 13
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.001972961 0.001998819 0.002231547 0.005363515 0.008831104 0.018431530
Max. Mean SD
0.537093886 0.028877632 0.094687228
Accuracy with training data: 96.77%
Out-of-bag accuracy: 90.35%
Gene: HLA-DQA1
Training dataset: 31 samples X 345 SNPs
# of HLA alleles: 7
# of individual classifiers: 10
total # of SNPs used: 80
avg. # of SNPs in an individual classifier: 11.40
(sd: 2.27, min: 8, max: 15, median: 11.00)
avg. # of haplotypes in an individual classifier: 20.70
(sd: 5.96, min: 13, max: 34, median: 21.50)
avg. out-of-bag accuracy: 90.35%
(sd: 5.72%, min: 82.14%, max: 100.00%, median: 89.64%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.001972961 0.001998819 0.002231547 0.005363515 0.008831104 0.018431530
Max. Mean SD
0.537093886 0.028877632 0.094687228
Genome assembly: hg19
HIBAG model for HLA-DQA1:
10 individual classifiers
345 SNPs
7 unique HLA alleles: 01:01, 01:02, 01:03, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 29
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:34) 0%
Predicting (2021-10-15 00:23:34) 100%
Gene: HLA-DQA1
Range: [32605169bp, 32612152bp] on hg19
# of samples: 29
# of unique HLA alleles: 6
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
5 (17.2%) 5 (17.2%) 2 (6.9%) 17 (58.6%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000001 0.0019253 0.0069908 0.0532601 0.0167536 0.5404845
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 29 21 49 0.7241379 0.8448276 0
n.call call.rate
1 29 1
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 6 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 350
# of samples: 34
# of unique HLA alleles: 12
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:34
=== building individual classifier 1, out-of-bag (11/32.4%) ===
[1] 2021-10-15 00:23:34, oob acc: 86.36%, # of SNPs: 13, # of haplo: 34
=== building individual classifier 2, out-of-bag (13/38.2%) ===
[2] 2021-10-15 00:23:34, oob acc: 76.92%, # of SNPs: 21, # of haplo: 42
=== building individual classifier 3, out-of-bag (13/38.2%) ===
[3] 2021-10-15 00:23:34, oob acc: 80.77%, # of SNPs: 10, # of haplo: 17
=== building individual classifier 4, out-of-bag (13/38.2%) ===
[4] 2021-10-15 00:23:35, oob acc: 92.31%, # of SNPs: 22, # of haplo: 78
=== building individual classifier 5, out-of-bag (13/38.2%) ===
[5] 2021-10-15 00:23:35, oob acc: 92.31%, # of SNPs: 11, # of haplo: 40
=== building individual classifier 6, out-of-bag (14/41.2%) ===
[6] 2021-10-15 00:23:35, oob acc: 71.43%, # of SNPs: 8, # of haplo: 22
=== building individual classifier 7, out-of-bag (14/41.2%) ===
[7] 2021-10-15 00:23:35, oob acc: 71.43%, # of SNPs: 14, # of haplo: 53
=== building individual classifier 8, out-of-bag (11/32.4%) ===
[8] 2021-10-15 00:23:35, oob acc: 86.36%, # of SNPs: 14, # of haplo: 40
=== building individual classifier 9, out-of-bag (14/41.2%) ===
[9] 2021-10-15 00:23:35, oob acc: 100.00%, # of SNPs: 16, # of haplo: 56
=== building individual classifier 10, out-of-bag (13/38.2%) ===
[10] 2021-10-15 00:23:35, oob acc: 88.46%, # of SNPs: 14, # of haplo: 34
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003282346 0.0003687353 0.0007332412 0.0038570393 0.0073528147 0.0148594626
Max. Mean SD
0.3073781820 0.0225078064 0.0573939534
Accuracy with training data: 98.53%
Out-of-bag accuracy: 84.64%
Gene: HLA-DQB1
Training dataset: 34 samples X 350 SNPs
# of HLA alleles: 12
# of individual classifiers: 10
total # of SNPs used: 99
avg. # of SNPs in an individual classifier: 14.30
(sd: 4.45, min: 8, max: 22, median: 14.00)
avg. # of haplotypes in an individual classifier: 41.60
(sd: 17.55, min: 17, max: 78, median: 40.00)
avg. out-of-bag accuracy: 84.64%
(sd: 9.41%, min: 71.43%, max: 100.00%, median: 86.36%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003282346 0.0003687353 0.0007332412 0.0038570393 0.0073528147 0.0148594626
Max. Mean SD
0.3073781820 0.0225078064 0.0573939534
Genome assembly: hg19
HIBAG model for HLA-DQB1:
10 individual classifiers
350 SNPs
12 unique HLA alleles: 02:01, 02:02, 03:01, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:35) 0%
Predicting (2021-10-15 00:23:35) 100%
Gene: HLA-DQB1
Range: [32627241bp, 32634466bp] on hg19
# of samples: 26
# of unique HLA alleles: 10
# of unique HLA genotypes: 17
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
3 (11.5%) 7 (26.9%) 5 (19.2%) 11 (42.3%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0002253 0.0018486 0.0308488 0.0099906 0.4023552
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 26 21 46 0.8076923 0.8846154 0
n.call call.rate
1 26 1
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 5 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 18
# of SNPs: 322
# of samples: 35
# of unique HLA alleles: 20
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:35
=== building individual classifier 1, out-of-bag (15/42.9%) ===
[1] 2021-10-15 00:23:35, oob acc: 70.00%, # of SNPs: 17, # of haplo: 77
=== building individual classifier 2, out-of-bag (16/45.7%) ===
[2] 2021-10-15 00:23:35, oob acc: 68.75%, # of SNPs: 22, # of haplo: 119
=== building individual classifier 3, out-of-bag (15/42.9%) ===
[3] 2021-10-15 00:23:35, oob acc: 73.33%, # of SNPs: 19, # of haplo: 33
=== building individual classifier 4, out-of-bag (13/37.1%) ===
[4] 2021-10-15 00:23:35, oob acc: 84.62%, # of SNPs: 18, # of haplo: 67
=== building individual classifier 5, out-of-bag (11/31.4%) ===
[5] 2021-10-15 00:23:36, oob acc: 86.36%, # of SNPs: 24, # of haplo: 127
=== building individual classifier 6, out-of-bag (12/34.3%) ===
[6] 2021-10-15 00:23:36, oob acc: 66.67%, # of SNPs: 18, # of haplo: 102
=== building individual classifier 7, out-of-bag (10/28.6%) ===
[7] 2021-10-15 00:23:36, oob acc: 75.00%, # of SNPs: 15, # of haplo: 71
=== building individual classifier 8, out-of-bag (15/42.9%) ===
[8] 2021-10-15 00:23:36, oob acc: 70.00%, # of SNPs: 15, # of haplo: 32
=== building individual classifier 9, out-of-bag (12/34.3%) ===
[9] 2021-10-15 00:23:36, oob acc: 91.67%, # of SNPs: 20, # of haplo: 93
=== building individual classifier 10, out-of-bag (15/42.9%) ===
[10] 2021-10-15 00:23:36, oob acc: 66.67%, # of SNPs: 15, # of haplo: 57
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.041155e-05 3.593240e-05 1.756200e-04 1.611725e-03 2.836751e-03 7.180633e-03
Max. Mean SD
4.558788e-01 4.152181e-02 1.239405e-01
Accuracy with training data: 94.29%
Out-of-bag accuracy: 75.31%
Gene: HLA-DRB1
Training dataset: 35 samples X 322 SNPs
# of HLA alleles: 20
# of individual classifiers: 10
total # of SNPs used: 129
avg. # of SNPs in an individual classifier: 18.30
(sd: 3.06, min: 15, max: 24, median: 18.00)
avg. # of haplotypes in an individual classifier: 77.80
(sd: 32.72, min: 32, max: 127, median: 74.00)
avg. out-of-bag accuracy: 75.31%
(sd: 9.00%, min: 66.67%, max: 91.67%, median: 71.67%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.041155e-05 3.593240e-05 1.756200e-04 1.611725e-03 2.836751e-03 7.180633e-03
Max. Mean SD
4.558788e-01 4.152181e-02 1.239405e-01
Genome assembly: hg19
HIBAG model for HLA-DRB1:
10 individual classifiers
322 SNPs
20 unique HLA alleles: 01:01, 01:03, 03:01, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 25
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:36) 0%
Predicting (2021-10-15 00:23:36) 100%
Gene: HLA-DRB1
Range: [32546546bp, 32557613bp] on hg19
# of samples: 25
# of unique HLA alleles: 10
# of unique HLA genotypes: 17
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
4 (16.0%) 5 (20.0%) 9 (36.0%) 7 (28.0%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0001451 0.0007388 0.0088345 0.0026166 0.1725407
total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1 25 16 40 0.64 0.8 0
n.call call.rate
1 25 1
>
>
>
> #############################################################
>
> {
+ function.list <- readRDS(
+ system.file("Meta", "Rd.rds", package="HIBAG"))$Name
+
+ sapply(function.list, FUN = function(func.name)
+ {
+ args <- list(
+ topic = func.name,
+ package = "HIBAG",
+ echo = FALSE,
+ verbose = FALSE,
+ ask = FALSE
+ )
+ suppressWarnings(do.call(example, args))
+ NULL
+ })
+ invisible()
+ }
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:36
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 211, loss: 196.4, oob acc: 54.55%, # of haplo: 13
2, SNP: 66, loss: 173.548, oob acc: 63.64%, # of haplo: 13
3, SNP: 177, loss: 136.352, oob acc: 68.18%, # of haplo: 13
4, SNP: 108, loss: 95.8359, oob acc: 72.73%, # of haplo: 13
5, SNP: 127, loss: 67.3216, oob acc: 77.27%, # of haplo: 13
6, SNP: 95, loss: 47.5888, oob acc: 77.27%, # of haplo: 13
7, SNP: 33, loss: 37.2631, oob acc: 77.27%, # of haplo: 16
8, SNP: 6, loss: 29.7419, oob acc: 77.27%, # of haplo: 18
9, SNP: 208, loss: 25.6913, oob acc: 77.27%, # of haplo: 19
10, SNP: 225, loss: 25.3087, oob acc: 77.27%, # of haplo: 21
11, SNP: 11, loss: 24.8356, oob acc: 77.27%, # of haplo: 23
12, SNP: 151, loss: 19.4134, oob acc: 77.27%, # of haplo: 23
13, SNP: 199, loss: 17.011, oob acc: 77.27%, # of haplo: 23
[1] 2021-10-15 00:23:36, oob acc: 77.27%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
1, SNP: 160, loss: 221.236, oob acc: 76.92%, # of haplo: 17
2, SNP: 145, loss: 173.538, oob acc: 80.77%, # of haplo: 23
3, SNP: 177, loss: 128.58, oob acc: 84.62%, # of haplo: 31
4, SNP: 111, loss: 79.6877, oob acc: 84.62%, # of haplo: 31
5, SNP: 207, loss: 52.5557, oob acc: 88.46%, # of haplo: 32
6, SNP: 245, loss: 41.8731, oob acc: 88.46%, # of haplo: 34
7, SNP: 230, loss: 31.7937, oob acc: 88.46%, # of haplo: 38
8, SNP: 151, loss: 20.4566, oob acc: 88.46%, # of haplo: 36
9, SNP: 14, loss: 19.5805, oob acc: 88.46%, # of haplo: 42
10, SNP: 132, loss: 19.5101, oob acc: 88.46%, # of haplo: 42
11, SNP: 221, loss: 19.485, oob acc: 88.46%, # of haplo: 44
12, SNP: 251, loss: 18.5695, oob acc: 88.46%, # of haplo: 48
[2] 2021-10-15 00:23:36, oob acc: 88.46%, # of SNPs: 12, # of haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 191, loss: 193.067, oob acc: 57.14%, # of haplo: 11
2, SNP: 264, loss: 150.427, oob acc: 64.29%, # of haplo: 12
3, SNP: 132, loss: 93.4067, oob acc: 67.86%, # of haplo: 12
4, SNP: 128, loss: 39.8353, oob acc: 71.43%, # of haplo: 12
5, SNP: 160, loss: 28.2998, oob acc: 75.00%, # of haplo: 12
6, SNP: 144, loss: 13.635, oob acc: 75.00%, # of haplo: 12
7, SNP: 111, loss: 6.04609, oob acc: 75.00%, # of haplo: 12
8, SNP: 40, loss: 6.04583, oob acc: 82.14%, # of haplo: 14
9, SNP: 141, loss: 6.04583, oob acc: 85.71%, # of haplo: 14
10, SNP: 73, loss: 2.9038, oob acc: 85.71%, # of haplo: 14
11, SNP: 199, loss: 2.20025, oob acc: 85.71%, # of haplo: 14
[3] 2021-10-15 00:23:36, oob acc: 85.71%, # of SNPs: 11, # of haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
1, SNP: 147, loss: 158.631, oob acc: 50.00%, # of haplo: 12
2, SNP: 152, loss: 140.375, oob acc: 55.00%, # of haplo: 13
3, SNP: 78, loss: 115.887, oob acc: 60.00%, # of haplo: 16
4, SNP: 115, loss: 77.8082, oob acc: 60.00%, # of haplo: 18
5, SNP: 148, loss: 62.6831, oob acc: 65.00%, # of haplo: 18
6, SNP: 13, loss: 46.5657, oob acc: 75.00%, # of haplo: 20
7, SNP: 109, loss: 31.0312, oob acc: 75.00%, # of haplo: 20
8, SNP: 176, loss: 22.5073, oob acc: 75.00%, # of haplo: 21
9, SNP: 145, loss: 20.9122, oob acc: 75.00%, # of haplo: 21
10, SNP: 128, loss: 20.6728, oob acc: 75.00%, # of haplo: 21
11, SNP: 73, loss: 14.6217, oob acc: 75.00%, # of haplo: 22
12, SNP: 151, loss: 10.2879, oob acc: 75.00%, # of haplo: 23
13, SNP: 199, loss: 8.74645, oob acc: 75.00%, # of haplo: 23
[4] 2021-10-15 00:23:37, oob acc: 75.00%, # of SNPs: 13, # of haplo: 23
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Accuracy with training data: 97.06%
Out-of-bag accuracy: 81.61%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 38
avg. # of SNPs in an individual classifier: 12.25
(sd: 0.96, min: 11, max: 13, median: 12.50)
avg. # of haplotypes in an individual classifier: 27.00
(sd: 14.63, min: 14, max: 48, median: 23.00)
avg. out-of-bag accuracy: 81.61%
(sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:37) 0%
Predicting (2021-10-15 00:23:37) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 4 (15.4%) 4 (15.4%) 17 (65.4%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.002097 0.007711 0.032190 0.028032 0.471142
Dosages:
$dosage - num [1:14, 1:26] 2.50e-01 2.16e-04 2.50e-06 7.31e-01 1.11e-14 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:14] "01:01" "02:01" "02:06" "03:01" ...
..$ : chr [1:26] "NA11881" "NA11992" "NA11994" "NA12249" ...
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:37) 0%
Predicting (2021-10-15 00:23:37) 100%
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed' (the individual-major mode)
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam'
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim'
Import 3932 SNPs within the xMHC region on chromosome 6
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 90
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:37) 0%
Predicting (2021-10-15 00:23:37) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
using the default genome assembly (assembly="hg19")
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 12
# of unique HLA genotypes: 28
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 100
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 32 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 40
# of SNPs: 1532
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:37
=== building individual classifier 1, out-of-bag (23/38.3%) ===
[1] 2021-10-15 00:23:37, oob acc: 78.26%, # of SNPs: 16, # of haplo: 93
=== building individual classifier 2, out-of-bag (24/40.0%) ===
[2] 2021-10-15 00:23:38, oob acc: 93.75%, # of SNPs: 21, # of haplo: 88
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.481068e-19 2.724336e-19 4.913754e-19 1.389214e-04 6.397680e-04 2.980482e-03
Max. Mean SD
1.226562e-01 7.012898e-03 2.176036e-02
Accuracy with training data: 98.33%
Out-of-bag accuracy: 86.01%
Gene: HLA-A
Training dataset: 60 samples X 1532 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 36
avg. # of SNPs in an individual classifier: 18.50
(sd: 3.54, min: 16, max: 21, median: 18.50)
avg. # of haplotypes in an individual classifier: 90.50
(sd: 3.54, min: 88, max: 93, median: 90.50)
avg. out-of-bag accuracy: 86.01%
(sd: 10.95%, min: 78.26%, max: 93.75%, median: 86.01%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.481068e-19 2.724336e-19 4.913754e-19 1.389214e-04 6.397680e-04 2.980482e-03
Max. Mean SD
1.226562e-01 7.012898e-03 2.176036e-02
Genome assembly: hg19
HIBAG model for HLA-A:
2 individual classifiers
1532 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 60
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:38) 0%
Predicting (2021-10-15 00:23:38) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 13
# of unique HLA genotypes: 28
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (1.7%) 10 (16.7%) 5 (8.3%) 44 (73.3%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0001389 0.0006398 0.0070129 0.0029805 0.1226562
Dosages:
$dosage - num [1:14, 1:60] 1.00 1.80e-10 7.81e-18 5.00e-06 1.25e-06 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:14] "01:01" "02:01" "02:06" "03:01" ...
..$ : chr [1:60] "NA11882" "NA11881" "NA11993" "NA11992" ...
Convert to dosage VCF format:
# of samples: 4
# of unique HLA alleles: 5
output: <connection>
##fileformat=VCFv4.0
##fileDate=20211015
##source=HIBAG
##FILTER=<ID=PASS,Description="All filters passed">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=DS,Number=1,Type=Float,Description="Dosage of HLA allele">
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA11882 NA11881 NA11993 NA11992
6 29911954 HLA-A*01:01 A P_0101 . PASS . GT:DS 1/0:1.0000e+00 0/0:5.1764e-14 0/0:2.3978e-11 1/0:1.0000e+00
6 29911954 HLA-A*02:01 A P_0201 . PASS . GT:DS 0/0:1.7996e-10 0/0:2.3569e-14 0/0:8.4571e-07 0/1:1.0000e+00
6 29911954 HLA-A*03:01 A P_0301 . PASS . GT:DS 0/0:5.0000e-06 1/0:9.9999e-01 0/0:3.8461e-01 0/0:1.0557e-16
6 29911954 HLA-A*26:01 A P_2601 . PASS . GT:DS 0/0:7.8140e-18 0/1:5.0000e-01 1/0:7.5000e-01 0/0:2.4148e-13
6 29911954 HLA-A*29:02 A P_2902 . PASS . GT:DS 0/1:5.0000e-01 0/0:1.1875e-35 0/1:5.0000e-01 0/0:5.7690e-34
dominant model:
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042*
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000
02:01 25 35 52.0 48.6 0.0000 1.000 1.000
02:06 59 1 50.8 0.0 0.0000 1.000 1.000
03:01 51 9 49.0 55.6 0.0000 1.000 1.000
11:01 55 5 50.9 40.0 0.0000 1.000 1.000
23:01 58 2 50.0 50.0 0.0000 1.000 1.000
24:03 59 1 50.8 0.0 0.0000 1.000 1.000
25:01 55 5 52.7 20.0 0.8727 0.350 0.353
26:01 57 3 52.6 0.0 1.4035 0.236 0.237
29:02 56 4 51.8 25.0 0.2679 0.605 0.612
31:01 57 3 49.1 66.7 0.0000 1.000 1.000
32:01 56 4 46.4 100.0 2.4107 0.121 0.112
68:01 57 3 52.6 0.0 1.4035 0.236 0.237
additive model:
[-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p
01:01 95 25 50.5 48.0 0.0000 1.000 1.000
02:01 77 43 48.1 53.5 0.1450 0.703 0.704
02:06 119 1 50.4 0.0 0.0000 1.000 1.000
03:01 111 9 49.5 55.6 0.0000 1.000 1.000
11:01 115 5 50.4 40.0 0.0000 1.000 1.000
23:01 117 3 50.4 33.3 0.0000 1.000 1.000
24:02 109 11 46.8 81.8 3.6030 0.058 0.053
24:03 119 1 50.4 0.0 0.0000 1.000 1.000
25:01 115 5 51.3 20.0 0.8348 0.361 0.364
26:01 117 3 51.3 0.0 1.3675 0.242 0.244
29:02 116 4 50.9 25.0 0.2586 0.611 0.619
31:01 117 3 49.6 66.7 0.0000 1.000 1.000
32:01 116 4 48.3 100.0 2.3276 0.127 0.119
68:01 117 3 51.3 0.0 1.3675 0.242 0.244
recessive model:
[-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p
01:01 59 1 50.8 0 0.000 1.000 1.000
02:01 52 8 46.2 75 1.298 0.255 0.254
02:06 60 0 50.0 . . . .
