Back to Multiple platform build/check report for BioC 3.14
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This page was generated on 2021-10-23 14:06:25 -0400 (Sat, 23 Oct 2021).

CHECK results for edgeR on riesling1

To the developers/maintainers of the edgeR package:
- Please allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/edgeR.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.

raw results

Package 566/2083HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
edgeR 3.35.3  (landing page)
Yunshun Chen , Gordon Smyth , Aaron Lun , Mark Robinson
Snapshot Date: 2021-10-22 14:50:11 -0400 (Fri, 22 Oct 2021)
git_url: https://git.bioconductor.org/packages/edgeR
git_branch: master
git_last_commit: 6bab998
git_last_commit_date: 2021-10-20 18:57:05 -0400 (Wed, 20 Oct 2021)
nebbiolo2Linux (Ubuntu 20.04.2 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
riesling1Windows Server 2019 Standard / x64  OK    OK    OK    OK  UNNEEDED, same version is already published
merida1macOS 10.14.6 Mojave / x86_64  OK    OK    OK    OK  YES

Summary

Package: edgeR
Version: 3.35.3
Command: D:\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:edgeR.install-out.txt --library=D:\biocbuild\bbs-3.14-bioc\R\library --no-vignettes --timings edgeR_3.35.3.tar.gz
StartedAt: 2021-10-23 05:57:31 -0400 (Sat, 23 Oct 2021)
EndedAt: 2021-10-23 06:00:16 -0400 (Sat, 23 Oct 2021)
EllapsedTime: 164.7 seconds
RetCode: 0
Status:   OK  
CheckDir: edgeR.Rcheck
Warnings: 0

Command output

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###
### Running command:
###
###   D:\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:edgeR.install-out.txt --library=D:\biocbuild\bbs-3.14-bioc\R\library --no-vignettes --timings edgeR_3.35.3.tar.gz
###
##############################################################################
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* using log directory 'D:/biocbuild/bbs-3.14-bioc/meat/edgeR.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 'edgeR/DESCRIPTION' ... OK
* this is package 'edgeR' version '3.35.3'
* 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 'edgeR' can be installed ... OK
* checking installed package size ... NOTE
  installed size is  6.1Mb
  sub-directories of 1Mb or more:
    doc    1.6Mb
    libs   2.4Mb
* 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 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 ... OK
* 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 'D:/biocbuild/bbs-3.14-bioc/R/library/edgeR/libs/i386/edgeR.dll':
  Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran)
  Found 'exit', possibly from 'exit' (C), 'stop' (Fortran)
  Found 'printf', possibly from 'printf' (C)
File 'D:/biocbuild/bbs-3.14-bioc/R/library/edgeR/libs/x64/edgeR.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
** running examples for arch 'x64' ... OK
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
** running tests for arch 'i386' ...
  Running 'edgeR-Tests.R'
  Comparing 'edgeR-Tests.Rout' to 'edgeR-Tests.Rout.save' ... OK
 OK
** running tests for arch 'x64' ...
  Running 'edgeR-Tests.R'
  Comparing 'edgeR-Tests.Rout' to 'edgeR-Tests.Rout.save' ... OK
 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
  'D:/biocbuild/bbs-3.14-bioc/meat/edgeR.Rcheck/00check.log'
for details.



Installation output

edgeR.Rcheck/00install.out

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##############################################################################
###
### Running command:
###
###   C:\cygwin\bin\curl.exe -O http://155.52.207.166/BBS/3.14/bioc/src/contrib/edgeR_3.35.3.tar.gz && rm -rf edgeR.buildbin-libdir && mkdir edgeR.buildbin-libdir && D:\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=edgeR.buildbin-libdir edgeR_3.35.3.tar.gz && D:\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD INSTALL edgeR_3.35.3.zip && rm edgeR_3.35.3.tar.gz edgeR_3.35.3.zip
###
##############################################################################
##############################################################################