03:01 60 0 50.0 . . . .
11:01 60 0 50.0 . . . .
23:01 59 1 50.8 0 0.000 1.000 1.000
24:02 60 0 50.0 . . . .
24:03 60 0 50.0 . . . .
25:01 60 0 50.0 . . . .
26:01 60 0 50.0 . . . .
29:02 60 0 50.0 . . . .
31:01 60 0 50.0 . . . .
32:01 60 0 50.0 . . . .
68:01 60 0 50.0 . . . .
genotype model:
[-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042*
-----
01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000
02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271
02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000
11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000
23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000
24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353
26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612
31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000
32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112
68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
dominant model:
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p
01:01 36 24 -0.14684 -0.117427 0.909
02:01 25 35 -0.32331 -0.000618 0.190
02:06 59 1 -0.14024 0.170057 .
03:01 51 9 -0.05600 -0.583178 0.147
11:01 55 5 -0.19188 0.489815 0.287
23:01 58 2 -0.15400 0.413687 0.281
24:02 49 11 -0.10486 -0.269664 0.537
24:03 59 1 -0.11409 -1.373118 .
25:01 55 5 -0.12237 -0.274749 0.742
26:01 57 3 -0.12473 -0.331558 0.690
29:02 56 4 -0.13044 -0.199941 0.789
31:01 57 3 -0.10097 -0.783003 0.607
32:01 56 4 -0.07702 -0.947791 0.092
68:01 57 3 -0.16915 0.512457 0.196
genotype model:
[-/-] [-/h] [h/h] avg.[-/-] avg.[-/h] avg.[h/h] anova.p
01:01 36 23 1 -0.14684 -0.08833 -0.78655 0.784
02:01 25 27 8 -0.32331 -0.02341 0.07631 0.446
02:06 59 1 0 -0.14024 0.17006 . 0.756
03:01 51 9 0 -0.05600 -0.58318 . 0.138
11:01 55 5 0 -0.19188 0.48981 . 0.137
23:01 58 1 1 -0.15400 0.10762 0.71975 0.663
24:02 49 11 0 -0.10486 -0.26966 . 0.618
24:03 59 1 0 -0.11409 -1.37312 . 0.205
25:01 55 5 0 -0.12237 -0.27475 . 0.742
26:01 57 3 0 -0.12473 -0.33156 . 0.725
29:02 56 4 0 -0.13044 -0.19994 . 0.892
31:01 57 3 0 -0.10097 -0.78300 . 0.243
32:01 56 4 0 -0.07702 -0.94779 . 0.086
68:01 57 3 0 -0.16915 0.51246 . 0.243
Logistic regression (dominant model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.792e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -8.777e-16
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.372e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.624e-01
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.418e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 2.874e-15
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.495e+00
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.170e+00
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.282e-01
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.771e+01
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
h.2.5% h.97.5% h.pval
24:02 0.1585 3.4251 0.032*
-----
01:01 -1.0330 1.0330 1.000
02:01 -1.1643 0.8899 0.793
02:06 -2868.1268 2836.9268 0.991
03:01 -1.1624 1.6872 0.718
11:01 -2.3074 1.4237 0.643
23:01 -2.8192 2.8192 1.000
24:03 -2868.1268 2836.9268 0.991
25:01 -3.7498 0.7588 0.194
26:01 -2731.9621 2698.6192 0.990
29:02 -3.4931 1.1530 0.324
31:01 -1.7277 3.1842 0.561
32:01 -3859.2763 3894.6947 0.993
68:01 -2731.9621 2698.6192 0.990
Logistic regression (dominant model) with 60 individuals:
glm(case ~ h + pc1, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 1.793e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 -2.268e-04
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 -1.370e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 -1.562e+01
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 2.686e-01
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 -4.451e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 -3.062e-03
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 -1.560e+01
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 -1.501e+00
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.667e+01
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 -1.189e+00
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 7.289e-01
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 1.781e+01
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 -1.673e+01
h.2.5% h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02 0.1587 3.4264 0.032* 0.011111 -0.5249 0.5471 0.968
-----
01:01 -1.0334 1.0330 1.000 -0.005807 -0.5126 0.5010 0.982
02:01 -1.1652 0.8913 0.794 -0.002618 -0.5102 0.5049 0.992
02:06 -2868.1460 2836.9076 0.991 -0.028534 -0.5374 0.4803 0.912
03:01 -1.1813 1.7185 0.717 0.011958 -0.5044 0.5283 0.964
11:01 -2.3225 1.4322 0.642 0.008025 -0.5026 0.5186 0.975
23:01 -2.8348 2.8287 0.998 -0.005857 -0.5148 0.5031 0.982
24:03 -2868.1286 2836.9250 0.991 -0.011249 -0.5182 0.4957 0.965
25:01 -3.7579 0.7568 0.193 -0.025685 -0.5490 0.4976 0.923
26:01 -2731.8901 2698.5450 0.990 -0.014069 -0.5297 0.5015 0.957
29:02 -3.5309 1.1526 0.320 0.033234 -0.4796 0.5461 0.899
31:01 -1.7274 3.1851 0.561 -0.008320 -0.5153 0.4987 0.974
32:01 -3845.6317 3881.2510 0.993 -0.125426 -0.6671 0.4162 0.650
68:01 -2721.2124 2687.7497 0.990 -0.086589 -0.6512 0.4781 0.764
Logistic regression (dominant model) with 60 individuals:
glm(case ~ h + pc1, family = binomial, data = data)
[-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p h.est_OR
24:02 49 11 42.9 81.8 4.0074 0.045* 0.042* 6.005e+00
-----
01:01 36 24 50.0 50.0 0.0000 1.000 1.000 9.998e-01
02:01 25 35 52.0 48.6 0.0000 1.000 1.000 8.720e-01
02:06 59 1 50.8 0.0 0.0000 1.000 1.000 1.647e-07
03:01 51 9 49.0 55.6 0.0000 1.000 1.000 1.308e+00
11:01 55 5 50.9 40.0 0.0000 1.000 1.000 6.407e-01
23:01 58 2 50.0 50.0 0.0000 1.000 1.000 9.969e-01
24:03 59 1 50.8 0.0 0.0000 1.000 1.000 1.676e-07
25:01 55 5 52.7 20.0 0.8727 0.350 0.353 2.230e-01
26:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.744e-08
29:02 56 4 51.8 25.0 0.2679 0.605 0.612 3.045e-01
31:01 57 3 49.1 66.7 0.0000 1.000 1.000 2.073e+00
32:01 56 4 46.4 100.0 2.4107 0.121 0.112 5.428e+07
68:01 57 3 52.6 0.0 1.4035 0.236 0.237 5.416e-08
h.2.5%_OR h.97.5%_OR h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02 1.17200 30.766 0.032* 0.011111 -0.5249 0.5471 0.968
-----
01:01 0.35579 2.809 1.000 -0.005807 -0.5126 0.5010 0.982
02:01 0.31185 2.438 0.794 -0.002618 -0.5102 0.5049 0.992
02:06 0.00000 Inf 0.991 -0.028534 -0.5374 0.4803 0.912
03:01 0.30687 5.576 0.717 0.011958 -0.5044 0.5283 0.964
11:01 0.09803 4.188 0.642 0.008025 -0.5026 0.5186 0.975
23:01 0.05873 16.923 0.998 -0.005857 -0.5148 0.5031 0.982
24:03 0.00000 Inf 0.991 -0.011249 -0.5182 0.4957 0.965
25:01 0.02333 2.131 0.193 -0.025685 -0.5490 0.4976 0.923
26:01 0.00000 Inf 0.990 -0.014069 -0.5297 0.5015 0.957
29:02 0.02928 3.167 0.320 0.033234 -0.4796 0.5461 0.899
31:01 0.17774 24.171 0.561 -0.008320 -0.5153 0.4987 0.974
32:01 0.00000 Inf 0.993 -0.125426 -0.6671 0.4162 0.650
68:01 0.00000 Inf 0.990 -0.086589 -0.6512 0.4781 0.764
Linear regression (dominant model) with 60 individuals:
glm(y ~ h, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5% h.97.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.02941 -0.4805 0.5393
02:01 25 35 -0.32331 -0.000618 0.190 0.32269 -0.1772 0.8226
02:06 59 1 -0.14024 0.170057 . 0.31030 -1.6397 2.2603
03:01 51 9 -0.05600 -0.583178 0.147 -0.52718 -1.2136 0.1592
11:01 55 5 -0.19188 0.489815 0.287 0.68170 -0.2051 1.5685
23:01 58 2 -0.15400 0.413687 0.281 0.56768 -0.8165 1.9518
24:02 49 11 -0.10486 -0.269664 0.537 -0.16481 -0.8091 0.4795
24:03 59 1 -0.11409 -1.373118 . -1.25903 -3.1835 0.6655
25:01 55 5 -0.12237 -0.274749 0.742 -0.15237 -1.0555 0.7507
26:01 57 3 -0.12473 -0.331558 0.690 -0.20683 -1.3519 0.9383
29:02 56 4 -0.13044 -0.199941 0.789 -0.06950 -1.0709 0.9319
31:01 57 3 -0.10097 -0.783003 0.607 -0.68203 -1.8149 0.4508
32:01 56 4 -0.07702 -0.947791 0.092 -0.87077 -1.8470 0.1054
68:01 57 3 -0.16915 0.512457 0.196 0.68161 -0.4512 1.8145
h.pval
01:01 0.910
02:01 0.211
02:06 0.756
03:01 0.138
11:01 0.137
23:01 0.425
24:02 0.618
24:03 0.205
25:01 0.742
26:01 0.725
29:02 0.892
31:01 0.243
32:01 0.086
68:01 0.243
Linear regression (dominant model) with 60 individuals:
glm(y ~ h + pc1, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773
02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891
02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722
03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884
11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520
23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598
24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034
24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708
25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388
26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460
29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496
31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254
32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862
68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749
h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386
02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414
02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371
03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539
11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474
23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343
24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393
24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395
25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396
26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390
29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372
31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371
32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210
68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294
Linear regression (dominant model) with 60 individuals:
glm(y ~ h + pc1, data = data)
[-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p h.est h.2.5%
01:01 36 24 -0.14684 -0.117427 0.909 0.03377 -0.4773
02:01 25 35 -0.32331 -0.000618 0.190 0.31273 -0.1891
02:06 59 1 -0.14024 0.170057 . 0.38821 -1.5722
03:01 51 9 -0.05600 -0.583178 0.147 -0.48613 -1.1884
11:01 55 5 -0.19188 0.489815 0.287 0.64430 -0.2520
23:01 58 2 -0.15400 0.413687 0.281 0.63150 -0.7598
24:02 49 11 -0.10486 -0.269664 0.537 -0.15742 -0.8034
24:03 59 1 -0.11409 -1.373118 . -1.24145 -3.1708
25:01 55 5 -0.12237 -0.274749 0.742 -0.13241 -1.0388
26:01 57 3 -0.12473 -0.331558 0.690 -0.19823 -1.3460
29:02 56 4 -0.13044 -0.199941 0.789 -0.13606 -1.1496
31:01 57 3 -0.10097 -0.783003 0.607 -0.69057 -1.8254
32:01 56 4 -0.07702 -0.947791 0.092 -0.99595 -1.9862
68:01 57 3 -0.16915 0.512457 0.196 0.76795 -0.3749
h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01 0.544844 0.897 0.11172 -0.1390 0.3624 0.386
02:01 0.814606 0.227 0.10412 -0.1436 0.3519 0.414
02:06 2.348616 0.699 0.11570 -0.1356 0.3670 0.371
03:01 0.216142 0.180 0.07919 -0.1719 0.3303 0.539
11:01 1.540569 0.164 0.09117 -0.1569 0.3392 0.474
23:01 2.022811 0.377 0.12207 -0.1280 0.3721 0.343
24:02 0.488543 0.635 0.10982 -0.1404 0.3601 0.393
24:03 0.687920 0.212 0.10809 -0.1392 0.3554 0.395
25:01 0.773943 0.776 0.10956 -0.1413 0.3604 0.396
26:01 0.949529 0.736 0.11067 -0.1398 0.3611 0.390
29:02 0.877431 0.793 0.11626 -0.1369 0.3694 0.372
31:01 0.444260 0.238 0.11387 -0.1338 0.3615 0.371
32:01 -0.005739 0.054 0.16001 -0.0873 0.4073 0.210
68:01 1.910822 0.193 0.13482 -0.1146 0.3842 0.294
Logistic regression (additive model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p h.est h.2.5%
24:02 109 11 46.8 81.8 3.6030 0.058 0.053 1.7918 0.1585
-----
01:01 95 25 50.5 48.0 0.0000 1.000 1.000 -0.1207 -1.0843
02:01 77 43 48.1 53.5 0.1450 0.703 0.704 0.2137 -0.5289
02:06 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268
03:01 111 9 49.5 55.6 0.0000 1.000 1.000 0.2624 -1.1624
11:01 115 5 50.4 40.0 0.0000 1.000 1.000 -0.4418 -2.3074
23:01 117 3 50.4 33.3 0.0000 1.000 1.000 -0.4323 -2.3435
24:03 119 1 50.4 0.0 0.0000 1.000 1.000 -15.6000 -2868.1268
25:01 115 5 51.3 20.0 0.8348 0.361 0.364 -1.4955 -3.7498
26:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621
29:02 116 4 50.9 25.0 0.2586 0.611 0.619 -1.1701 -3.4931
31:01 117 3 49.6 66.7 0.0000 1.000 1.000 0.7282 -1.7277
32:01 116 4 48.3 100.0 2.3276 0.127 0.119 17.7092 -3859.2763
68:01 117 3 51.3 0.0 1.3675 0.242 0.244 -16.6714 -2731.9621
h.97.5% h.pval
24:02 3.4251 0.032*
-----
01:01 0.8430 0.806
02:01 0.9563 0.573
02:06 2836.9268 0.991
03:01 1.6872 0.718
11:01 1.4237 0.643
23:01 1.4789 0.658
24:03 2836.9268 0.991
25:01 0.7588 0.194
26:01 2698.6192 0.990
29:02 1.1530 0.324
31:01 3.1842 0.561
32:01 3894.6947 0.993
68:01 2698.6192 0.990
Logistic regression (recessive model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p h.est
01:01 59 1 50.8 0 0.000 1.000 1.000 -15.600
02:01 52 8 46.2 75 1.298 0.255 0.254 1.253
02:06 60 0 50.0 . . . . .
03:01 60 0 50.0 . . . . .
11:01 60 0 50.0 . . . . .
23:01 59 1 50.8 0 0.000 1.000 1.000 -15.600
24:02 60 0 50.0 . . . . .
24:03 60 0 50.0 . . . . .
25:01 60 0 50.0 . . . . .
26:01 60 0 50.0 . . . . .
29:02 60 0 50.0 . . . . .
31:01 60 0 50.0 . . . . .
32:01 60 0 50.0 . . . . .
68:01 60 0 50.0 . . . . .
h.2.5% h.97.5% h.pval
01:01 -2868.1268 2836.927 0.991
02:01 -0.4379 2.943 0.146
02:06 . . .
03:01 . . .
11:01 . . .
23:01 -2868.1268 2836.927 0.991
24:02 . . .
24:03 . . .
25:01 . . .
26:01 . . .
29:02 . . .
31:01 . . .
32:01 . . .
68:01 . . .
Logistic regression (genotype model) with 60 individuals:
glm(case ~ h, family = binomial, data = data)
[-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02 49 11 0 42.9 81.8 . 4.0074 0.045* 0.042*
-----
01:01 36 23 1 50.0 52.2 0 1.0435 0.593 1.000
02:01 25 27 8 52.0 40.7 75 2.9659 0.227 0.271
02:06 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
03:01 51 9 0 49.0 55.6 . 0.0000 1.000 1.000
11:01 55 5 0 50.9 40.0 . 0.0000 1.000 1.000
23:01 58 1 1 50.0 100.0 0 2.0000 0.368 1.000
24:03 59 1 0 50.8 0.0 . 0.0000 1.000 1.000
25:01 55 5 0 52.7 20.0 . 0.8727 0.350 0.353
26:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
29:02 56 4 0 51.8 25.0 . 0.2679 0.605 0.612
31:01 57 3 0 49.1 66.7 . 0.0000 1.000 1.000
32:01 56 4 0 46.4 100.0 . 2.4107 0.121 0.112
68:01 57 3 0 52.6 0.0 . 1.4035 0.236 0.237
h1.est h1.2.5% h1.97.5% h1.pval h2.est h2.2.5% h2.97.5%
24:02 1.79176 0.1585 3.4251 0.032* . . .
-----
01:01 0.08701 -0.9600 1.1340 0.871 -15.566 -2868.0929 2836.961
02:01 -0.45474 -1.5524 0.6430 0.417 1.019 -0.7637 2.801
02:06 -15.59997 -2868.1268 2836.9268 0.991 . . .
03:01 0.26236 -1.1624 1.6872 0.718 . . .
11:01 -0.44183 -2.3074 1.4237 0.643 . . .
23:01 16.56607 -4686.4552 4719.5873 0.994 -16.566 -4719.5873 4686.455
24:03 -15.59997 -2868.1268 2836.9268 0.991 . . .
25:01 -1.49549 -3.7498 0.7588 0.194 . . .
26:01 -16.67143 -2731.9621 2698.6192 0.990 . . .
29:02 -1.17007 -3.4931 1.1530 0.324 . . .
31:01 0.72824 -1.7277 3.1842 0.561 . . .
32:01 17.70917 -3859.2763 3894.6947 0.993 . . .
68:01 -16.67143 -2731.9621 2698.6192 0.990 . . .
h2.pval
24:02 .
-----
01:01 0.991
02:01 0.263
02:06 .
03:01 .
11:01 .
23:01 0.994
24:03 .
25:01 .
26:01 .
29:02 .
31:01 .
32:01 .
68:01 .