  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100 1552k  100 1552k    0     0  4118k      0 --:--:-- --:--:-- --:--:-- 4118k

install for i386

* installing *source* package 'edgeR' ...
** using staged installation
** libs
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_add_prior_count.cpp -o R_add_prior_count.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_ave_log_cpm.cpp -o R_ave_log_cpm.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_calculate_cpm.cpp -o R_calculate_cpm.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_check_poisson_bound.cpp -o R_check_poisson_bound.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_compute_apl.cpp -o R_compute_apl.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_compute_nbdev.cpp -o R_compute_nbdev.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_exact_test_by_deviance.cpp -o R_exact_test_by_deviance.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_fit_levenberg.cpp -o R_fit_levenberg.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_fit_one_group.cpp -o R_fit_one_group.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_get_one_way_fitted.cpp -o R_get_one_way_fitted.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_initialize_levenberg.cpp -o R_initialize_levenberg.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_loess_by_col.cpp -o R_loess_by_col.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_maximize_interpolant.cpp -o R_maximize_interpolant.o
"C:/rtools40/mingw32/bin/"gcc  -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -std=gnu99 -mfpmath=sse -msse2 -mstackrealign  -c R_process_hairpin_reads.c -o R_process_hairpin_reads.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_simple_good_turing.cpp -o R_simple_good_turing.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c add_prior.cpp -o add_prior.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c adj_coxreid.cpp -o adj_coxreid.o
"C:/rtools40/mingw32/bin/"gcc  -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -std=gnu99 -mfpmath=sse -msse2 -mstackrealign  -c fmm_spline.c -o fmm_spline.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c glm_levenberg.cpp -o glm_levenberg.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c glm_one_group.cpp -o glm_one_group.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c init.cpp -o init.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c interpolator.cpp -o interpolator.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c nbdev.cpp -o nbdev.o
"C:/rtools40/mingw32/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c objects.cpp -o objects.o
C:/rtools40/mingw32/bin/g++ -shared -s -static-libgcc -o edgeR.dll tmp.def R_add_prior_count.o R_ave_log_cpm.o R_calculate_cpm.o R_check_poisson_bound.o R_compute_apl.o R_compute_nbdev.o R_exact_test_by_deviance.o R_fit_levenberg.o R_fit_one_group.o R_get_one_way_fitted.o R_initialize_levenberg.o R_loess_by_col.o R_maximize_interpolant.o R_process_hairpin_reads.o R_simple_good_turing.o add_prior.o adj_coxreid.o fmm_spline.o glm_levenberg.o glm_one_group.o init.o interpolator.o nbdev.o objects.o -LD:/biocbuild/bbs-3.14-bioc/R/bin/i386 -lRlapack -LD:/biocbuild/bbs-3.14-bioc/R/bin/i386 -lRblas -lgfortran -lm -lquadmath -LC:/extsoft/lib/i386 -LC:/extsoft/lib -LD:/biocbuild/bbs-3.14-bioc/R/bin/i386 -lR
installing to D:/biocbuild/bbs-3.14-bioc/meat/edgeR.buildbin-libdir/00LOCK-edgeR/00new/edgeR/libs/i386
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
  converting help for package 'edgeR'
    finding HTML links ... done
    DGEExact-class                          html  
    DGEGLM-class                            html  
    DGELRT-class                            html  
    DGEList-class                           html  
    DGEList                                 html  
    SE2DGEList                              html  
    WLEB                                    html  
    addPriorCount                           html  
    adjustedProfileLik                      html  
    asdataframe                             html  
    asmatrix                                html  
    aveLogCPM                               html  
    binomTest                               html  
    calcNormFactors                         html  
    camera.DGEList                          html  
    finding level-2 HTML links ... done