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:39
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 211, loss: 196.4, oob acc: 54.55%, # of haplo: 13
2, SNP: 66, loss: 173.548, oob acc: 63.64%, # of haplo: 13
3, SNP: 177, loss: 136.352, oob acc: 68.18%, # of haplo: 13
4, SNP: 108, loss: 95.8359, oob acc: 72.73%, # of haplo: 13
5, SNP: 127, loss: 67.3216, oob acc: 77.27%, # of haplo: 13
6, SNP: 95, loss: 47.5888, oob acc: 77.27%, # of haplo: 13
7, SNP: 33, loss: 37.2631, oob acc: 77.27%, # of haplo: 16
8, SNP: 6, loss: 29.7419, oob acc: 77.27%, # of haplo: 18
9, SNP: 208, loss: 25.6913, oob acc: 77.27%, # of haplo: 19
10, SNP: 225, loss: 25.3087, oob acc: 77.27%, # of haplo: 21
11, SNP: 11, loss: 24.8356, oob acc: 77.27%, # of haplo: 23
12, SNP: 151, loss: 19.4134, oob acc: 77.27%, # of haplo: 23
13, SNP: 199, loss: 17.011, oob acc: 77.27%, # of haplo: 23
[1] 2021-10-15 00:23:39, oob acc: 77.27%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
1, SNP: 160, loss: 221.236, oob acc: 76.92%, # of haplo: 17
2, SNP: 145, loss: 173.538, oob acc: 80.77%, # of haplo: 23
3, SNP: 177, loss: 128.58, oob acc: 84.62%, # of haplo: 31
4, SNP: 111, loss: 79.6877, oob acc: 84.62%, # of haplo: 31
5, SNP: 207, loss: 52.5557, oob acc: 88.46%, # of haplo: 32
6, SNP: 245, loss: 41.8731, oob acc: 88.46%, # of haplo: 34
7, SNP: 230, loss: 31.7937, oob acc: 88.46%, # of haplo: 38
8, SNP: 151, loss: 20.4566, oob acc: 88.46%, # of haplo: 36
9, SNP: 14, loss: 19.5805, oob acc: 88.46%, # of haplo: 42
10, SNP: 132, loss: 19.5101, oob acc: 88.46%, # of haplo: 42
11, SNP: 221, loss: 19.485, oob acc: 88.46%, # of haplo: 44
12, SNP: 251, loss: 18.5695, oob acc: 88.46%, # of haplo: 48
[2] 2021-10-15 00:23:39, oob acc: 88.46%, # of SNPs: 12, # of haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 191, loss: 193.067, oob acc: 57.14%, # of haplo: 11
2, SNP: 264, loss: 150.427, oob acc: 64.29%, # of haplo: 12
3, SNP: 132, loss: 93.4067, oob acc: 67.86%, # of haplo: 12
4, SNP: 128, loss: 39.8353, oob acc: 71.43%, # of haplo: 12
5, SNP: 160, loss: 28.2998, oob acc: 75.00%, # of haplo: 12
6, SNP: 144, loss: 13.635, oob acc: 75.00%, # of haplo: 12
7, SNP: 111, loss: 6.04609, oob acc: 75.00%, # of haplo: 12
8, SNP: 40, loss: 6.04583, oob acc: 82.14%, # of haplo: 14
9, SNP: 141, loss: 6.04583, oob acc: 85.71%, # of haplo: 14
10, SNP: 73, loss: 2.9038, oob acc: 85.71%, # of haplo: 14
11, SNP: 199, loss: 2.20025, oob acc: 85.71%, # of haplo: 14
[3] 2021-10-15 00:23:39, oob acc: 85.71%, # of SNPs: 11, # of haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
1, SNP: 147, loss: 158.631, oob acc: 50.00%, # of haplo: 12
2, SNP: 152, loss: 140.375, oob acc: 55.00%, # of haplo: 13
3, SNP: 78, loss: 115.887, oob acc: 60.00%, # of haplo: 16
4, SNP: 115, loss: 77.8082, oob acc: 60.00%, # of haplo: 18
5, SNP: 148, loss: 62.6831, oob acc: 65.00%, # of haplo: 18
6, SNP: 13, loss: 46.5657, oob acc: 75.00%, # of haplo: 20
7, SNP: 109, loss: 31.0312, oob acc: 75.00%, # of haplo: 20
8, SNP: 176, loss: 22.5073, oob acc: 75.00%, # of haplo: 21
9, SNP: 145, loss: 20.9122, oob acc: 75.00%, # of haplo: 21
10, SNP: 128, loss: 20.6728, oob acc: 75.00%, # of haplo: 21
11, SNP: 73, loss: 14.6217, oob acc: 75.00%, # of haplo: 22
12, SNP: 151, loss: 10.2879, oob acc: 75.00%, # of haplo: 23
13, SNP: 199, loss: 8.74645, oob acc: 75.00%, # of haplo: 23
[4] 2021-10-15 00:23:39, oob acc: 75.00%, # of SNPs: 13, # of haplo: 23
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Accuracy with training data: 97.06%
Out-of-bag accuracy: 81.61%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 38
avg. # of SNPs in an individual classifier: 12.25
(sd: 0.96, min: 11, max: 13, median: 12.50)
avg. # of haplotypes in an individual classifier: 27.00
(sd: 14.63, min: 14, max: 48, median: 23.00)
avg. out-of-bag accuracy: 81.61%
(sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:39) 0%
Predicting (2021-10-15 00:23:39) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 4 (15.4%) 4 (15.4%) 17 (65.4%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.002097 0.007711 0.032190 0.028032 0.471142
Dosages:
$dosage - num [1:14, 1:26] 2.50e-01 2.16e-04 2.50e-06 7.31e-01 1.11e-14 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:14] "01:01" "02:01" "02:06" "03:01" ...
..$ : chr [1:26] "NA11881" "NA11992" "NA11994" "NA12249" ...
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:39) 0%
Predicting (2021-10-15 00:23:39) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 4 (15.4%) 4 (15.4%) 17 (65.4%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.002097 0.007711 0.032190 0.028032 0.471142
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed' (the individual-major mode)
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam'
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim'
Import 3932 SNPs within the xMHC region on chromosome 6
SNP genotypes:
90 samples X 3932 SNPs
SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
A/G C/T G/T A/C C/G A/T
1567 1510 348 332 111 64
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed' (the individual-major mode)
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam'
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim'
Import 5316 SNPs from chromosome 6
SNP genotypes:
90 samples X 5316 SNPs
SNPs range from 25651262bp to 33426848bp on hg19
Missing rate per SNP:
min: 0, max: 0.1, mean: 0.0882054, median: 0.1, sd: 0.030674
Missing rate per sample:
min: 0, max: 0.863619, mean: 0.0882054, median: 0.00131678, sd: 0.259735
Minor allele frequency:
min: 0, max: 0.5, mean: 0.201867, median: 0.179012, sd: 0.155475
Allelic information:
A/G C/T G/T A/C C/G A/T
2102 2046 480 471 134 83
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 1 monomorphic SNP
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77
# of samples: 60
# of unique HLA alleles: 12
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:39
=== building individual classifier 1, out-of-bag (25/41.7%) ===
[1] 2021-10-15 00:23:39, oob acc: 98.00%, # of SNPs: 13, # of haplo: 20
=== building individual classifier 2, out-of-bag (22/36.7%) ===
[2] 2021-10-15 00:23:39, oob acc: 90.91%, # of SNPs: 15, # of haplo: 21
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.196909e-03 1.419916e-02 2.996040e-02
Max. Mean SD
4.735980e-01 4.413724e-02 1.070518e-01
Accuracy with training data: 95.00%
Out-of-bag accuracy: 94.45%
Gene: HLA-DQB1
Training dataset: 60 samples X 77 SNPs
# of HLA alleles: 12
# of individual classifiers: 2
total # of SNPs used: 20
avg. # of SNPs in an individual classifier: 14.00
(sd: 1.41, min: 13, max: 15, median: 14.00)
avg. # of haplotypes in an individual classifier: 20.50
(sd: 0.71, min: 20, max: 21, median: 20.50)
avg. out-of-bag accuracy: 94.45%
(sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.196909e-03 1.419916e-02 2.996040e-02
Max. Mean SD
4.735980e-01 4.413724e-02 1.070518e-01
Genome assembly: hg19
The HIBAG model:
There are 77 SNP predictors in total.
There are 2 individual classifiers.
Summarize the missing fractions of SNP predictors per classifier:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 60
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 0
# of unique HLA genotypes: 0
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 200
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Build a HIBAG model with 1 individual classifier:
MAF threshold: NaN
excluding 9 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:39
=== building individual classifier 1, out-of-bag (23/38.3%) ===
[1] 2021-10-15 00:23:39, oob acc: 86.96%, # of SNPs: 12, # of haplo: 32
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
4.166789e-14 4.261245e-14 5.111347e-14 2.589270e-03 1.608934e-02 5.868848e-02
Max. Mean SD
6.267394e-01 6.664806e-02 1.405453e-01
Accuracy with training data: 94.17%
Out-of-bag accuracy: 86.96%
Build a HIBAG model with 1 individual classifier:
MAF threshold: NaN
excluding 9 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:39
=== building individual classifier 1, out-of-bag (24/40.0%) ===
[1] 2021-10-15 00:23:39, oob acc: 87.50%, # of SNPs: 15, # of haplo: 40
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.894066e-24 9.219565e-20 9.218854e-19 2.189685e-03 7.704546e-03 2.406258e-02
Max. Mean SD
2.755151e-01 2.949891e-02 6.162169e-02
Accuracy with training data: 95.00%
Out-of-bag accuracy: 87.50%
Gene: HLA-A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 24
avg. # of SNPs in an individual classifier: 13.50
(sd: 2.12, min: 12, max: 15, median: 13.50)
avg. # of haplotypes in an individual classifier: 36.00
(sd: 5.66, min: 32, max: 40, median: 36.00)
avg. out-of-bag accuracy: 87.23%
(sd: 0.38%, min: 86.96%, max: 87.50%, median: 87.23%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
9.233104e-13 5.204084e-10 5.195775e-09 2.309655e-03 1.448839e-02 3.746431e-02
Max. Mean SD
4.511273e-01 4.807348e-02 1.006148e-01
Genome assembly: hg19
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:39
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 211, loss: 196.4, oob acc: 54.55%, # of haplo: 13
2, SNP: 66, loss: 173.548, oob acc: 63.64%, # of haplo: 13
3, SNP: 177, loss: 136.352, oob acc: 68.18%, # of haplo: 13
4, SNP: 108, loss: 95.8359, oob acc: 72.73%, # of haplo: 13
5, SNP: 127, loss: 67.3216, oob acc: 77.27%, # of haplo: 13
6, SNP: 95, loss: 47.5888, oob acc: 77.27%, # of haplo: 13
7, SNP: 33, loss: 37.2631, oob acc: 77.27%, # of haplo: 16
8, SNP: 6, loss: 29.7419, oob acc: 77.27%, # of haplo: 18
9, SNP: 208, loss: 25.6913, oob acc: 77.27%, # of haplo: 19
10, SNP: 225, loss: 25.3087, oob acc: 77.27%, # of haplo: 21
11, SNP: 11, loss: 24.8356, oob acc: 77.27%, # of haplo: 23
12, SNP: 151, loss: 19.4134, oob acc: 77.27%, # of haplo: 23
13, SNP: 199, loss: 17.011, oob acc: 77.27%, # of haplo: 23
[1] 2021-10-15 00:23:39, oob acc: 77.27%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
1, SNP: 160, loss: 221.236, oob acc: 76.92%, # of haplo: 17
2, SNP: 145, loss: 173.538, oob acc: 80.77%, # of haplo: 23
3, SNP: 177, loss: 128.58, oob acc: 84.62%, # of haplo: 31
4, SNP: 111, loss: 79.6877, oob acc: 84.62%, # of haplo: 31
5, SNP: 207, loss: 52.5557, oob acc: 88.46%, # of haplo: 32
6, SNP: 245, loss: 41.8731, oob acc: 88.46%, # of haplo: 34
7, SNP: 230, loss: 31.7937, oob acc: 88.46%, # of haplo: 38
8, SNP: 151, loss: 20.4566, oob acc: 88.46%, # of haplo: 36
9, SNP: 14, loss: 19.5805, oob acc: 88.46%, # of haplo: 42
10, SNP: 132, loss: 19.5101, oob acc: 88.46%, # of haplo: 42
11, SNP: 221, loss: 19.485, oob acc: 88.46%, # of haplo: 44
12, SNP: 251, loss: 18.5695, oob acc: 88.46%, # of haplo: 48
[2] 2021-10-15 00:23:40, oob acc: 88.46%, # of SNPs: 12, # of haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 191, loss: 193.067, oob acc: 57.14%, # of haplo: 11
2, SNP: 264, loss: 150.427, oob acc: 64.29%, # of haplo: 12
3, SNP: 132, loss: 93.4067, oob acc: 67.86%, # of haplo: 12
4, SNP: 128, loss: 39.8353, oob acc: 71.43%, # of haplo: 12
5, SNP: 160, loss: 28.2998, oob acc: 75.00%, # of haplo: 12
6, SNP: 144, loss: 13.635, oob acc: 75.00%, # of haplo: 12
7, SNP: 111, loss: 6.04609, oob acc: 75.00%, # of haplo: 12
8, SNP: 40, loss: 6.04583, oob acc: 82.14%, # of haplo: 14
9, SNP: 141, loss: 6.04583, oob acc: 85.71%, # of haplo: 14
10, SNP: 73, loss: 2.9038, oob acc: 85.71%, # of haplo: 14
11, SNP: 199, loss: 2.20025, oob acc: 85.71%, # of haplo: 14
[3] 2021-10-15 00:23:40, oob acc: 85.71%, # of SNPs: 11, # of haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
1, SNP: 147, loss: 158.631, oob acc: 50.00%, # of haplo: 12
2, SNP: 152, loss: 140.375, oob acc: 55.00%, # of haplo: 13
3, SNP: 78, loss: 115.887, oob acc: 60.00%, # of haplo: 16
4, SNP: 115, loss: 77.8082, oob acc: 60.00%, # of haplo: 18
5, SNP: 148, loss: 62.6831, oob acc: 65.00%, # of haplo: 18
6, SNP: 13, loss: 46.5657, oob acc: 75.00%, # of haplo: 20
7, SNP: 109, loss: 31.0312, oob acc: 75.00%, # of haplo: 20
8, SNP: 176, loss: 22.5073, oob acc: 75.00%, # of haplo: 21
9, SNP: 145, loss: 20.9122, oob acc: 75.00%, # of haplo: 21
10, SNP: 128, loss: 20.6728, oob acc: 75.00%, # of haplo: 21
11, SNP: 73, loss: 14.6217, oob acc: 75.00%, # of haplo: 22
12, SNP: 151, loss: 10.2879, oob acc: 75.00%, # of haplo: 23
13, SNP: 199, loss: 8.74645, oob acc: 75.00%, # of haplo: 23
[4] 2021-10-15 00:23:40, oob acc: 75.00%, # of SNPs: 13, # of haplo: 23
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Accuracy with training data: 97.06%
Out-of-bag accuracy: 81.61%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 38
avg. # of SNPs in an individual classifier: 12.25
(sd: 0.96, min: 11, max: 13, median: 12.50)
avg. # of haplotypes in an individual classifier: 27.00
(sd: 14.63, min: 14, max: 48, median: 23.00)
avg. out-of-bag accuracy: 81.61%
(sd: 6.49%, min: 75.00%, max: 88.46%, median: 81.49%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:40) 0%
Predicting (2021-10-15 00:23:40) 100%
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 09:01
Allelic ambiguity: 09:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num * - A D E F G H I K L M N Q R S T V W Y
1 120 120 . . . . . . . . . . . . . . . . . . .
9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17
44 120 . 25 . . . . . . . . . . . . 95 . . . . .
56 120 . 117 . . . . . . . . . . . . 3 . . . . .
62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . .
63 120 . 105 . . . . . . . . . . 11 4 . . . . . .
65 120 . 105 . . . . 15 . . . . . . . . . . . . .
66 120 . 61 . . . . . . . 59 . . . . . . . . . .
67 120 . 25 . . . . . . . . . . . . . . . 95 . .
70 120 . 99 . . . . . . . . . . . 21 . . . . . .
73 120 . 117 . . . . . . 3 . . . . . . . . . . .
74 120 . 76 . . . . . 44 . . . . . . . . . . . .
76 120 . 32 . . 24 . . . . . . . . . . . . 64 . .
77 120 . 47 . 64 . . . . . . . . . . . 9 . . . .
79 120 . 96 . . . . . . . . . . . . 24 . . . . .
80 120 . 96 . . . . . . 24 . . . . . . . . . . .
81 120 . 96 24 . . . . . . . . . . . . . . . . .
82 120 . 96 . . . . . . . . 24 . . . . . . . . .
83 120 . 96 . . . . . . . . . . . . 24 . . . . .
90 120 . 38 82 . . . . . . . . . . . . . . . . .
95 120 . 61 . . . . . . . . 15 . . . . . . 44 . .
97 120 . 39 . . . . . . . . . 29 . . 52 . . . . .
99 120 . 105 . . . 15 . . . . . . . . . . . . . .
105 120 . 42 . . . . . . . . . . . . . 78 . . . .
107 120 . 76 . . . . . . . . . . . . . . . . 44 .
109 120 . 116 . . . . . . . . 4 . . . . . . . . .
114 120 . 46 . . . . . 59 . . . . . 15 . . . . . .
116 120 . 61 . . . . . . . . . . . . . . . . . 59
127 120 . 58 . . . . . . . 62 . . . . . . . . . .
142 120 . 73 . . . . . . . . . . . . . . 47 . . .
144 120 . 98 . . . . . . . . . . . 22 . . . . . .
145 120 . 73 . . . . . 47 . . . . . . . . . . . .
149 120 . 112 . . . . . . . . . . . . . . 8 . . .
150 120 . 25 95 . . . . . . . . . . . . . . . . .
151 120 . 106 . . . . . . . . . . . . 14 . . . . .
152 120 . 30 . . 17 . . . . . . . . . . . . 73 . .
156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 .
158 120 . 25 95 . . . . . . . . . . . . . . . . .
161 120 . 111 . 9 . . . . . . . . . . . . . . . .
163 120 . 38 . . . . . . . . . . . . . . 82 . . .
166 120 . 39 . . 81 . . . . . . . . . . . . . . .
167 120 . 39 . . . . . . . . . . . . . . . . 81 .
183 120 120 . . . . . . . . . . . . . . . . . . .
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num * - A D E F G H I K L M N Q R S T V W Y
-23 120 120 . . . . . . . . . . . . . . . . . . .
-22 120 120 . . . . . . . . . . . . . . . . . . .
-21 120 120 . . . . . . . . . . . . . . . . . . .
-20 120 120 . . . . . . . . . . . . . . . . . . .
-19 120 120 . . . . . . . . . . . . . . . . . . .
-18 120 120 . . . . . . . . . . . . . . . . . . .
-17 120 120 . . . . . . . . . . . . . . . . . . .
-16 120 120 . . . . . . . . . . . . . . . . . . .
-15 120 120 . . . . . . . . . . . . . . . . . . .
-14 120 120 . . . . . . . . . . . . . . . . . . .
-13 120 120 . . . . . . . . . . . . . . . . . . .
-12 120 120 . . . . . . . . . . . . . . . . . . .
-11 120 120 . . . . . . . . . . . . . . . . . . .
-10 120 120 . . . . . . . . . . . . . . . . . . .
-9 120 120 . . . . . . . . . . . . . . . . . . .
-8 120 120 . . . . . . . . . . . . . . . . . . .
-7 120 120 . . . . . . . . . . . . . . . . . . .
-6 120 120 . . . . . . . . . . . . . . . . . . .
-5 120 120 . . . . . . . . . . . . . . . . . . .
-4 120 120 . . . . . . . . . . . . . . . . . . .
-3 120 120 . . . . . . . . . . . . . . . . . . .
-2 120 120 . . . . . . . . . . . . . . . . . . .
-1 120 120 . . . . . . . . . . . . . . . . . . .
. 120 120 . . . . . . . . . . . . . . . . . . .
1 120 120 . . . . . . . . . . . . . . . . . . .
9 120 . 81 . . . . . . . . . . . . . 15 7 . . 17
44 120 . 25 . . . . . . . . . . . . 95 . . . . .
56 120 . 117 . . . . . . . . . . . . 3 . . . . .
62 120 . 46 . . 15 . 44 . . . 4 . . . 11 . . . . .
63 120 . 105 . . . . . . . . . . 11 4 . . . . . .
65 120 . 105 . . . . 15 . . . . . . . . . . . . .
66 120 . 61 . . . . . . . 59 . . . . . . . . . .
67 120 . 25 . . . . . . . . . . . . . . . 95 . .
70 120 . 99 . . . . . . . . . . . 21 . . . . . .
73 120 . 117 . . . . . . 3 . . . . . . . . . . .
74 120 . 76 . . . . . 44 . . . . . . . . . . . .
76 120 . 32 . . 24 . . . . . . . . . . . . 64 . .
77 120 . 47 . 64 . . . . . . . . . . . 9 . . . .
79 120 . 96 . . . . . . . . . . . . 24 . . . . .
80 120 . 96 . . . . . . 24 . . . . . . . . . . .
81 120 . 96 24 . . . . . . . . . . . . . . . . .
82 120 . 96 . . . . . . . . 24 . . . . . . . . .
83 120 . 96 . . . . . . . . . . . . 24 . . . . .
90 120 . 38 82 . . . . . . . . . . . . . . . . .
95 120 . 61 . . . . . . . . 15 . . . . . . 44 . .
97 120 . 39 . . . . . . . . . 29 . . 52 . . . . .
99 120 . 105 . . . 15 . . . . . . . . . . . . . .
105 120 . 42 . . . . . . . . . . . . . 78 . . . .
107 120 . 76 . . . . . . . . . . . . . . . . 44 .
109 120 . 116 . . . . . . . . 4 . . . . . . . . .
114 120 . 46 . . . . . 59 . . . . . 15 . . . . . .
116 120 . 61 . . . . . . . . . . . . . . . . . 59
127 120 . 58 . . . . . . . 62 . . . . . . . . . .
142 120 . 73 . . . . . . . . . . . . . . 47 . . .
144 120 . 98 . . . . . . . . . . . 22 . . . . . .
145 120 . 73 . . . . . 47 . . . . . . . . . . . .
149 120 . 112 . . . . . . . . . . . . . . 8 . . .
150 120 . 25 95 . . . . . . . . . . . . . . . . .
151 120 . 106 . . . . . . . . . . . . 14 . . . . .
152 120 . 30 . . 17 . . . . . . . . . . . . 73 . .
156 120 . 25 . . . . . . . . 67 . . 17 . . . . 11 .
158 120 . 25 95 . . . . . . . . . . . . . . . . .
161 120 . 111 . 9 . . . . . . . . . . . . . . . .
163 120 . 38 . . . . . . . . . . . . . . 82 . . .
166 120 . 39 . . 81 . . . . . . . . . . . . . . .
167 120 . 39 . . . . . . . . . . . . . . . . 81 .
183 120 120 . . . . . . . . . . . . . . . . . . .
184 120 120 . . . . . . . . . . . . . . . . . . .
185 120 120 . . . . . . . . . . . . . . . . . . .
186 120 120 . . . . . . . . . . . . . . . . . . .
187 120 120 . . . . . . . . . . . . . . . . . . .
188 120 120 . . . . . . . . . . . . . . . . . . .
189 120 120 . . . . . . . . . . . . . . . . . . .
190 120 120 . . . . . . . . . . . . . . . . . . .
191 120 120 . . . . . . . . . . . . . . . . . . .
192 120 120 . . . . . . . . . . . . . . . . . . .
193 120 120 . . . . . . . . . . . . . . . . . . .
194 120 120 . . . . . . . . . . . . . . . . . . .
195 120 120 . . . . . . . . . . . . . . . . . . .
196 120 120 . . . . . . . . . . . . . . . . . . .
197 120 120 . . . . . . . . . . . . . . . . . . .
198 120 120 . . . . . . . . . . . . . . . . . . .
199 120 120 . . . . . . . . . . . . . . . . . . .
200 120 120 . . . . . . . . . . . . . . . . . . .
201 120 120 . . . . . . . . . . . . . . . . . . .
202 120 120 . . . . . . . . . . . . . . . . . . .
203 120 120 . . . . . . . . . . . . . . . . . . .
204 120 120 . . . . . . . . . . . . . . . . . . .
205 120 120 . . . . . . . . . . . . . . . . . . .
206 120 120 . . . . . . . . . . . . . . . . . . .
207 120 120 . . . . . . . . . . . . . . . . . . .
208 120 120 . . . . . . . . . . . . . . . . . . .
209 120 120 . . . . . . . . . . . . . . . . . . .
210 120 120 . . . . . . . . . . . . . . . . . . .
211 120 120 . . . . . . . . . . . . . . . . . . .
212 120 120 . . . . . . . . . . . . . . . . . . .
213 120 120 . . . . . . . . . . . . . . . . . . .
214 120 120 . . . . . . . . . . . . . . . . . . .
215 120 120 . . . . . . . . . . . . . . . . . . .
216 120 120 . . . . . . . . . . . . . . . . . . .
217 120 120 . . . . . . . . . . . . . . . . . . .
218 120 120 . . . . . . . . . . . . . . . . . . .
219 120 120 . . . . . . . . . . . . . . . . . . .
220 120 120 . . . . . . . . . . . . . . . . . . .
221 120 120 . . . . . . . . . . . . . . . . . . .
222 120 120 . . . . . . . . . . . . . . . . . . .
223 120 120 . . . . . . . . . . . . . . . . . . .
224 120 120 . . . . . . . . . . . . . . . . . . .
225 120 120 . . . . . . . . . . . . . . . . . . .
226 120 120 . . . . . . . . . . . . . . . . . . .
227 120 120 . . . . . . . . . . . . . . . . . . .
228 120 120 . . . . . . . . . . . . . . . . . . .
229 120 120 . . . . . . . . . . . . . . . . . . .
230 120 120 . . . . . . . . . . . . . . . . . . .
231 120 120 . . . . . . . . . . . . . . . . . . .
232 120 120 . . . . . . . . . . . . . . . . . . .
233 120 120 . . . . . . . . . . . . . . . . . . .
234 120 120 . . . . . . . . . . . . . . . . . . .
235 120 120 . . . . . . . . . . . . . . . . . . .
236 120 120 . . . . . . . . . . . . . . . . . . .
237 120 120 . . . . . . . . . . . . . . . . . . .
238 120 120 . . . . . . . . . . . . . . . . . . .
239 120 120 . . . . . . . . . . . . . . . . . . .
240 120 120 . . . . . . . . . . . . . . . . . . .
241 120 120 . . . . . . . . . . . . . . . . . . .
242 120 120 . . . . . . . . . . . . . . . . . . .
243 120 120 . . . . . . . . . . . . . . . . . . .
244 120 120 . . . . . . . . . . . . . . . . . . .
245 120 120 . . . . . . . . . . . . . . . . . . .
246 120 120 . . . . . . . . . . . . . . . . . . .
247 120 120 . . . . . . . . . . . . . . . . . . .
248 120 120 . . . . . . . . . . . . . . . . . . .
249 120 120 . . . . . . . . . . . . . . . . . . .
250 120 120 . . . . . . . . . . . . . . . . . . .
251 120 120 . . . . . . . . . . . . . . . . . . .
252 120 120 . . . . . . . . . . . . . . . . . . .
253 120 120 . . . . . . . . . . . . . . . . . . .
254 120 120 . . . . . . . . . . . . . . . . . . .
255 120 120 . . . . . . . . . . . . . . . . . . .
256 120 120 . . . . . . . . . . . . . . . . . . .
257 120 120 . . . . . . . . . . . . . . . . . . .
258 120 120 . . . . . . . . . . . . . . . . . . .
259 120 120 . . . . . . . . . . . . . . . . . . .
260 120 120 . . . . . . . . . . . . . . . . . . .
261 120 120 . . . . . . . . . . . . . . . . . . .
262 120 120 . . . . . . . . . . . . . . . . . . .
263 120 120 . . . . . . . . . . . . . . . . . . .
264 120 120 . . . . . . . . . . . . . . . . . . .
265 120 120 . . . . . . . . . . . . . . . . . . .
266 120 120 . . . . . . . . . . . . . . . . . . .
267 120 120 . . . . . . . . . . . . . . . . . . .
268 120 120 . . . . . . . . . . . . . . . . . . .
269 120 120 . . . . . . . . . . . . . . . . . . .
270 120 120 . . . . . . . . . . . . . . . . . . .
271 120 120 . . . . . . . . . . . . . . . . . . .
272 120 120 . . . . . . . . . . . . . . . . . . .
273 120 120 . . . . . . . . . . . . . . . . . . .
274 120 120 . . . . . . . . . . . . . . . . . . .
275 120 120 . . . . . . . . . . . . . . . . . . .
276 120 120 . . . . . . . . . . . . . . . . . . .
277 120 120 . . . . . . . . . . . . . . . . . . .
278 120 120 . . . . . . . . . . . . . . . . . . .
279 120 120 . . . . . . . . . . . . . . . . . . .
280 120 120 . . . . . . . . . . . . . . . . . . .
281 120 120 . . . . . . . . . . . . . . . . . . .
282 120 120 . . . . . . . . . . . . . . . . . . .
283 120 120 . . . . . . . . . . . . . . . . . . .
284 120 120 . . . . . . . . . . . . . . . . . . .
285 120 120 . . . . . . . . . . . . . . . . . . .
286 120 120 . . . . . . . . . . . . . . . . . . .
287 120 120 . . . . . . . . . . . . . . . . . . .
288 120 120 . . . . . . . . . . . . . . . . . . .
289 120 120 . . . . . . . . . . . . . . . . . . .
290 120 120 . . . . . . . . . . . . . . . . . . .
291 120 120 . . . . . . . . . . . . . . . . . . .
292 120 120 . . . . . . . . . . . . . . . . . . .
293 120 120 . . . . . . . . . . . . . . . . . . .
294 120 120 . . . . . . . . . . . . . . . . . . .
295 120 120 . . . . . . . . . . . . . . . . . . .
296 120 120 . . . . . . . . . . . . . . . . . . .
297 120 120 . . . . . . . . . . . . . . . . . . .
298 120 120 . . . . . . . . . . . . . . . . . . .
299 120 120 . . . . . . . . . . . . . . . . . . .
300 120 120 . . . . . . . . . . . . . . . . . . .
301 120 120 . . . . . . . . . . . . . . . . . . .
302 120 120 . . . . . . . . . . . . . . . . . . .
303 120 120 . . . . . . . . . . . . . . . . . . .
304 120 120 . . . . . . . . . . . . . . . . . . .
305 120 120 . . . . . . . . . . . . . . . . . . .
306 120 120 . . . . . . . . . . . . . . . . . . .
307 120 120 . . . . . . . . . . . . . . . . . . .
308 120 120 . . . . . . . . . . . . . . . . . . .
309 120 120 . . . . . . . . . . . . . . . . . . .
310 120 120 . . . . . . . . . . . . . . . . . . .
311 120 120 . . . . . . . . . . . . . . . . . . .
312 120 120 . . . . . . . . . . . . . . . . . . .
313 120 120 . . . . . . . . . . . . . . . . . . .
314 120 120 . . . . . . . . . . . . . . . . . . .
315 120 120 . . . . . . . . . . . . . . . . . . .
316 120 120 . . . . . . . . . . . . . . . . . . .
317 120 120 . . . . . . . . . . . . . . . . . . .
318 120 120 . . . . . . . . . . . . . . . . . . .
319 120 120 . . . . . . . . . . . . . . . . . . .
320 120 120 . . . . . . . . . . . . . . . . . . .
321 120 120 . . . . . . . . . . . . . . . . . . .
322 120 120 . . . . . . . . . . . . . . . . . . .
323 120 120 . . . . . . . . . . . . . . . . . . .
324 120 120 . . . . . . . . . . . . . . . . . . .
325 120 120 . . . . . . . . . . . . . . . . . . .
326 120 120 . . . . . . . . . . . . . . . . . . .
327 120 120 . . . . . . . . . . . . . . . . . . .
328 120 120 . . . . . . . . . . . . . . . . . . .
329 120 120 . . . . . . . . . . . . . . . . . . .
330 120 120 . . . . . . . . . . . . . . . . . . .
331 120 120 . . . . . . . . . . . . . . . . . . .
332 120 120 . . . . . . . . . . . . . . . . . . .
333 120 120 . . . . . . . . . . . . . . . . . . .
334 120 120 . . . . . . . . . . . . . . . . . . .
335 120 120 . . . . . . . . . . . . . . . . . . .
336 120 120 . . . . . . . . . . . . . . . . . . .
337 120 120 . . . . . . . . . . . . . . . . . . .
338 120 120 . . . . . . . . . . . . . . . . . . .
339 120 120 . . . . . . . . . . . . . . . . . . .
340 120 120 . . . . . . . . . . . . . . . . . . .
341 120 120 . . . . . . . . . . . . . . . . . . .
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num * - A D E F G I K L M N P Q R S T Y
5 120 112 . . . . . . . . . . 8 . . . . . .
6 120 20 92 8 . . . . . . . . . . . . . . .
7 112 20 92 . . . . . . . . . . . . . . . .
8 112 20 92 . . . . . . . . . . . . . . . .
9 112 3 76 . . . 33 . . . . . . . . . . . .
10 112 3 109 . . . . . . . . . . . . . . . .
11 112 3 109 . . . . . . . . . . . . . . . .
12 112 3 109 . . . . . . . . . . . . . . . .
13 112 3 93 16 . . . . . . . . . . . . . . .
14 112 3 14 . . . . . . . . 95 . . . . . . .
15 112 3 109 . . . . . . . . . . . . . . . .
16 112 3 109 . . . . . . . . . . . . . . . .
17 112 3 109 . . . . . . . . . . . . . . . .
18 112 3 109 . . . . . . . . . . . . . . . .
19 112 3 109 . . . . . . . . . . . . . . . .
20 112 3 109 . . . . . . . . . . . . . . . .
26 112 . 20 . . . . . . . 76 . . . . . . . 16
28 112 . 100 . . . . . . . . . . . . . 12 . .
30 112 . 24 . . . . . . . . . . . . . 12 . 76
37 112 . 100 . . . . . 12 . . . . . . . . . .
38 112 . 29 83 . . . . . . . . . . . . . . .
45 112 . 96 . . 16 . . . . . . . . . . . . .
46 112 . 100 . . 12 . . . . . . . . . . . . .
47 112 . 100 . . . 12 . . . . . . . . . . . .
52 112 . 100 . . . . . . . 12 . . . . . . . .
53 112 . 54 . . . . . . . 58 . . . . . . . .
55 112 . 57 . . . . . . . 12 . . 43 . . . . .
56 112 . 109 . . . . . . . 3 . . . . . . . .
57 112 . 14 33 64 . . . . . . . . . . . 1 . .
66 112 . 97 . 15 . . . . . . . . . . . . . .
67 112 . 97 . . . . . 15 . . . . . . . . . .
70 112 3 50 . . 3 . . . . . . . . . 56 . . .
71 112 3 14 . 3 . . . . 12 . . . . . . . 80 .
72 112 3 109 . . . . . . . . . . . . . . . .
73 112 3 109 . . . . . . . . . . . . . . . .
74 112 3 17 12 . 80 . . . . . . . . . . . . .
75 112 3 29 . . . . . . . 80 . . . . . . . .
76 112 3 109 . . . . . . . . . . . . . . . .
77 112 3 26 . . . . . . . . . . . . . . 83 .
78 112 3 109 . . . . . . . . . . . . . . . .
79 112 3 109 . . . . . . . . . . . . . . . .
80 112 3 109 . . . . . . . . . . . . . . . .
81 112 3 109 . . . . . . . . . . . . . . . .
82 112 3 109 . . . . . . . . . . . . . . . .
83 112 3 109 . . . . . . . . . . . . . . . .
84 112 3 51 . . . . . . . . . . . 58 . . . .
85 112 3 51 . . . . . . . 58 . . . . . . . .
86 112 3 50 . . 58 . 1 . . . . . . . . . . .
87 112 3 15 . . . 36 . . . 58 . . . . . . . .
88 112 3 109 . . . . . . . . . . . . . . . .
89 112 3 51 . . . . . . . . . . . . . . 58 .
90 112 3 51 . . . . . . . . . . . . . . 58 .
91 112 3 109 . . . . . . . . . . . . . . . .
92 112 3 109 . . . . . . . . . . . . . . . .
93 112 3 109 . . . . . . . . . . . . . . . .
94 112 17 95 . . . . . . . . . . . . . . . .
95 112 112 . . . . . . . . . . . . . . . . .
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num * - A D E F G I K L M N P Q R S T Y
-31 120 112 . . . . . . . . . . 8 . . . . . .
-30 120 112 . 8 . . . . . . . . . . . . . . .
-29 112 112 . . . . . . . . . . . . . . . . .
-28 112 112 . . . . . . . . . . . . . . . . .
-27 112 112 . . . . . . . . . . . . . . . . .
-26 112 112 . . . . . . . . . . . . . . . . .
-25 112 112 . . . . . . . . . . . . . . . . .
-24 112 112 . . . . . . . . . . . . . . . . .
-23 112 112 . . . . . . . . . . . . . . . . .
-22 112 112 . . . . . . . . . . . . . . . . .
-21 112 112 . . . . . . . . . . . . . . . . .
-20 112 112 . . . . . . . . . . . . . . . . .
-19 112 112 . . . . . . . . . . . . . . . . .
-18 112 112 . . . . . . . . . . . . . . . . .
-17 112 112 . . . . . . . . . . . . . . . . .
-16 112 112 . . . . . . . . . . . . . . . . .
-15 112 112 . . . . . . . . . . . . . . . . .
-14 112 112 . . . . . . . . . . . . . . . . .
-13 112 112 . . . . . . . . . . . . . . . . .
-12 112 112 . . . . . . . . . . . . . . . . .
-11 112 112 . . . . . . . . . . . . . . . . .
-10 112 112 . . . . . . . . . . . . . . . . .
-9 112 112 . . . . . . . . . . . . . . . . .
-8 112 112 . . . . . . . . . . . . . . . . .
-7 112 112 . . . . . . . . . . . . . . . . .
-6 112 112 . . . . . . . . . . . . . . . . .
-5 112 112 . . . . . . . . . . . . . . . . .
-4 112 112 . . . . . . . . . . . . . . . . .
-3 112 112 . . . . . . . . . . . . . . . . .
-2 112 112 . . . . . . . . . . . . . . . . .
-1 112 112 . . . . . . . . . . . . . . . . .
. 112 112 . . . . . . . . . . . . . . . . .
1 112 112 . . . . . . . . . . . . . . . . .
2 112 112 . . . . . . . . . . . . . . . . .
3 112 112 . . . . . . . . . . . . . . . . .
4 112 112 . . . . . . . . . . . . . . . . .
5 112 112 . . . . . . . . . . . . . . . . .
6 112 20 92 . . . . . . . . . . . . . . . .
7 112 20 92 . . . . . . . . . . . . . . . .
8 112 20 92 . . . . . . . . . . . . . . . .
9 112 3 76 . . . 33 . . . . . . . . . . . .
10 112 3 109 . . . . . . . . . . . . . . . .
11 112 3 109 . . . . . . . . . . . . . . . .
12 112 3 109 . . . . . . . . . . . . . . . .
13 112 3 93 16 . . . . . . . . . . . . . . .
14 112 3 14 . . . . . . . . 95 . . . . . . .
15 112 3 109 . . . . . . . . . . . . . . . .
16 112 3 109 . . . . . . . . . . . . . . . .
17 112 3 109 . . . . . . . . . . . . . . . .
18 112 3 109 . . . . . . . . . . . . . . . .
19 112 3 109 . . . . . . . . . . . . . . . .
20 112 3 109 . . . . . . . . . . . . . . . .
26 112 . 20 . . . . . . . 76 . . . . . . . 16
28 112 . 100 . . . . . . . . . . . . . 12 . .
30 112 . 24 . . . . . . . . . . . . . 12 . 76
37 112 . 100 . . . . . 12 . . . . . . . . . .
38 112 . 29 83 . . . . . . . . . . . . . . .
45 112 . 96 . . 16 . . . . . . . . . . . . .
46 112 . 100 . . 12 . . . . . . . . . . . . .
47 112 . 100 . . . 12 . . . . . . . . . . . .
52 112 . 100 . . . . . . . 12 . . . . . . . .
53 112 . 54 . . . . . . . 58 . . . . . . . .
55 112 . 57 . . . . . . . 12 . . 43 . . . . .
56 112 . 109 . . . . . . . 3 . . . . . . . .
57 112 . 14 33 64 . . . . . . . . . . . 1 . .
66 112 . 97 . 15 . . . . . . . . . . . . . .
67 112 . 97 . . . . . 15 . . . . . . . . . .
70 112 3 50 . . 3 . . . . . . . . . 56 . . .
71 112 3 14 . 3 . . . . 12 . . . . . . . 80 .
72 112 3 109 . . . . . . . . . . . . . . . .
73 112 3 109 . . . . . . . . . . . . . . . .
74 112 3 17 12 . 80 . . . . . . . . . . . . .
75 112 3 29 . . . . . . . 80 . . . . . . . .
76 112 3 109 . . . . . . . . . . . . . . . .
77 112 3 26 . . . . . . . . . . . . . . 83 .
78 112 3 109 . . . . . . . . . . . . . . . .
79 112 3 109 . . . . . . . . . . . . . . . .
80 112 3 109 . . . . . . . . . . . . . . . .
81 112 3 109 . . . . . . . . . . . . . . . .
82 112 3 109 . . . . . . . . . . . . . . . .
83 112 3 109 . . . . . . . . . . . . . . . .
84 112 3 51 . . . . . . . . . . . 58 . . . .
85 112 3 51 . . . . . . . 58 . . . . . . . .
86 112 3 50 . . 58 . 1 . . . . . . . . . . .