    catchSalmon                             html  
    cbind                                   html  
    commonCondLogLikDerDelta                html  
    condLogLikDerSize                       html  
    cpm                                     html  
    cutWithMinN                             html  
    decidetestsDGE                          html  
    dglmStdResid                            html  
    diffSpliceDGE                           html  
    dim                                     html  
    dimnames                                html  
    dispBinTrend                            html  
    dispCoxReid                             html  
    dispCoxReidInterpolateTagwise           html  
    dispCoxReidSplineTrend                  html  
    dropEmptyLevels                         html  
    edgeR-package                           html  
    edgeRUsersGuide                         html  
    effectiveLibSizes                       html  
    equalizeLibSizes                        html  
    estimateCommonDisp                      html  
    estimateDisp                            html  
    estimateExonGenewisedisp                html  
    estimateGLMCommonDisp                   html  
    estimateGLMRobustDisp                   html  
    estimateGLMTagwiseDisp                  html  
    estimateGLMTrendedDisp                  html  
    estimateTagwiseDisp                     html  
    estimateTrendedDisp                     html  
    exactTest                               html  
    expandAsMatrix                          html  
    featureCounts2DGEList                   html  
    filterByExpr                            html  
    getCounts                               html  
    getPriorN                               html  
    gini                                    html  
    glmQLFTest                              html  
    glmTreat                                html  
    glmfit                                  html  
    goana                                   html  
    gof                                     html  
    goodTuring                              html  
    head                                    html  
    loessByCol                              html  
    maPlot                                  html  
    makeCompressedMatrix                    html  
    maximizeInterpolant                     html  
    maximizeQuadratic                       html  
    meanvar                                 html  
    mglm                                    html  
    modelMatrixMeth                         html  
    movingAverageByCol                      html  
    nbinomDeviance                          html  
    nbinomUnitDeviance                      html  
    nearestReftoX                           html  
    nearestTSS                              html  
    normalizeChIPtoInput                    html  
    plotBCV                                 html  
    plotExonUsage                           html  
    plotMD                                  html  
    plotMDS.DGEList                         html  
    plotQLDisp                              html  
    plotSmear                               html  
    plotSpliceDGE                           html  
    predFC                                  html  
    processAmplicons                        html  
    q2qnbinom                               html  
    read10X                                 html  
    readBismark2DGE                         html  
    readDGE                                 html  
    roast.DGEList                           html  
    romer.DGEList                           html  
    rowsum                                  html  
    scaleOffset                             html  
    spliceVariants                          html  
    splitIntoGroups                         html  
    subsetting                              html  
    sumTechReps                             html  
    systematicSubset                        html  
    thinCounts                              html  
    topSpliceDGE                            html  
    topTags                                 html  
    validDGEList                            html  
    voomLmFit                               html  
    weightedCondLogLikDerDelta              html  
    zscoreNBinom                            html  
** building package indices
** installing vignettes
   'edgeR.Rnw' 
** 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 'edgeR' ...
** libs
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_add_prior_count.cpp -o R_add_prior_count.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_ave_log_cpm.cpp -o R_ave_log_cpm.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_calculate_cpm.cpp -o R_calculate_cpm.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_check_poisson_bound.cpp -o R_check_poisson_bound.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_compute_apl.cpp -o R_compute_apl.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_compute_nbdev.cpp -o R_compute_nbdev.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_exact_test_by_deviance.cpp -o R_exact_test_by_deviance.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_fit_levenberg.cpp -o R_fit_levenberg.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_fit_one_group.cpp -o R_fit_one_group.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_get_one_way_fitted.cpp -o R_get_one_way_fitted.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_initialize_levenberg.cpp -o R_initialize_levenberg.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_loess_by_col.cpp -o R_loess_by_col.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_maximize_interpolant.cpp -o R_maximize_interpolant.o
"C:/rtools40/mingw64/bin/"gcc  -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -std=gnu99 -mfpmath=sse -msse2 -mstackrealign  -c R_process_hairpin_reads.c -o R_process_hairpin_reads.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c R_simple_good_turing.cpp -o R_simple_good_turing.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c add_prior.cpp -o add_prior.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c adj_coxreid.cpp -o adj_coxreid.o
"C:/rtools40/mingw64/bin/"gcc  -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -std=gnu99 -mfpmath=sse -msse2 -mstackrealign  -c fmm_spline.c -o fmm_spline.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c glm_levenberg.cpp -o glm_levenberg.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c glm_one_group.cpp -o glm_one_group.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c init.cpp -o init.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c interpolator.cpp -o interpolator.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c nbdev.cpp -o nbdev.o
"C:/rtools40/mingw64/bin/"g++  -std=gnu++11 -I"D:/biocbuild/bbs-3.14-bioc/R/include" -DNDEBUG  -I'D:/biocbuild/bbs-3.14-bioc/R/library/Rcpp/include'   -I"C:/extsoft/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c objects.cpp -o objects.o
C:/rtools40/mingw64/bin/g++ -shared -s -static-libgcc -o edgeR.dll tmp.def R_add_prior_count.o R_ave_log_cpm.o R_calculate_cpm.o R_check_poisson_bound.o R_compute_apl.o R_compute_nbdev.o R_exact_test_by_deviance.o R_fit_levenberg.o R_fit_one_group.o R_get_one_way_fitted.o R_initialize_levenberg.o R_loess_by_col.o R_maximize_interpolant.o R_process_hairpin_reads.o R_simple_good_turing.o add_prior.o adj_coxreid.o fmm_spline.o glm_levenberg.o glm_one_group.o init.o interpolator.o nbdev.o objects.o -LD:/biocbuild/bbs-3.14-bioc/R/bin/x64 -lRlapack -LD:/biocbuild/bbs-3.14-bioc/R/bin/x64 -lRblas -lgfortran -lm -lquadmath -LC:/extsoft/lib/x64 -LC:/extsoft/lib -LD:/biocbuild/bbs-3.14-bioc/R/bin/x64 -lR
installing to D:/biocbuild/bbs-3.14-bioc/meat/edgeR.buildbin-libdir/edgeR/libs/x64
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'edgeR' as edgeR_3.35.3.zip
* DONE (edgeR)
* installing to library 'D:/biocbuild/bbs-3.14-bioc/R/library'
package 'edgeR' successfully unpacked and MD5 sums checked