87 112 3 15 . . . 36 . . . 58 . . . . . . . .
88 112 3 109 . . . . . . . . . . . . . . . .
89 112 3 51 . . . . . . . . . . . . . . 58 .
90 112 3 51 . . . . . . . . . . . . . . 58 .
91 112 3 109 . . . . . . . . . . . . . . . .
92 112 3 109 . . . . . . . . . . . . . . . .
93 112 3 109 . . . . . . . . . . . . . . . .
94 112 17 95 . . . . . . . . . . . . . . . .
95 112 112 . . . . . . . . . . . . . . . . .
96 112 112 . . . . . . . . . . . . . . . . .
97 112 112 . . . . . . . . . . . . . . . . .
98 112 112 . . . . . . . . . . . . . . . . .
99 112 112 . . . . . . . . . . . . . . . . .
100 112 112 . . . . . . . . . . . . . . . . .
101 112 112 . . . . . . . . . . . . . . . . .
102 112 112 . . . . . . . . . . . . . . . . .
103 112 112 . . . . . . . . . . . . . . . . .
104 112 112 . . . . . . . . . . . . . . . . .
105 112 112 . . . . . . . . . . . . . . . . .
106 112 112 . . . . . . . . . . . . . . . . .
107 112 112 . . . . . . . . . . . . . . . . .
108 112 112 . . . . . . . . . . . . . . . . .
109 112 112 . . . . . . . . . . . . . . . . .
110 112 112 . . . . . . . . . . . . . . . . .
111 112 112 . . . . . . . . . . . . . . . . .
112 112 112 . . . . . . . . . . . . . . . . .
113 112 112 . . . . . . . . . . . . . . . . .
114 112 112 . . . . . . . . . . . . . . . . .
115 112 112 . . . . . . . . . . . . . . . . .
116 112 112 . . . . . . . . . . . . . . . . .
117 112 112 . . . . . . . . . . . . . . . . .
118 112 112 . . . . . . . . . . . . . . . . .
119 112 112 . . . . . . . . . . . . . . . . .
120 112 112 . . . . . . . . . . . . . . . . .
121 112 112 . . . . . . . . . . . . . . . . .
122 112 112 . . . . . . . . . . . . . . . . .
123 112 112 . . . . . . . . . . . . . . . . .
124 112 112 . . . . . . . . . . . . . . . . .
125 112 112 . . . . . . . . . . . . . . . . .
126 112 112 . . . . . . . . . . . . . . . . .
127 112 112 . . . . . . . . . . . . . . . . .
128 112 112 . . . . . . . . . . . . . . . . .
129 112 112 . . . . . . . . . . . . . . . . .
130 112 112 . . . . . . . . . . . . . . . . .
131 112 112 . . . . . . . . . . . . . . . . .
132 112 112 . . . . . . . . . . . . . . . . .
133 112 112 . . . . . . . . . . . . . . . . .
134 112 112 . . . . . . . . . . . . . . . . .
135 112 112 . . . . . . . . . . . . . . . . .
136 112 112 . . . . . . . . . . . . . . . . .
137 112 112 . . . . . . . . . . . . . . . . .
138 112 112 . . . . . . . . . . . . . . . . .
139 112 112 . . . . . . . . . . . . . . . . .
140 112 112 . . . . . . . . . . . . . . . . .
141 112 112 . . . . . . . . . . . . . . . . .
142 112 112 . . . . . . . . . . . . . . . . .
143 112 112 . . . . . . . . . . . . . . . . .
144 112 112 . . . . . . . . . . . . . . . . .
145 112 112 . . . . . . . . . . . . . . . . .
146 112 112 . . . . . . . . . . . . . . . . .
147 112 112 . . . . . . . . . . . . . . . . .
148 112 112 . . . . . . . . . . . . . . . . .
149 112 112 . . . . . . . . . . . . . . . . .
150 112 112 . . . . . . . . . . . . . . . . .
151 112 112 . . . . . . . . . . . . . . . . .
152 112 112 . . . . . . . . . . . . . . . . .
153 112 112 . . . . . . . . . . . . . . . . .
154 112 112 . . . . . . . . . . . . . . . . .
155 112 112 . . . . . . . . . . . . . . . . .
156 112 112 . . . . . . . . . . . . . . . . .
157 112 112 . . . . . . . . . . . . . . . . .
158 112 112 . . . . . . . . . . . . . . . . .
159 112 112 . . . . . . . . . . . . . . . . .
160 112 112 . . . . . . . . . . . . . . . . .
161 112 112 . . . . . . . . . . . . . . . . .
162 112 112 . . . . . . . . . . . . . . . . .
163 112 112 . . . . . . . . . . . . . . . . .
164 112 112 . . . . . . . . . . . . . . . . .
165 112 112 . . . . . . . . . . . . . . . . .
166 112 112 . . . . . . . . . . . . . . . . .
167 112 112 . . . . . . . . . . . . . . . . .
168 112 112 . . . . . . . . . . . . . . . . .
169 112 112 . . . . . . . . . . . . . . . . .
170 112 112 . . . . . . . . . . . . . . . . .
171 112 112 . . . . . . . . . . . . . . . . .
172 112 112 . . . . . . . . . . . . . . . . .
173 112 112 . . . . . . . . . . . . . . . . .
174 112 112 . . . . . . . . . . . . . . . . .
175 112 112 . . . . . . . . . . . . . . . . .
176 112 112 . . . . . . . . . . . . . . . . .
177 112 112 . . . . . . . . . . . . . . . . .
178 112 112 . . . . . . . . . . . . . . . . .
179 112 112 . . . . . . . . . . . . . . . . .
180 112 112 . . . . . . . . . . . . . . . . .
181 112 112 . . . . . . . . . . . . . . . . .
182 112 112 . . . . . . . . . . . . . . . . .
183 112 112 . . . . . . . . . . . . . . . . .
184 112 112 . . . . . . . . . . . . . . . . .
185 112 112 . . . . . . . . . . . . . . . . .
186 112 112 . . . . . . . . . . . . . . . . .
187 112 112 . . . . . . . . . . . . . . . . .
188 112 112 . . . . . . . . . . . . . . . . .
189 112 112 . . . . . . . . . . . . . . . . .
190 112 112 . . . . . . . . . . . . . . . . .
191 112 112 . . . . . . . . . . . . . . . . .
192 112 112 . . . . . . . . . . . . . . . . .
193 112 112 . . . . . . . . . . . . . . . . .
194 112 112 . . . . . . . . . . . . . . . . .
195 112 112 . . . . . . . . . . . . . . . . .
196 112 112 . . . . . . . . . . . . . . . . .
197 112 112 . . . . . . . . . . . . . . . . .
198 112 112 . . . . . . . . . . . . . . . . .
199 112 112 . . . . . . . . . . . . . . . . .
200 112 112 . . . . . . . . . . . . . . . . .
201 112 112 . . . . . . . . . . . . . . . . .
202 112 112 . . . . . . . . . . . . . . . . .
203 112 112 . . . . . . . . . . . . . . . . .
204 112 112 . . . . . . . . . . . . . . . . .
205 112 112 . . . . . . . . . . . . . . . . .
206 112 112 . . . . . . . . . . . . . . . . .
207 112 112 . . . . . . . . . . . . . . . . .
208 112 112 . . . . . . . . . . . . . . . . .
209 112 112 . . . . . . . . . . . . . . . . .
210 112 112 . . . . . . . . . . . . . . . . .
211 112 112 . . . . . . . . . . . . . . . . .
212 112 112 . . . . . . . . . . . . . . . . .
213 112 112 . . . . . . . . . . . . . . . . .
214 112 112 . . . . . . . . . . . . . . . . .
215 112 112 . . . . . . . . . . . . . . . . .
216 112 112 . . . . . . . . . . . . . . . . .
217 112 112 . . . . . . . . . . . . . . . . .
218 112 112 . . . . . . . . . . . . . . . . .
219 112 112 . . . . . . . . . . . . . . . . .
220 112 112 . . . . . . . . . . . . . . . . .
221 112 112 . . . . . . . . . . . . . . . . .
222 112 112 . . . . . . . . . . . . . . . . .
223 112 112 . . . . . . . . . . . . . . . . .
224 112 112 . . . . . . . . . . . . . . . . .
225 112 112 . . . . . . . . . . . . . . . . .
226 112 112 . . . . . . . . . . . . . . . . .
227 112 112 . . . . . . . . . . . . . . . . .
228 112 112 . . . . . . . . . . . . . . . . .
229 112 112 . . . . . . . . . . . . . . . . .
230 112 112 . . . . . . . . . . . . . . . . .
231 112 112 . . . . . . . . . . . . . . . . .
232 112 112 . . . . . . . . . . . . . . . . .
233 112 112 . . . . . . . . . . . . . . . . .
234 112 112 . . . . . . . . . . . . . . . . .
235 112 112 . . . . . . . . . . . . . . . . .
236 112 112 . . . . . . . . . . . . . . . . .
237 112 112 . . . . . . . . . . . . . . . . .
using the default genome assembly (assembly="hg19")
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
Build a HIBAG model with 10 individual classifiers:
MAF threshold: NaN
excluding 9 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:42
=== building individual classifier 1, out-of-bag (23/38.3%) ===
[1] 2021-10-15 00:23:42, oob acc: 86.96%, # of SNPs: 12, # of haplo: 32
=== building individual classifier 2, out-of-bag (24/40.0%) ===
[2] 2021-10-15 00:23:42, oob acc: 87.50%, # of SNPs: 15, # of haplo: 40
=== building individual classifier 3, out-of-bag (24/40.0%) ===
[3] 2021-10-15 00:23:42, oob acc: 97.92%, # of SNPs: 14, # of haplo: 21
=== building individual classifier 4, out-of-bag (22/36.7%) ===
[4] 2021-10-15 00:23:42, oob acc: 95.45%, # of SNPs: 14, # of haplo: 25
=== building individual classifier 5, out-of-bag (19/31.7%) ===
[5] 2021-10-15 00:23:42, oob acc: 78.95%, # of SNPs: 14, # of haplo: 21
=== building individual classifier 6, out-of-bag (24/40.0%) ===
[6] 2021-10-15 00:23:42, oob acc: 93.75%, # of SNPs: 16, # of haplo: 22
=== building individual classifier 7, out-of-bag (24/40.0%) ===
[7] 2021-10-15 00:23:42, oob acc: 93.75%, # of SNPs: 24, # of haplo: 81
=== building individual classifier 8, out-of-bag (21/35.0%) ===
[8] 2021-10-15 00:23:43, oob acc: 92.86%, # of SNPs: 20, # of haplo: 45
=== building individual classifier 9, out-of-bag (19/31.7%) ===
[9] 2021-10-15 00:23:43, oob acc: 94.74%, # of SNPs: 16, # of haplo: 45
=== building individual classifier 10, out-of-bag (19/31.7%) ===
[10] 2021-10-15 00:23:43, oob acc: 97.37%, # of SNPs: 15, # of haplo: 40
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001493079 0.0001617044 0.0002732730 0.0039571951 0.0150509810 0.0326242837
Max. Mean SD
0.3657388922 0.0410332850 0.0799788450
Accuracy with training data: 98.33%
Out-of-bag accuracy: 91.92%
Gene: HLA-A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 10
total # of SNPs used: 95
avg. # of SNPs in an individual classifier: 16.00
(sd: 3.50, min: 12, max: 24, median: 15.00)
avg. # of haplotypes in an individual classifier: 37.20
(sd: 18.22, min: 21, max: 81, median: 36.00)
avg. out-of-bag accuracy: 91.92%
(sd: 5.83%, min: 78.95%, max: 97.92%, median: 93.75%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0001493079 0.0001617044 0.0002732730 0.0039571951 0.0150509810 0.0326242837
Max. Mean SD
0.3657388922 0.0410332850 0.0799788450
Genome assembly: hg19
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
using the default genome assembly (assembly="hg19")
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
Loading required namespace: gdsfmt
Loading required namespace: SNPRelate
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU_Chr6.gds'
Import 1668 SNPs within the xMHC region on chromosome 6
2 SNPs with invalid alleles have been removed.
SNP genotypes:
165 samples X 1666 SNPs
SNPs range from 28837960bp to 33524089bp on hg18
Missing rate per SNP:
min: 0, max: 0.0484848, mean: 0.00175707, median: 0, sd: 0.00515153
Missing rate per sample:
min: 0, max: 0.0120048, mean: 0.00175707, median: 0.00120048, sd: 0.00210091
Minor allele frequency:
min: 0, max: 0.5, mean: 0.19767, median: 0.175758, sd: 0.150469
Allelic information:
A/G T/C G/A C/T T/G A/C C/A G/T A/T C/G G/C T/A
412 318 299 285 79 76 75 56 20 19 16 11
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed' (the individual-major mode)
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam'
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim'
Import 3932 SNPs within the xMHC region on chromosome 6
No allelic strand or A/B allele is flipped.
SNP genotypes:
150 samples X 1214 SNPs
SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0866667, mean: 0.0844646, median: 0.0866667, sd: 0.0128841
Missing rate per sample:
min: 0, max: 0.968699, mean: 0.0844646, median: 0.000823723, sd: 0.273119
Minor allele frequency:
min: 0, max: 0.5, mean: 0.234168, median: 0.218978, sd: 0.137855
Allelic information:
A/G C/T G/T A/C
505 496 109 104
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1197 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0657059, median: 0.0666667, sd: 0.00757446
Missing rate per sample:
min: 0, max: 0.978279, mean: 0.0657059, median: 0.000835422, sd: 0.245786
Minor allele frequency:
min: 0.101695, max: 0.5, mean: 0.278734, median: 0.267857, sd: 0.117338
Allelic information:
A/G C/T A/C G/T
511 476 105 105
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed' (the individual-major mode)
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam'
Open 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim'
Import 3932 SNPs within the xMHC region on chromosome 6
SNP genotypes:
90 samples X 3932 SNPs
SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
A/G C/T G/T A/C C/G A/T
1567 1510 348 332 111 64
No allelic strand or A/B allele is flipped.
SNP genotypes:
60 samples X 1214 SNPs
SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0650879, median: 0.0666667, sd: 0.0097381
Missing rate per sample:
min: 0, max: 0.968699, mean: 0.0650879, median: 0.000823723, sd: 0.243373
Minor allele frequency:
min: 0, max: 0.5, mean: 0.234476, median: 0.223214, sd: 0.13833
Allelic information:
A/G C/T G/T A/C
505 496 109 104
using the default genome assembly (assembly="hg19")
SNP genotypes:
60 samples X 275 SNPs
SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C
125 97 32 21
MAF filter (>=0.01), excluding 9 SNP(s)
using the default genome assembly (assembly="hg19")
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
Build a HIBAG model with 1 individual classifier:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:45
=== building individual classifier 1, out-of-bag (25/41.7%) ===
[1] 2021-10-15 00:23:45, oob acc: 92.00%, # of SNPs: 24, # of haplo: 29
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.222247e-28 1.128571e-24 1.128371e-23 6.944660e-04 8.333349e-03 3.673611e-02
Max. Mean SD
9.105734e-02 2.054649e-02 2.598603e-02
Accuracy with training data: 96.67%
Out-of-bag accuracy: 92.00%
Build a HIBAG model with 1 individual classifier:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:45
=== building individual classifier 1, out-of-bag (20/33.3%) ===
[1] 2021-10-15 00:23:45, oob acc: 97.50%, # of SNPs: 18, # of haplo: 34
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
5.014366e-13 4.671716e-10 4.667203e-09 1.640727e-03 7.504546e-03 2.126745e-02
Max. Mean SD
9.784316e-02 1.490504e-02 1.947399e-02
Accuracy with training data: 97.50%
Out-of-bag accuracy: 97.50%
Build a HIBAG model with 1 individual classifier:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:45
=== building individual classifier 1, out-of-bag (18/30.0%) ===
[1] 2021-10-15 00:23:45, oob acc: 88.89%, # of SNPs: 14, # of haplo: 38
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
2.222223e-18 6.603163e-16 6.583163e-15 1.944468e-03 1.020834e-02 4.122739e-02
Max. Mean SD
1.808372e-01 2.422083e-02 3.699146e-02
Accuracy with training data: 95.83%
Out-of-bag accuracy: 88.89%
Gene: HLA-C
Training dataset: 60 samples X 83 SNPs
# of HLA alleles: 17
# of individual classifiers: 3
total # of SNPs used: 40
avg. # of SNPs in an individual classifier: 18.67
(sd: 5.03, min: 14, max: 24, median: 18.00)
avg. # of haplotypes in an individual classifier: 33.67
(sd: 4.51, min: 29, max: 38, median: 34.00)
avg. out-of-bag accuracy: 92.80%
(sd: 4.36%, min: 88.89%, max: 97.50%, median: 92.00%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.708707e-13 1.229313e-05 1.229313e-04 1.860746e-03 9.050936e-03 3.332722e-02
Max. Mean SD
1.210500e-01 1.989079e-02 2.507466e-02
Genome assembly: hg19
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 1 monomorphic SNP
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77
# of samples: 60
# of unique HLA alleles: 12
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:45
=== building individual classifier 1, out-of-bag (25/41.7%) ===
[1] 2021-10-15 00:23:45, oob acc: 98.00%, # of SNPs: 13, # of haplo: 20
=== building individual classifier 2, out-of-bag (22/36.7%) ===
[2] 2021-10-15 00:23:45, oob acc: 90.91%, # of SNPs: 15, # of haplo: 21
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.196909e-03 1.419916e-02 2.996040e-02
Max. Mean SD
4.735980e-01 4.413724e-02 1.070518e-01
Accuracy with training data: 95.00%
Out-of-bag accuracy: 94.45%
Gene: HLA-DQB1
Training dataset: 60 samples X 77 SNPs
# of HLA alleles: 12
# of individual classifiers: 2
total # of SNPs used: 20
avg. # of SNPs in an individual classifier: 14.00
(sd: 1.41, min: 13, max: 15, median: 14.00)
avg. # of haplotypes in an individual classifier: 20.50
(sd: 0.71, min: 20, max: 21, median: 20.50)
avg. out-of-bag accuracy: 94.45%
(sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
7.678603e-08 2.465759e-05 2.458848e-04 5.196909e-03 1.419916e-02 2.996040e-02
Max. Mean SD
4.735980e-01 4.413724e-02 1.070518e-01
Genome assembly: hg19
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 9 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:45
=== building individual classifier 1, out-of-bag (23/38.3%) ===
[1] 2021-10-15 00:23:45, oob acc: 86.96%, # of SNPs: 12, # of haplo: 32
=== building individual classifier 2, out-of-bag (24/40.0%) ===
[2] 2021-10-15 00:23:45, oob acc: 87.50%, # of SNPs: 15, # of haplo: 40
=== building individual classifier 3, out-of-bag (24/40.0%) ===
[3] 2021-10-15 00:23:45, oob acc: 97.92%, # of SNPs: 14, # of haplo: 21
=== building individual classifier 4, out-of-bag (22/36.7%) ===
[4] 2021-10-15 00:23:45, oob acc: 95.45%, # of SNPs: 14, # of haplo: 25
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777
Max. Mean SD
0.3658111951 0.0404459574 0.0794719104
Accuracy with training data: 99.17%
Out-of-bag accuracy: 91.96%
Gene: HLA-A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 42
avg. # of SNPs in an individual classifier: 13.75
(sd: 1.26, min: 12, max: 15, median: 14.00)
avg. # of haplotypes in an individual classifier: 29.50
(sd: 8.35, min: 21, max: 40, median: 28.50)
avg. out-of-bag accuracy: 91.96%
(sd: 5.56%, min: 86.96%, max: 97.92%, median: 91.48%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777
Max. Mean SD
0.3658111951 0.0404459574 0.0794719104
Genome assembly: hg19
Gene: HLA-A
Training dataset: 60 samples X 266 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 42
avg. # of SNPs in an individual classifier: 13.75
(sd: 1.26, min: 12, max: 15, median: 14.00)
avg. # of haplotypes in an individual classifier: 29.50
(sd: 8.35, min: 21, max: 40, median: 28.50)
avg. out-of-bag accuracy: 91.96%
(sd: 5.56%, min: 86.96%, max: 97.92%, median: 91.48%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777
Max. Mean SD
0.3658111951 0.0404459574 0.0794719104
Genome assembly: hg19
Fri Oct 15 00:23:45 2021, passing the 1/4 classifiers.
Fri Oct 15 00:23:45 2021, passing the 2/4 classifiers.
Fri Oct 15 00:23:45 2021, passing the 3/4 classifiers.
Fri Oct 15 00:23:45 2021, passing the 4/4 classifiers.
Allele Num. Freq. CR ACC SEN SPE PPV NPV Miscall
Valid. Valid. (%) (%) (%) (%) (%) (%) (%)
----
Overall accuracy: 92.0%, Call rate: 100.0%
01:01 25 0.2083 100.0 100.0 100.0 100.0 100.0 100.0 --
02:01 43 0.3583 100.0 96.7 100.0 95.1 92.5 100.0 --
02:06 1 0.0083 25.0 97.7 0.0 100.0 -- 97.7 02:01 (100)
03:01 9 0.0750 100.0 100.0 100.0 100.0 100.0 100.0 --
11:01 5 0.0417 100.0 100.0 100.0 100.0 100.0 100.0 --
23:01 3 0.0250 100.0 98.4 75.0 100.0 100.0 98.4 24:02 (100)
24:02 11 0.0917 100.0 97.3 100.0 97.1 76.2 100.0 --
24:03 1 0.0083 100.0 97.8 0.0 100.0 -- 97.8 24:02 (75)
25:01 5 0.0417 100.0 98.4 100.0 98.3 84.7 100.0 --
26:01 3 0.0250 100.0 98.4 62.5 100.0 100.0 98.4 25:01 (83)
29:02 4 0.0333 100.0 97.8 75.0 100.0 100.0 97.8 02:01 (75)
31:01 3 0.0250 75.0 100.0 100.0 100.0 100.0 100.0 --
32:01 4 0.0333 100.0 100.0 100.0 100.0 100.0 100.0 --
68:01 3 0.0250 100.0 100.0 100.0 100.0 100.0 100.0 --
\title{Imputation Evaluation}
\documentclass[12pt]{article}
\usepackage{fullpage}
\usepackage{longtable}
\begin{document}
\maketitle
\setlength{\LTcapwidth}{6.5in}
% -------- BEGIN TABLE --------
\begin{longtable}{rrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{10}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{10}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{10}{l}{\it Overall accuracy: 92.0\%, Call rate: 100.0\%} \\
01:01 & 25 & 0.2083 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
02:01 & 43 & 0.3583 & 100.0 & 96.7 & 100.0 & 95.1 & 92.5 & 100.0 & -- \\
02:06 & 1 & 0.0083 & 25.0 & 97.7 & 0.0 & 100.0 & -- & 97.7 & 02:01 (100) \\
03:01 & 9 & 0.0750 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
11:01 & 5 & 0.0417 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
23:01 & 3 & 0.0250 & 100.0 & 98.4 & 75.0 & 100.0 & 100.0 & 98.4 & 24:02 (100) \\
24:02 & 11 & 0.0917 & 100.0 & 97.3 & 100.0 & 97.1 & 76.2 & 100.0 & -- \\
24:03 & 1 & 0.0083 & 100.0 & 97.8 & 0.0 & 100.0 & -- & 97.8 & 24:02 (75) \\
25:01 & 5 & 0.0417 & 100.0 & 98.4 & 100.0 & 98.3 & 84.7 & 100.0 & -- \\
26:01 & 3 & 0.0250 & 100.0 & 98.4 & 62.5 & 100.0 & 100.0 & 98.4 & 25:01 (83) \\
29:02 & 4 & 0.0333 & 100.0 & 97.8 & 75.0 & 100.0 & 100.0 & 97.8 & 02:01 (75) \\
31:01 & 3 & 0.0250 & 75.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
32:01 & 4 & 0.0333 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 3 & 0.0250 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
\end{longtable}
% -------- END TABLE --------
\end{document}
<!DOCTYPE html>
<html>
<head>
<title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1" CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="10">
<i> Overall accuracy: 92.0%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>25</td> <td>0.2083</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>43</td> <td>0.3583</td> <td>100.0</td> <td>96.7</td> <td>100.0</td> <td>95.1</td> <td>92.5</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0083</td> <td>25.0</td> <td>97.7</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.7</td> <td>02:01 (100)</td>
</tr>
<tr>
<td>03:01</td> <td>9</td> <td>0.0750</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>11:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>98.4</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>98.4</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>24:02</td> <td>11</td> <td>0.0917</td> <td>100.0</td> <td>97.3</td> <td>100.0</td> <td>97.1</td> <td>76.2</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0083</td> <td>100.0</td> <td>97.8</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.8</td> <td>24:02 (75)</td>
</tr>
<tr>
<td>25:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>98.4</td> <td>100.0</td> <td>98.3</td> <td>84.7</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>26:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>98.4</td> <td>62.5</td> <td>100.0</td> <td>100.0</td> <td>98.4</td> <td>25:01 (83)</td>
</tr>
<tr>
<td>29:02</td> <td>4</td> <td>0.0333</td> <td>100.0</td> <td>97.8</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>97.8</td> <td>02:01 (75)</td>
</tr>
<tr>
<td>31:01</td> <td>3</td> <td>0.0250</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>32:01</td> <td>4</td> <td>0.0333</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
</table>
</body>
</html>
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 23
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Building a HIBAG model:
4 individual classifiers
run in parallel with 1 compute node
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 2
[-] 2021-10-15 00:23:45
=== building individual classifier 1, out-of-bag (11/32.4%) ===
[1] 2021-10-15 00:23:45, oob acc: 77.27%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
[2] 2021-10-15 00:23:45, oob acc: 88.46%, # of SNPs: 12, # of haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
[3] 2021-10-15 00:23:45, oob acc: 85.71%, # of SNPs: 11, # of haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
[4] 2021-10-15 00:23:46, oob acc: 75.00%, # of SNPs: 13, # of haplo: 23
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002162725 0.0002198443 0.0002519909 0.0043752063 0.0092453043 0.0265291653
Max. Mean SD
0.4711415503 0.0442439721 0.1054645240
Accuracy with training data: 97.06%
Out-of-bag accuracy: 81.61%
Building a HIBAG model:
4 individual classifiers
run in parallel with 2 compute nodes
autosave to 'tmp_model.RData'
[-] 2021-10-15 00:23:47
[1] 2021-10-15 00:23:47, worker 1, # of SNPs: 14, # of haplo: 70, oob acc: 90.9%
==Saved== #1, avg oob acc: 90.91%, sd: NA%, min: 90.91%, max: 90.91%
[2] 2021-10-15 00:23:47, worker 1, # of SNPs: 14, # of haplo: 21, oob acc: 84.6%
==Saved== #2, avg oob acc: 87.76%, sd: 4.45%, min: 84.62%, max: 90.91%
[3] 2021-10-15 00:23:47, worker 2, # of SNPs: 12, # of haplo: 53, oob acc: 90.9%
Stop "job 2".
==Saved== #3, avg oob acc: 88.81%, sd: 3.63%, min: 84.62%, max: 90.91%
[4] 2021-10-15 00:23:47, worker 1, # of SNPs: 14, # of haplo: 20, oob acc: 90.9%
Stop "job 1".
==Saved== #4, avg oob acc: 89.34%, sd: 3.15%, min: 84.62%, max: 90.91%
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003244051 0.0003361529 0.0004418826 0.0035757653 0.0117932960 0.0382212645
Max. Mean SD
0.4365283453 0.0477395507 0.1031755866
Accuracy with training data: 98.53%
Out-of-bag accuracy: 89.34%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 43
avg. # of SNPs in an individual classifier: 13.50
(sd: 1.00, min: 12, max: 14, median: 14.00)
avg. # of haplotypes in an individual classifier: 41.00
(sd: 24.67, min: 20, max: 70, median: 37.00)
avg. out-of-bag accuracy: 89.34%
(sd: 3.15%, min: 84.62%, max: 90.91%, median: 90.91%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0003244051 0.0003361529 0.0004418826 0.0035757653 0.0117932960 0.0382212645
Max. Mean SD
0.4365283453 0.0477395507 0.1031755866
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:47) 0%
Predicting (2021-10-15 00:23:47) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 2 (7.7%) 2 (7.7%) 21 (80.8%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.000872 0.005911 0.031131 0.031966 0.436528
Dosages:
$dosage - num [1:14, 1:26] 1.18e-10 4.32e-09 3.75e-12 9.93e-01 2.60e-20 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:14] "01:01" "02:01" "02:06" "03:01" ...
..$ : chr [1:26] "NA11881" "NA11992" "NA11994" "NA12249" ...
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
run in parallel with 2 compute nodes
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 14
Posterior probability:
[0,0.25) [0.25,0.5) [0.5,0.75) [0.75,1]
1 (3.8%) 2 (7.7%) 2 (7.7%) 21 (80.8%)
Matching proportion of SNP haplotype:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.000872 0.005911 0.031131 0.031966 0.436528
Dosages:
$dosage - num [1:14, 1:26] 1.18e-10 4.32e-09 3.75e-12 9.93e-01 2.60e-20 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:14] "01:01" "02:01" "02:06" "03:01" ...
..$ : chr [1:26] "NA11881" "NA11992" "NA11994" "NA12249" ...
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:48
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 235, loss: 208.912, oob acc: 59.09%, # of haplo: 14
2, SNP: 136, loss: 139.316, oob acc: 63.64%, # of haplo: 14
3, SNP: 126, loss: 96.148, oob acc: 72.73%, # of haplo: 14
4, SNP: 89, loss: 76.2917, oob acc: 77.27%, # of haplo: 15
5, SNP: 102, loss: 61.3783, oob acc: 86.36%, # of haplo: 15
6, SNP: 105, loss: 49.1567, oob acc: 90.91%, # of haplo: 15
7, SNP: 117, loss: 43.0927, oob acc: 95.45%, # of haplo: 15
8, SNP: 259, loss: 26.6243, oob acc: 95.45%, # of haplo: 17
9, SNP: 60, loss: 17.6253, oob acc: 95.45%, # of haplo: 19
10, SNP: 236, loss: 9.50329, oob acc: 95.45%, # of haplo: 20
11, SNP: 94, loss: 7.27191, oob acc: 95.45%, # of haplo: 21
12, SNP: 58, loss: 6.70503, oob acc: 95.45%, # of haplo: 27
13, SNP: 243, loss: 2.87079, oob acc: 95.45%, # of haplo: 30
14, SNP: 5, loss: 2.77321, oob acc: 95.45%, # of haplo: 31
[1] 2021-10-15 00:23:48, oob acc: 95.45%, # of SNPs: 14, # of haplo: 31
=== building individual classifier 2, out-of-bag (9/26.5%) ===
1, SNP: 149, loss: 171.797, oob acc: 66.67%, # of haplo: 13
2, SNP: 176, loss: 120.459, oob acc: 72.22%, # of haplo: 14
3, SNP: 97, loss: 80.1731, oob acc: 83.33%, # of haplo: 14
4, SNP: 56, loss: 51.5193, oob acc: 94.44%, # of haplo: 16
5, SNP: 182, loss: 34.5643, oob acc: 94.44%, # of haplo: 18
6, SNP: 121, loss: 23.0259, oob acc: 94.44%, # of haplo: 18
7, SNP: 234, loss: 15.0596, oob acc: 94.44%, # of haplo: 20
8, SNP: 148, loss: 9.66757, oob acc: 94.44%, # of haplo: 20
9, SNP: 19, loss: 4.29975, oob acc: 94.44%, # of haplo: 27
10, SNP: 226, loss: 0.481093, oob acc: 94.44%, # of haplo: 27
11, SNP: 64, loss: 0.447483, oob acc: 94.44%, # of haplo: 28
12, SNP: 240, loss: 0.365545, oob acc: 94.44%, # of haplo: 37
13, SNP: 57, loss: 0.365132, oob acc: 94.44%, # of haplo: 38
[2] 2021-10-15 00:23:48, oob acc: 94.44%, # of SNPs: 13, # of haplo: 38
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.275953e-07 1.742509e-05 1.731025e-04 2.811482e-03 8.650597e-03 1.989621e-02
Max. Mean SD
5.990492e-02 1.464043e-02 1.658610e-02
Accuracy with training data: 100.00%
Out-of-bag accuracy: 94.95%
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:48
=== building individual classifier 1, out-of-bag (14/41.2%) ===
1, SNP: 118, loss: 190.304, oob acc: 53.57%, # of haplo: 13
2, SNP: 175, loss: 157.208, oob acc: 60.71%, # of haplo: 15
3, SNP: 103, loss: 128.429, oob acc: 64.29%, # of haplo: 15
4, SNP: 182, loss: 66.6054, oob acc: 71.43%, # of haplo: 15
5, SNP: 152, loss: 58.8041, oob acc: 78.57%, # of haplo: 15
6, SNP: 111, loss: 30.086, oob acc: 82.14%, # of haplo: 15
7, SNP: 130, loss: 15.3177, oob acc: 89.29%, # of haplo: 19
8, SNP: 229, loss: 9.99758, oob acc: 89.29%, # of haplo: 28
9, SNP: 185, loss: 7.40712, oob acc: 89.29%, # of haplo: 29
10, SNP: 199, loss: 6.21341, oob acc: 89.29%, # of haplo: 29
11, SNP: 217, loss: 1.38739, oob acc: 89.29%, # of haplo: 30
[1] 2021-10-15 00:23:48, oob acc: 89.29%, # of SNPs: 11, # of haplo: 30
=== building individual classifier 2, out-of-bag (13/38.2%) ===
1, SNP: 101, loss: 154.355, oob acc: 46.15%, # of haplo: 16
2, SNP: 102, loss: 139.148, oob acc: 61.54%, # of haplo: 22
3, SNP: 132, loss: 95.2502, oob acc: 73.08%, # of haplo: 23
4, SNP: 147, loss: 76.9692, oob acc: 76.92%, # of haplo: 34
5, SNP: 53, loss: 68.3851, oob acc: 88.46%, # of haplo: 51
6, SNP: 186, loss: 41.8787, oob acc: 88.46%, # of haplo: 53
7, SNP: 128, loss: 33.5437, oob acc: 92.31%, # of haplo: 53
8, SNP: 14, loss: 23.3103, oob acc: 92.31%, # of haplo: 55
9, SNP: 219, loss: 18.3628, oob acc: 92.31%, # of haplo: 57
10, SNP: 149, loss: 17.9413, oob acc: 92.31%, # of haplo: 89
11, SNP: 73, loss: 16.3172, oob acc: 92.31%, # of haplo: 90
12, SNP: 70, loss: 16.1056, oob acc: 92.31%, # of haplo: 90
13, SNP: 199, loss: 12.3057, oob acc: 92.31%, # of haplo: 90
14, SNP: 203, loss: 12.2013, oob acc: 92.31%, # of haplo: 90
15, SNP: 151, loss: 11.1795, oob acc: 92.31%, # of haplo: 90
[2] 2021-10-15 00:23:48, oob acc: 92.31%, # of SNPs: 15, # of haplo: 90
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0002703521 0.0002971139 0.0005379705 0.0036521203 0.0131584084 0.0415528465
Max. Mean SD
0.5087413114 0.0420589840 0.0891771528
Accuracy with training data: 97.06%
Out-of-bag accuracy: 90.80%
HIBAG model for HLA-A:
2 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
by voting from all individual classifiers
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:48) 0%
Predicting (2021-10-15 00:23:48) 100%
HIBAG model for HLA-A:
2 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
by voting from all individual classifiers
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:48) 0%
Predicting (2021-10-15 00:23:48) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:48
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 235, loss: 208.912, oob acc: 59.09%, # of haplo: 14
2, SNP: 136, loss: 139.316, oob acc: 63.64%, # of haplo: 14
3, SNP: 126, loss: 96.148, oob acc: 72.73%, # of haplo: 14
4, SNP: 89, loss: 76.2917, oob acc: 77.27%, # of haplo: 15
5, SNP: 102, loss: 61.3783, oob acc: 86.36%, # of haplo: 15
6, SNP: 105, loss: 49.1567, oob acc: 90.91%, # of haplo: 15
7, SNP: 117, loss: 43.0927, oob acc: 95.45%, # of haplo: 15
8, SNP: 259, loss: 26.6243, oob acc: 95.45%, # of haplo: 17
9, SNP: 60, loss: 17.6253, oob acc: 95.45%, # of haplo: 19
10, SNP: 236, loss: 9.50329, oob acc: 95.45%, # of haplo: 20
11, SNP: 94, loss: 7.27191, oob acc: 95.45%, # of haplo: 21
12, SNP: 58, loss: 6.70503, oob acc: 95.45%, # of haplo: 27
13, SNP: 243, loss: 2.87079, oob acc: 95.45%, # of haplo: 30
14, SNP: 5, loss: 2.77321, oob acc: 95.45%, # of haplo: 31
[1] 2021-10-15 00:23:48, oob acc: 95.45%, # of SNPs: 14, # of haplo: 31
=== building individual classifier 2, out-of-bag (9/26.5%) ===
1, SNP: 149, loss: 171.797, oob acc: 66.67%, # of haplo: 13
2, SNP: 176, loss: 120.459, oob acc: 72.22%, # of haplo: 14
3, SNP: 97, loss: 80.1731, oob acc: 83.33%, # of haplo: 14
4, SNP: 56, loss: 51.5193, oob acc: 94.44%, # of haplo: 16
5, SNP: 182, loss: 34.5643, oob acc: 94.44%, # of haplo: 18
6, SNP: 121, loss: 23.0259, oob acc: 94.44%, # of haplo: 18
7, SNP: 234, loss: 15.0596, oob acc: 94.44%, # of haplo: 20
8, SNP: 148, loss: 9.66757, oob acc: 94.44%, # of haplo: 20
9, SNP: 19, loss: 4.29975, oob acc: 94.44%, # of haplo: 27
10, SNP: 226, loss: 0.481093, oob acc: 94.44%, # of haplo: 27
11, SNP: 64, loss: 0.447483, oob acc: 94.44%, # of haplo: 28
12, SNP: 240, loss: 0.365545, oob acc: 94.44%, # of haplo: 37
13, SNP: 57, loss: 0.365132, oob acc: 94.44%, # of haplo: 38
[2] 2021-10-15 00:23:48, oob acc: 94.44%, # of SNPs: 13, # of haplo: 38
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 118, loss: 190.304, oob acc: 53.57%, # of haplo: 13
2, SNP: 175, loss: 157.208, oob acc: 60.71%, # of haplo: 15
3, SNP: 103, loss: 128.429, oob acc: 64.29%, # of haplo: 15
4, SNP: 182, loss: 66.6054, oob acc: 71.43%, # of haplo: 15
5, SNP: 152, loss: 58.8041, oob acc: 78.57%, # of haplo: 15
6, SNP: 111, loss: 30.086, oob acc: 82.14%, # of haplo: 15
7, SNP: 130, loss: 15.3177, oob acc: 89.29%, # of haplo: 19
8, SNP: 229, loss: 9.99758, oob acc: 89.29%, # of haplo: 28
9, SNP: 185, loss: 7.40712, oob acc: 89.29%, # of haplo: 29
10, SNP: 199, loss: 6.21341, oob acc: 89.29%, # of haplo: 29
11, SNP: 217, loss: 1.38739, oob acc: 89.29%, # of haplo: 30
[3] 2021-10-15 00:23:48, oob acc: 89.29%, # of SNPs: 11, # of haplo: 30
=== building individual classifier 4, out-of-bag (13/38.2%) ===
1, SNP: 101, loss: 154.355, oob acc: 46.15%, # of haplo: 16
2, SNP: 102, loss: 139.148, oob acc: 61.54%, # of haplo: 22
3, SNP: 132, loss: 95.2502, oob acc: 73.08%, # of haplo: 23
4, SNP: 147, loss: 76.9692, oob acc: 76.92%, # of haplo: 34
5, SNP: 53, loss: 68.3851, oob acc: 88.46%, # of haplo: 51
6, SNP: 186, loss: 41.8787, oob acc: 88.46%, # of haplo: 53
7, SNP: 128, loss: 33.5437, oob acc: 92.31%, # of haplo: 53
8, SNP: 14, loss: 23.3103, oob acc: 92.31%, # of haplo: 55
9, SNP: 219, loss: 18.3628, oob acc: 92.31%, # of haplo: 57
10, SNP: 149, loss: 17.9413, oob acc: 92.31%, # of haplo: 89
11, SNP: 73, loss: 16.3172, oob acc: 92.31%, # of haplo: 90
12, SNP: 70, loss: 16.1056, oob acc: 92.31%, # of haplo: 90
13, SNP: 199, loss: 12.3057, oob acc: 92.31%, # of haplo: 90
14, SNP: 203, loss: 12.2013, oob acc: 92.31%, # of haplo: 90
15, SNP: 151, loss: 11.1795, oob acc: 92.31%, # of haplo: 90
[4] 2021-10-15 00:23:49, oob acc: 92.31%, # of SNPs: 15, # of haplo: 90
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Accuracy with training data: 97.06%
Out-of-bag accuracy: 92.87%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 49
avg. # of SNPs in an individual classifier: 13.25
(sd: 1.71, min: 11, max: 15, median: 13.50)
avg. # of haplotypes in an individual classifier: 47.25
(sd: 28.72, min: 30, max: 90, median: 34.50)
avg. out-of-bag accuracy: 92.87%
(sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:49) 0%
Predicting (2021-10-15 00:23:49) 100%
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
excluding 1 monomorphic SNP
# of SNPs randomly sampled as candidates for each selection: 13
# of SNPs: 158
# of samples: 60
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:49
=== building individual classifier 1, out-of-bag (24/40.0%) ===
1, SNP: 141, loss: 378.06, oob acc: 52.08%, # of haplo: 14
2, SNP: 74, loss: 262.497, oob acc: 58.33%, # of haplo: 15
3, SNP: 78, loss: 162.497, oob acc: 68.75%, # of haplo: 19
4, SNP: 118, loss: 70.0426, oob acc: 72.92%, # of haplo: 23
5, SNP: 82, loss: 45.8279, oob acc: 83.33%, # of haplo: 23
6, SNP: 95, loss: 35.4414, oob acc: 89.58%, # of haplo: 27
7, SNP: 89, loss: 32.6134, oob acc: 89.58%, # of haplo: 35
8, SNP: 83, loss: 31.7921, oob acc: 89.58%, # of haplo: 51
9, SNP: 151, loss: 31.0653, oob acc: 89.58%, # of haplo: 55
10, SNP: 94, loss: 31.0246, oob acc: 89.58%, # of haplo: 55
11, SNP: 111, loss: 18.9027, oob acc: 89.58%, # of haplo: 56
12, SNP: 139, loss: 18.4248, oob acc: 89.58%, # of haplo: 59
13, SNP: 93, loss: 17.0195, oob acc: 91.67%, # of haplo: 58
14, SNP: 15, loss: 14.1692, oob acc: 91.67%, # of haplo: 60
[1] 2021-10-15 00:23:49, oob acc: 91.67%, # of SNPs: 14, # of haplo: 60
=== building individual classifier 2, out-of-bag (19/31.7%) ===
1, SNP: 94, loss: 403.365, oob acc: 63.16%, # of haplo: 15
2, SNP: 82, loss: 294.053, oob acc: 71.05%, # of haplo: 18
3, SNP: 57, loss: 226.142, oob acc: 76.32%, # of haplo: 23
4, SNP: 155, loss: 197.199, oob acc: 84.21%, # of haplo: 29
5, SNP: 44, loss: 132.804, oob acc: 86.84%, # of haplo: 40
6, SNP: 30, loss: 122.507, oob acc: 92.11%, # of haplo: 40
7, SNP: 109, loss: 72.0179, oob acc: 92.11%, # of haplo: 41
8, SNP: 72, loss: 59.3281, oob acc: 92.11%, # of haplo: 41
9, SNP: 36, loss: 54.939, oob acc: 94.74%, # of haplo: 43
10, SNP: 127, loss: 48.1392, oob acc: 94.74%, # of haplo: 43
11, SNP: 53, loss: 44.7676, oob acc: 94.74%, # of haplo: 43
12, SNP: 148, loss: 43.047, oob acc: 94.74%, # of haplo: 44
13, SNP: 152, loss: 40.2104, oob acc: 94.74%, # of haplo: 45
14, SNP: 125, loss: 39.8862, oob acc: 94.74%, # of haplo: 45
15, SNP: 78, loss: 39.5652, oob acc: 94.74%, # of haplo: 45
16, SNP: 3, loss: 39.0621, oob acc: 94.74%, # of haplo: 47
17, SNP: 141, loss: 37.6822, oob acc: 94.74%, # of haplo: 47
18, SNP: 1, loss: 36.5022, oob acc: 94.74%, # of haplo: 50
[2] 2021-10-15 00:23:49, oob acc: 94.74%, # of SNPs: 18, # of haplo: 50
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02
Max. Mean SD
4.790185e-01 5.479747e-02 1.101559e-01
Accuracy with training data: 96.67%
Out-of-bag accuracy: 93.20%
Gene: HLA-A
Training dataset: 60 samples X 158 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 28
avg. # of SNPs in an individual classifier: 16.00
(sd: 2.83, min: 14, max: 18, median: 16.00)
avg. # of haplotypes in an individual classifier: 55.00
(sd: 7.07, min: 50, max: 60, median: 55.00)
avg. out-of-bag accuracy: 93.20%
(sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02
Max. Mean SD
4.790185e-01 5.479747e-02 1.101559e-01
Genome assembly: hg19
Remove 130 unused SNPs.
Gene: HLA-A
Training dataset: 60 samples X 28 SNPs
# of HLA alleles: 14
# of individual classifiers: 2
total # of SNPs used: 28
avg. # of SNPs in an individual classifier: 16.00
(sd: 2.83, min: 14, max: 18, median: 16.00)
avg. # of haplotypes in an individual classifier: 55.00
(sd: 7.07, min: 50, max: 60, median: 55.00)
avg. out-of-bag accuracy: 93.20%
(sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02
Max. Mean SD
4.790185e-01 5.479747e-02 1.101559e-01
Genome assembly: hg19
Platform: Illumina 1M Duo
Information: Training set -- HapMap Phase II
HIBAG model for HLA-A:
2 individual classifiers
158 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 60
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:49) 0%
Predicting (2021-10-15 00:23:49) 100%
HIBAG model for HLA-A:
2 individual classifiers
28 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: Illumina 1M Duo
No allelic strand or A/B allele is flipped.
# of samples: 60
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:49) 0%
Predicting (2021-10-15 00:23:49) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:49
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 235, loss: 208.912, oob acc: 59.09%, # of haplo: 14
2, SNP: 136, loss: 139.316, oob acc: 63.64%, # of haplo: 14
3, SNP: 126, loss: 96.148, oob acc: 72.73%, # of haplo: 14
4, SNP: 89, loss: 76.2917, oob acc: 77.27%, # of haplo: 15
5, SNP: 102, loss: 61.3783, oob acc: 86.36%, # of haplo: 15
6, SNP: 105, loss: 49.1567, oob acc: 90.91%, # of haplo: 15
7, SNP: 117, loss: 43.0927, oob acc: 95.45%, # of haplo: 15
8, SNP: 259, loss: 26.6243, oob acc: 95.45%, # of haplo: 17
9, SNP: 60, loss: 17.6253, oob acc: 95.45%, # of haplo: 19
10, SNP: 236, loss: 9.50329, oob acc: 95.45%, # of haplo: 20
11, SNP: 94, loss: 7.27191, oob acc: 95.45%, # of haplo: 21
12, SNP: 58, loss: 6.70503, oob acc: 95.45%, # of haplo: 27
13, SNP: 243, loss: 2.87079, oob acc: 95.45%, # of haplo: 30
14, SNP: 5, loss: 2.77321, oob acc: 95.45%, # of haplo: 31
[1] 2021-10-15 00:23:49, oob acc: 95.45%, # of SNPs: 14, # of haplo: 31
=== building individual classifier 2, out-of-bag (9/26.5%) ===
1, SNP: 149, loss: 171.797, oob acc: 66.67%, # of haplo: 13
2, SNP: 176, loss: 120.459, oob acc: 72.22%, # of haplo: 14
3, SNP: 97, loss: 80.1731, oob acc: 83.33%, # of haplo: 14
4, SNP: 56, loss: 51.5193, oob acc: 94.44%, # of haplo: 16
5, SNP: 182, loss: 34.5643, oob acc: 94.44%, # of haplo: 18
6, SNP: 121, loss: 23.0259, oob acc: 94.44%, # of haplo: 18
7, SNP: 234, loss: 15.0596, oob acc: 94.44%, # of haplo: 20
8, SNP: 148, loss: 9.66757, oob acc: 94.44%, # of haplo: 20
9, SNP: 19, loss: 4.29975, oob acc: 94.44%, # of haplo: 27
10, SNP: 226, loss: 0.481093, oob acc: 94.44%, # of haplo: 27
11, SNP: 64, loss: 0.447483, oob acc: 94.44%, # of haplo: 28
12, SNP: 240, loss: 0.365545, oob acc: 94.44%, # of haplo: 37
13, SNP: 57, loss: 0.365132, oob acc: 94.44%, # of haplo: 38
[2] 2021-10-15 00:23:49, oob acc: 94.44%, # of SNPs: 13, # of haplo: 38
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 118, loss: 190.304, oob acc: 53.57%, # of haplo: 13
2, SNP: 175, loss: 157.208, oob acc: 60.71%, # of haplo: 15
3, SNP: 103, loss: 128.429, oob acc: 64.29%, # of haplo: 15
4, SNP: 182, loss: 66.6054, oob acc: 71.43%, # of haplo: 15
5, SNP: 152, loss: 58.8041, oob acc: 78.57%, # of haplo: 15
6, SNP: 111, loss: 30.086, oob acc: 82.14%, # of haplo: 15
7, SNP: 130, loss: 15.3177, oob acc: 89.29%, # of haplo: 19
8, SNP: 229, loss: 9.99758, oob acc: 89.29%, # of haplo: 28
9, SNP: 185, loss: 7.40712, oob acc: 89.29%, # of haplo: 29
10, SNP: 199, loss: 6.21341, oob acc: 89.29%, # of haplo: 29
11, SNP: 217, loss: 1.38739, oob acc: 89.29%, # of haplo: 30
[3] 2021-10-15 00:23:49, oob acc: 89.29%, # of SNPs: 11, # of haplo: 30
=== building individual classifier 4, out-of-bag (13/38.2%) ===
1, SNP: 101, loss: 154.355, oob acc: 46.15%, # of haplo: 16
2, SNP: 102, loss: 139.148, oob acc: 61.54%, # of haplo: 22
3, SNP: 132, loss: 95.2502, oob acc: 73.08%, # of haplo: 23
4, SNP: 147, loss: 76.9692, oob acc: 76.92%, # of haplo: 34
5, SNP: 53, loss: 68.3851, oob acc: 88.46%, # of haplo: 51
6, SNP: 186, loss: 41.8787, oob acc: 88.46%, # of haplo: 53
7, SNP: 128, loss: 33.5437, oob acc: 92.31%, # of haplo: 53
8, SNP: 14, loss: 23.3103, oob acc: 92.31%, # of haplo: 55
9, SNP: 219, loss: 18.3628, oob acc: 92.31%, # of haplo: 57
10, SNP: 149, loss: 17.9413, oob acc: 92.31%, # of haplo: 89
11, SNP: 73, loss: 16.3172, oob acc: 92.31%, # of haplo: 90
12, SNP: 70, loss: 16.1056, oob acc: 92.31%, # of haplo: 90
13, SNP: 199, loss: 12.3057, oob acc: 92.31%, # of haplo: 90
14, SNP: 203, loss: 12.2013, oob acc: 92.31%, # of haplo: 90
15, SNP: 151, loss: 11.1795, oob acc: 92.31%, # of haplo: 90
[4] 2021-10-15 00:23:49, oob acc: 92.31%, # of SNPs: 15, # of haplo: 90
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Accuracy with training data: 97.06%
Out-of-bag accuracy: 92.87%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 49
avg. # of SNPs in an individual classifier: 13.25
(sd: 1.71, min: 11, max: 15, median: 13.50)
avg. # of haplotypes in an individual classifier: 47.25
(sd: 28.72, min: 30, max: 90, median: 34.50)
avg. out-of-bag accuracy: 92.87%
(sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:49) 0%
Predicting (2021-10-15 00:23:49) 100%
Allele Num. Freq. Num. Freq. CR ACC SEN SPE PPV NPV Miscall
Train Train Valid. Valid. (%) (%) (%) (%) (%) (%) (%)
----
Overall accuracy: 88.5%, Call rate: 100.0%
01:01 13 0.1912 12 0.2308 100.0 96.2 100.0 95.0 85.7 100.0 --
02:01 25 0.3676 18 0.3462 100.0 98.1 94.4 100.0 100.0 97.1 01:01 (100)
02:06 1 0.0147 0 0 -- -- -- -- -- -- --
03:01 4 0.0588 5 0.0962 100.0 98.1 100.0 97.9 83.3 100.0 --
11:01 2 0.0294 3 0.0577 100.0 100.0 100.0 100.0 100.0 100.0 --
23:01 1 0.0147 2 0.0385 100.0 96.2 0.0 100.0 -- 96.2 24:02 (100)
24:02 6 0.0882 5 0.0962 100.0 92.3 60.0 95.7 60.0 95.7 01:01 (50)
24:03 1 0.0147 0 0 -- -- -- -- -- -- --
25:01 4 0.0588 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
26:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
29:02 3 0.0441 1 0.0192 100.0 98.1 0.0 100.0 -- 98.1 03:01 (50)
31:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
32:01 2 0.0294 2 0.0385 100.0 100.0 100.0 100.0 100.0 100.0 --
68:01 2 0.0294 1 0.0192 100.0 98.1 100.0 98.0 50.0 100.0 --
\title{Imputation Evaluation}
\documentclass[12pt]{article}
\usepackage{fullpage}
\usepackage{longtable}
\begin{document}
\maketitle
\setlength{\LTcapwidth}{6.5in}
% -------- BEGIN TABLE --------
\begin{longtable}{rrrrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{12}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
& Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{12}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{12}{l}{\it Overall accuracy: 88.5\%, Call rate: 100.0\%} \\
01:01 & 13 & 0.1912 & 12 & 0.2308 & 100.0 & 96.2 & 100.0 & 95.0 & 85.7 & 100.0 & -- \\
02:01 & 25 & 0.3676 & 18 & 0.3462 & 100.0 & 98.1 & 94.4 & 100.0 & 100.0 & 97.1 & 01:01 (100) \\
02:06 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
03:01 & 4 & 0.0588 & 5 & 0.0962 & 100.0 & 98.1 & 100.0 & 97.9 & 83.3 & 100.0 & -- \\
11:01 & 2 & 0.0294 & 3 & 0.0577 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
23:01 & 1 & 0.0147 & 2 & 0.0385 & 100.0 & 96.2 & 0.0 & 100.0 & -- & 96.2 & 24:02 (100) \\
24:02 & 6 & 0.0882 & 5 & 0.0962 & 100.0 & 92.3 & 60.0 & 95.7 & 60.0 & 95.7 & 01:01 (50) \\
24:03 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
25:01 & 4 & 0.0588 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
26:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
29:02 & 3 & 0.0441 & 1 & 0.0192 & 100.0 & 98.1 & 0.0 & 100.0 & -- & 98.1 & 03:01 (50) \\
31:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
32:01 & 2 & 0.0294 & 2 & 0.0385 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 100.0 & 98.0 & 50.0 & 100.0 & -- \\
\end{longtable}
% -------- END TABLE --------
\end{document}
<!DOCTYPE html>
<html>
<head>
<title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1" CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Train</th> <th>Freq. Train</th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="12">
<i> Overall accuracy: 88.5%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>13</td> <td>0.1912</td> <td>12</td> <td>0.2308</td> <td>100.0</td> <td>96.2</td> <td>100.0</td> <td>95.0</td> <td>85.7</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>25</td> <td>0.3676</td> <td>18</td> <td>0.3462</td> <td>100.0</td> <td>98.1</td> <td>94.4</td> <td>100.0</td> <td>100.0</td> <td>97.1</td> <td>01:01 (100)</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>03:01</td> <td>4</td> <td>0.0588</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>97.9</td> <td>83.3</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>11:01</td> <td>2</td> <td>0.0294</td> <td>3</td> <td>0.0577</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>1</td> <td>0.0147</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>96.2</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>96.2</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>24:02</td> <td>6</td> <td>0.0882</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>92.3</td> <td>60.0</td> <td>95.7</td> <td>60.0</td> <td>95.7</td> <td>01:01 (50)</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>25:01</td> <td>4</td> <td>0.0588</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>26:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>29:02</td> <td>3</td> <td>0.0441</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>98.1</td> <td>03:01 (50)</td>
</tr>
<tr>
<td>31:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>32:01</td> <td>2</td> <td>0.0294</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>98.0</td> <td>50.0</td> <td>100.0</td> <td>--</td>
</tr>
</table>
</body>
</html>
**Overall accuracy: 88.5%, Call rate: 100.0%**
| Allele | # Train | Freq. Train | # Valid. | Freq. Valid. | CR (%) | ACC (%) | SEN (%) | SPE (%) | PPV (%) | NPV (%) | Miscall (%) |
|:--|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|:--|
| 01:01 | 13 | 0.1912 | 12 | 0.2308 | 100.0 | 96.2 | 100.0 | 95.0 | 85.7 | 100.0 | -- |
| 02:01 | 25 | 0.3676 | 18 | 0.3462 | 100.0 | 98.1 | 94.4 | 100.0 | 100.0 | 97.1 | 01:01 (100) |
| 02:06 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 03:01 | 4 | 0.0588 | 5 | 0.0962 | 100.0 | 98.1 | 100.0 | 97.9 | 83.3 | 100.0 | -- |
| 11:01 | 2 | 0.0294 | 3 | 0.0577 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 23:01 | 1 | 0.0147 | 2 | 0.0385 | 100.0 | 96.2 | 0.0 | 100.0 | -- | 96.2 | 24:02 (100) |
| 24:02 | 6 | 0.0882 | 5 | 0.0962 | 100.0 | 92.3 | 60.0 | 95.7 | 60.0 | 95.7 | 01:01 (50) |
| 24:03 | 1 | 0.0147 | 0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 25:01 | 4 | 0.0588 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 26:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 29:02 | 3 | 0.0441 | 1 | 0.0192 | 100.0 | 98.1 | 0.0 | 100.0 | -- | 98.1 | 03:01 (50) |
| 31:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 32:01 | 2 | 0.0294 | 2 | 0.0385 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 68:01 | 2 | 0.0294 | 1 | 0.0192 | 100.0 | 98.1 | 100.0 | 98.0 | 50.0 | 100.0 | -- |
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 4 individual classifiers:
MAF threshold: NaN
excluding 11 monomorphic SNPs
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 264
# of samples: 34
# of unique HLA alleles: 14
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:49
=== building individual classifier 1, out-of-bag (11/32.4%) ===
1, SNP: 235, loss: 208.912, oob acc: 59.09%, # of haplo: 14
2, SNP: 136, loss: 139.316, oob acc: 63.64%, # of haplo: 14
3, SNP: 126, loss: 96.148, oob acc: 72.73%, # of haplo: 14
4, SNP: 89, loss: 76.2917, oob acc: 77.27%, # of haplo: 15
5, SNP: 102, loss: 61.3783, oob acc: 86.36%, # of haplo: 15
6, SNP: 105, loss: 49.1567, oob acc: 90.91%, # of haplo: 15
7, SNP: 117, loss: 43.0927, oob acc: 95.45%, # of haplo: 15
8, SNP: 259, loss: 26.6243, oob acc: 95.45%, # of haplo: 17
9, SNP: 60, loss: 17.6253, oob acc: 95.45%, # of haplo: 19
10, SNP: 236, loss: 9.50329, oob acc: 95.45%, # of haplo: 20
11, SNP: 94, loss: 7.27191, oob acc: 95.45%, # of haplo: 21
12, SNP: 58, loss: 6.70503, oob acc: 95.45%, # of haplo: 27
13, SNP: 243, loss: 2.87079, oob acc: 95.45%, # of haplo: 30
14, SNP: 5, loss: 2.77321, oob acc: 95.45%, # of haplo: 31
[1] 2021-10-15 00:23:49, oob acc: 95.45%, # of SNPs: 14, # of haplo: 31
=== building individual classifier 2, out-of-bag (9/26.5%) ===
1, SNP: 149, loss: 171.797, oob acc: 66.67%, # of haplo: 13
2, SNP: 176, loss: 120.459, oob acc: 72.22%, # of haplo: 14
3, SNP: 97, loss: 80.1731, oob acc: 83.33%, # of haplo: 14
4, SNP: 56, loss: 51.5193, oob acc: 94.44%, # of haplo: 16
5, SNP: 182, loss: 34.5643, oob acc: 94.44%, # of haplo: 18
6, SNP: 121, loss: 23.0259, oob acc: 94.44%, # of haplo: 18
7, SNP: 234, loss: 15.0596, oob acc: 94.44%, # of haplo: 20
8, SNP: 148, loss: 9.66757, oob acc: 94.44%, # of haplo: 20
9, SNP: 19, loss: 4.29975, oob acc: 94.44%, # of haplo: 27
10, SNP: 226, loss: 0.481093, oob acc: 94.44%, # of haplo: 27
11, SNP: 64, loss: 0.447483, oob acc: 94.44%, # of haplo: 28
12, SNP: 240, loss: 0.365545, oob acc: 94.44%, # of haplo: 37
13, SNP: 57, loss: 0.365132, oob acc: 94.44%, # of haplo: 38
[2] 2021-10-15 00:23:49, oob acc: 94.44%, # of SNPs: 13, # of haplo: 38
=== building individual classifier 3, out-of-bag (14/41.2%) ===
1, SNP: 118, loss: 190.304, oob acc: 53.57%, # of haplo: 13
2, SNP: 175, loss: 157.208, oob acc: 60.71%, # of haplo: 15
3, SNP: 103, loss: 128.429, oob acc: 64.29%, # of haplo: 15
4, SNP: 182, loss: 66.6054, oob acc: 71.43%, # of haplo: 15
5, SNP: 152, loss: 58.8041, oob acc: 78.57%, # of haplo: 15
6, SNP: 111, loss: 30.086, oob acc: 82.14%, # of haplo: 15
7, SNP: 130, loss: 15.3177, oob acc: 89.29%, # of haplo: 19
8, SNP: 229, loss: 9.99758, oob acc: 89.29%, # of haplo: 28
9, SNP: 185, loss: 7.40712, oob acc: 89.29%, # of haplo: 29
10, SNP: 199, loss: 6.21341, oob acc: 89.29%, # of haplo: 29
11, SNP: 217, loss: 1.38739, oob acc: 89.29%, # of haplo: 30
[3] 2021-10-15 00:23:49, oob acc: 89.29%, # of SNPs: 11, # of haplo: 30
=== building individual classifier 4, out-of-bag (13/38.2%) ===
1, SNP: 101, loss: 154.355, oob acc: 46.15%, # of haplo: 16
2, SNP: 102, loss: 139.148, oob acc: 61.54%, # of haplo: 22
3, SNP: 132, loss: 95.2502, oob acc: 73.08%, # of haplo: 23
4, SNP: 147, loss: 76.9692, oob acc: 76.92%, # of haplo: 34
5, SNP: 53, loss: 68.3851, oob acc: 88.46%, # of haplo: 51
6, SNP: 186, loss: 41.8787, oob acc: 88.46%, # of haplo: 53
7, SNP: 128, loss: 33.5437, oob acc: 92.31%, # of haplo: 53
8, SNP: 14, loss: 23.3103, oob acc: 92.31%, # of haplo: 55
9, SNP: 219, loss: 18.3628, oob acc: 92.31%, # of haplo: 57
10, SNP: 149, loss: 17.9413, oob acc: 92.31%, # of haplo: 89
11, SNP: 73, loss: 16.3172, oob acc: 92.31%, # of haplo: 90
12, SNP: 70, loss: 16.1056, oob acc: 92.31%, # of haplo: 90
13, SNP: 199, loss: 12.3057, oob acc: 92.31%, # of haplo: 90
14, SNP: 203, loss: 12.2013, oob acc: 92.31%, # of haplo: 90
15, SNP: 151, loss: 11.1795, oob acc: 92.31%, # of haplo: 90
[4] 2021-10-15 00:23:49, oob acc: 92.31%, # of SNPs: 15, # of haplo: 90
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Accuracy with training data: 97.06%
Out-of-bag accuracy: 92.87%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
# of HLA alleles: 14
# of individual classifiers: 4
total # of SNPs used: 49
avg. # of SNPs in an individual classifier: 13.25
(sd: 1.71, min: 11, max: 15, median: 13.50)
avg. # of haplotypes in an individual classifier: 47.25
(sd: 28.72, min: 30, max: 90, median: 34.50)
avg. out-of-bag accuracy: 92.87%
(sd: 2.73%, min: 89.29%, max: 95.45%, median: 93.38%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
0.0004055642 0.0004189435 0.0005393566 0.0035332670 0.0110835407 0.0312705424
Max. Mean SD
0.5148772297 0.0357753361 0.0879935706
Genome assembly: hg19
HIBAG model for HLA-A:
4 individual classifiers
264 SNPs
14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
match.type="--" missing SNPs #
Position 0 (0.0%) *being used [1]
Pos+Allele 0 (0.0%) [2]
RefSNP+Position 0 (0.0%)
RefSNP 0 (0.0%)
[1]: useful if ambiguous strands on array-based platforms
[2]: suggested if the model and test data have been matched to the same reference genome
Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 32-bit, AVX2
# of threads: 1
Predicting (2021-10-15 00:23:49) 0%
Predicting (2021-10-15 00:23:49) 100%
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 17
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 8
# of SNPs: 51
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:50
=== building individual classifier 1, out-of-bag (24/40.0%) ===
1, SNP: 13, loss: 391.274, oob acc: 41.67%, # of haplo: 17
2, SNP: 2, loss: 321.685, oob acc: 52.08%, # of haplo: 18
3, SNP: 36, loss: 232.846, oob acc: 58.33%, # of haplo: 19
4, SNP: 28, loss: 178.077, oob acc: 62.50%, # of haplo: 20
5, SNP: 35, loss: 107.151, oob acc: 68.75%, # of haplo: 20
6, SNP: 3, loss: 72.2736, oob acc: 72.92%, # of haplo: 23
7, SNP: 19, loss: 50.8439, oob acc: 77.08%, # of haplo: 25
8, SNP: 4, loss: 47.2744, oob acc: 83.33%, # of haplo: 29
9, SNP: 42, loss: 47.0092, oob acc: 85.42%, # of haplo: 37
10, SNP: 33, loss: 41.5486, oob acc: 85.42%, # of haplo: 41
11, SNP: 5, loss: 39.769, oob acc: 85.42%, # of haplo: 51
12, SNP: 10, loss: 34.0977, oob acc: 85.42%, # of haplo: 51
13, SNP: 37, loss: 32.3969, oob acc: 85.42%, # of haplo: 52
14, SNP: 7, loss: 28.1492, oob acc: 85.42%, # of haplo: 52
15, SNP: 15, loss: 27.2163, oob acc: 85.42%, # of haplo: 55
[1] 2021-10-15 00:23:50, oob acc: 85.42%, # of SNPs: 15, # of haplo: 55
=== building individual classifier 2, out-of-bag (17/28.3%) ===
1, SNP: 18, loss: 453.852, oob acc: 44.12%, # of haplo: 17
2, SNP: 4, loss: 358.517, oob acc: 50.00%, # of haplo: 18
3, SNP: 49, loss: 258.495, oob acc: 52.94%, # of haplo: 18
4, SNP: 5, loss: 172.555, oob acc: 67.65%, # of haplo: 21
5, SNP: 42, loss: 144.905, oob acc: 76.47%, # of haplo: 21
6, SNP: 38, loss: 98.7462, oob acc: 79.41%, # of haplo: 21
7, SNP: 36, loss: 83.4743, oob acc: 82.35%, # of haplo: 24
8, SNP: 19, loss: 60.2385, oob acc: 88.24%, # of haplo: 24
9, SNP: 46, loss: 49.1775, oob acc: 88.24%, # of haplo: 24
10, SNP: 20, loss: 42.3205, oob acc: 88.24%, # of haplo: 24
11, SNP: 12, loss: 41.1299, oob acc: 91.18%, # of haplo: 25
12, SNP: 1, loss: 33.8332, oob acc: 91.18%, # of haplo: 25
13, SNP: 37, loss: 32.8313, oob acc: 91.18%, # of haplo: 26
14, SNP: 7, loss: 38.8398, oob acc: 94.12%, # of haplo: 27
15, SNP: 15, loss: 35.0817, oob acc: 94.12%, # of haplo: 32
16, SNP: 39, loss: 33.7063, oob acc: 94.12%, # of haplo: 30
[2] 2021-10-15 00:23:50, oob acc: 94.12%, # of SNPs: 16, # of haplo: 30
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
3.464062e-10 4.686489e-10 1.568833e-09 3.197938e-03 1.266674e-02 3.773631e-02
Max. Mean SD
9.739941e-02 2.429599e-02 2.696412e-02
Accuracy with training data: 95.83%
Out-of-bag accuracy: 89.77%
Gene: HLA-C
Training dataset: 60 samples X 51 SNPs
# of HLA alleles: 17
# of individual classifiers: 2
total # of SNPs used: 23
avg. # of SNPs in an individual classifier: 15.50
(sd: 0.71, min: 15, max: 16, median: 15.50)
avg. # of haplotypes in an individual classifier: 42.50
(sd: 17.68, min: 30, max: 55, median: 42.50)
avg. out-of-bag accuracy: 89.77%
(sd: 6.15%, min: 85.42%, max: 94.12%, median: 89.77%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
3.464062e-10 4.686489e-10 1.568833e-09 3.197938e-03 1.266674e-02 3.773631e-02
Max. Mean SD
9.739941e-02 2.429599e-02 2.696412e-02
Genome assembly: hg19
Gene: HLA-C
Training dataset: 60 samples X 51 SNPs
# of HLA alleles: 17
# of individual classifiers: 1
total # of SNPs used: 15
avg. # of SNPs in an individual classifier: 15.00
(sd: NA, min: 15, max: 15, median: 15.00)
avg. # of haplotypes in an individual classifier: 55.00
(sd: NA, min: 55, max: 55, median: 55.00)
avg. out-of-bag accuracy: 85.42%
(sd: NA%, min: 85.42%, max: 85.42%, median: 85.42%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
3.464062e-10 4.686489e-10 1.568833e-09 3.197938e-03 1.266674e-02 3.773631e-02
Max. Mean SD
9.739941e-02 2.429599e-02 2.696412e-02
Genome assembly: hg19
Gene: HLA-A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:50
=== building individual classifier 1, out-of-bag (24/40.0%) ===
1, SNP: 44, loss: 396.506, oob acc: 41.67%, # of haplo: 17
2, SNP: 58, loss: 291.148, oob acc: 50.00%, # of haplo: 17
3, SNP: 80, loss: 211.469, oob acc: 56.25%, # of haplo: 20
4, SNP: 18, loss: 138.615, oob acc: 60.42%, # of haplo: 20
5, SNP: 29, loss: 111.977, oob acc: 62.50%, # of haplo: 22
6, SNP: 62, loss: 90.976, oob acc: 68.75%, # of haplo: 24
7, SNP: 13, loss: 70.2962, oob acc: 72.92%, # of haplo: 24
8, SNP: 14, loss: 54.5685, oob acc: 77.08%, # of haplo: 22
9, SNP: 72, loss: 35.1951, oob acc: 77.08%, # of haplo: 24
10, SNP: 3, loss: 23.2868, oob acc: 79.17%, # of haplo: 24
11, SNP: 70, loss: 21.089, oob acc: 79.17%, # of haplo: 28
12, SNP: 5, loss: 20.9664, oob acc: 79.17%, # of haplo: 29
13, SNP: 40, loss: 20.9662, oob acc: 83.33%, # of haplo: 37
14, SNP: 24, loss: 20.2385, oob acc: 85.42%, # of haplo: 38
15, SNP: 2, loss: 20.2383, oob acc: 87.50%, # of haplo: 39
16, SNP: 74, loss: 20.0601, oob acc: 87.50%, # of haplo: 40
17, SNP: 57, loss: 17.8846, oob acc: 87.50%, # of haplo: 41
18, SNP: 56, loss: 14.7377, oob acc: 89.58%, # of haplo: 43
19, SNP: 27, loss: 10.7709, oob acc: 89.58%, # of haplo: 43
[1] 2021-10-15 00:23:50, oob acc: 89.58%, # of SNPs: 19, # of haplo: 43
=== building individual classifier 2, out-of-bag (17/28.3%) ===
1, SNP: 66, loss: 434.369, oob acc: 44.12%, # of haplo: 19
2, SNP: 28, loss: 337.451, oob acc: 58.82%, # of haplo: 21
3, SNP: 30, loss: 302.194, oob acc: 73.53%, # of haplo: 21
4, SNP: 59, loss: 209.932, oob acc: 73.53%, # of haplo: 21
5, SNP: 69, loss: 146.631, oob acc: 82.35%, # of haplo: 21
6, SNP: 73, loss: 96.4111, oob acc: 91.18%, # of haplo: 21
7, SNP: 70, loss: 81.5466, oob acc: 91.18%, # of haplo: 21
8, SNP: 3, loss: 71.8294, oob acc: 91.18%, # of haplo: 22
9, SNP: 5, loss: 66.5825, oob acc: 94.12%, # of haplo: 23
10, SNP: 27, loss: 46.6959, oob acc: 94.12%, # of haplo: 23
11, SNP: 72, loss: 39.0572, oob acc: 94.12%, # of haplo: 23
12, SNP: 6, loss: 35.0674, oob acc: 94.12%, # of haplo: 24
13, SNP: 78, loss: 34.8741, oob acc: 94.12%, # of haplo: 32
14, SNP: 82, loss: 33.4558, oob acc: 94.12%, # of haplo: 38
15, SNP: 57, loss: 30.709, oob acc: 94.12%, # of haplo: 41
16, SNP: 32, loss: 26.6513, oob acc: 94.12%, # of haplo: 42
17, SNP: 23, loss: 26.6236, oob acc: 94.12%, # of haplo: 46
18, SNP: 2, loss: 25.7938, oob acc: 94.12%, # of haplo: 56
[2] 2021-10-15 00:23:50, oob acc: 94.12%, # of SNPs: 18, # of haplo: 56
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.736136e-08 5.138167e-06 5.122542e-05 1.574056e-03 6.457411e-03 3.142989e-02
Max. Mean SD
8.812257e-02 1.848522e-02 2.222954e-02
Accuracy with training data: 96.67%
Out-of-bag accuracy: 91.85%
Build a HIBAG model with 2 individual classifiers:
MAF threshold: NaN
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83
# of samples: 60
# of unique HLA alleles: 17
CPU flags: 32-bit, AVX2
# of threads: 1
[-] 2021-10-15 00:23:50
=== building individual classifier 1, out-of-bag (24/40.0%) ===
1, SNP: 44, loss: 396.506, oob acc: 41.67%, # of haplo: 17
2, SNP: 58, loss: 291.148, oob acc: 50.00%, # of haplo: 17
3, SNP: 80, loss: 211.469, oob acc: 56.25%, # of haplo: 20
4, SNP: 18, loss: 138.615, oob acc: 60.42%, # of haplo: 20
5, SNP: 29, loss: 111.977, oob acc: 62.50%, # of haplo: 22
6, SNP: 62, loss: 90.976, oob acc: 68.75%, # of haplo: 24
7, SNP: 13, loss: 70.2962, oob acc: 72.92%, # of haplo: 24
8, SNP: 14, loss: 54.5685, oob acc: 77.08%, # of haplo: 22
9, SNP: 72, loss: 35.1951, oob acc: 77.08%, # of haplo: 24
10, SNP: 3, loss: 23.2868, oob acc: 79.17%, # of haplo: 24
11, SNP: 70, loss: 21.089, oob acc: 79.17%, # of haplo: 28
12, SNP: 5, loss: 20.9664, oob acc: 79.17%, # of haplo: 29
13, SNP: 40, loss: 20.9662, oob acc: 83.33%, # of haplo: 37
14, SNP: 24, loss: 20.2385, oob acc: 85.42%, # of haplo: 38
15, SNP: 2, loss: 20.2383, oob acc: 87.50%, # of haplo: 39
16, SNP: 74, loss: 20.0601, oob acc: 87.50%, # of haplo: 40
17, SNP: 57, loss: 17.8846, oob acc: 87.50%, # of haplo: 41
18, SNP: 56, loss: 14.7377, oob acc: 89.58%, # of haplo: 43
19, SNP: 27, loss: 10.7709, oob acc: 89.58%, # of haplo: 43
[1] 2021-10-15 00:23:50, oob acc: 89.58%, # of SNPs: 19, # of haplo: 43
=== building individual classifier 2, out-of-bag (17/28.3%) ===
1, SNP: 66, loss: 434.369, oob acc: 44.12%, # of haplo: 19
2, SNP: 28, loss: 337.451, oob acc: 58.82%, # of haplo: 21
3, SNP: 30, loss: 302.194, oob acc: 73.53%, # of haplo: 21
4, SNP: 59, loss: 209.932, oob acc: 73.53%, # of haplo: 21
5, SNP: 69, loss: 146.631, oob acc: 82.35%, # of haplo: 21
6, SNP: 73, loss: 96.4111, oob acc: 91.18%, # of haplo: 21
7, SNP: 70, loss: 81.5466, oob acc: 91.18%, # of haplo: 21
8, SNP: 3, loss: 71.8294, oob acc: 91.18%, # of haplo: 22
9, SNP: 5, loss: 66.5825, oob acc: 94.12%, # of haplo: 23
10, SNP: 27, loss: 46.6959, oob acc: 94.12%, # of haplo: 23
11, SNP: 72, loss: 39.0572, oob acc: 94.12%, # of haplo: 23
12, SNP: 6, loss: 35.0674, oob acc: 94.12%, # of haplo: 24
13, SNP: 78, loss: 34.8741, oob acc: 94.12%, # of haplo: 32
14, SNP: 82, loss: 33.4558, oob acc: 94.12%, # of haplo: 38
15, SNP: 57, loss: 30.709, oob acc: 94.12%, # of haplo: 41
16, SNP: 32, loss: 26.6513, oob acc: 94.12%, # of haplo: 42
17, SNP: 23, loss: 26.6236, oob acc: 94.12%, # of haplo: 46
18, SNP: 2, loss: 25.7938, oob acc: 94.12%, # of haplo: 56
[2] 2021-10-15 00:23:50, oob acc: 94.12%, # of SNPs: 18, # of haplo: 56
Calculating matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.736136e-08 5.138167e-06 5.122542e-05 1.574056e-03 6.457411e-03 3.142989e-02
Max. Mean SD
8.812257e-02 1.848522e-02 2.222954e-02
Accuracy with training data: 96.67%
Out-of-bag accuracy: 91.85%
Gene: HLA-C
Training dataset: 60 samples X 83 SNPs
# of HLA alleles: 17
# of individual classifiers: 2
total # of SNPs used: 30
avg. # of SNPs in an individual classifier: 18.50
(sd: 0.71, min: 18, max: 19, median: 18.50)
avg. # of haplotypes in an individual classifier: 49.50
(sd: 9.19, min: 43, max: 56, median: 49.50)
avg. out-of-bag accuracy: 91.85%
(sd: 3.21%, min: 89.58%, max: 94.12%, median: 91.85%)
Matching proportion:
Min. 0.1% Qu. 1% Qu. 1st Qu. Median 3rd Qu.
1.736136e-08 5.138167e-06 5.122542e-05 1.574056e-03 6.457411e-03 3.142989e-02
Max. Mean SD
8.812257e-02 1.848522e-02 2.222954e-02
Genome assembly: hg19
SNP genotypes:
60 samples X 1564 SNPs
SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C
655 632 141 136
>
> proc.time()
user system elapsed
17.14 0.39 19.17
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HIBAG.Rcheck/examples_i386/HIBAG-Ex.timings
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HIBAG.Rcheck/examples_x64/HIBAG-Ex.timings
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