Tests output

edgeR.Rcheck/tests_i386/edgeR-Tests.Rout.save


R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
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> library(edgeR)
Loading required package: limma
> options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE)
> 
> set.seed(0); u <- runif(100)
> 
> # generate raw counts from NB, create list object
> y <- matrix(rnbinom(80,size=5,mu=10),nrow=20)
> y <- rbind(0,c(0,0,2,2),y)
> rownames(y) <- paste("Tag",1:nrow(y),sep=".")
> d <- DGEList(counts=y,group=rep(1:2,each=2),lib.size=1001:1004)
> 
> filterByExpr(d)
 Tag.1  Tag.2  Tag.3  Tag.4  Tag.5  Tag.6  Tag.7  Tag.8  Tag.9 Tag.10 Tag.11 
 FALSE  FALSE  FALSE   TRUE  FALSE  FALSE   TRUE  FALSE  FALSE   TRUE   TRUE 
Tag.12 Tag.13 Tag.14 Tag.15 Tag.16 Tag.17 Tag.18 Tag.19 Tag.20 Tag.21 Tag.22 
  TRUE   TRUE  FALSE  FALSE   TRUE   TRUE  FALSE  FALSE   TRUE   TRUE   TRUE 
> 
> # estimate common dispersion and find differences in expression
> d <- estimateCommonDisp(d)
> d$common.dispersion
[1] 0.210292
> de <- exactTest(d)
> summary(de$table)
     logFC             logCPM          PValue       
 Min.   :-1.7266   Min.   :10.96   Min.   :0.01976  
 1st Qu.:-0.4855   1st Qu.:13.21   1st Qu.:0.33120  
 Median : 0.2253   Median :13.37   Median :0.56514  
 Mean   : 0.1877   Mean   :13.26   Mean   :0.54504  
 3rd Qu.: 0.5258   3rd Qu.:13.70   3rd Qu.:0.81052  
 Max.   : 4.0861   Max.   :14.31   Max.   :1.00000  
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450964 13.73726 0.01975954 0.4347099
Tag.21 -1.7265870 13.38327 0.06131012 0.6744114
Tag.6  -1.6329986 12.81479 0.12446044 0.8982100
Tag.2   4.0861092 11.54121 0.16331090 0.8982100
Tag.16  0.9324996 13.57074 0.29050785 0.9655885
Tag.20  0.8543138 13.76364 0.31736609 0.9655885
Tag.12  0.7081170 14.31389 0.37271028 0.9655885
Tag.19 -0.7976602 13.31405 0.40166354 0.9655885
Tag.3  -0.7300410 13.54155 0.42139935 0.9655885
Tag.8  -0.7917906 12.86353 0.47117217 0.9655885
> 
> d2 <- estimateTagwiseDisp(d,trend="none",prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1757  0.1896  0.1989  0.2063  0.2185  0.2677 
> de <- exactTest(d2,dispersion="common")
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450964 13.73726 0.01975954 0.4347099
Tag.21 -1.7265870 13.38327 0.06131012 0.6744114
Tag.6  -1.6329986 12.81479 0.12446044 0.8982100
Tag.2   4.0861092 11.54121 0.16331090 0.8982100
Tag.16  0.9324996 13.57074 0.29050785 0.9655885
Tag.20  0.8543138 13.76364 0.31736609 0.9655885
Tag.12  0.7081170 14.31389 0.37271028 0.9655885
Tag.19 -0.7976602 13.31405 0.40166354 0.9655885
Tag.3  -0.7300410 13.54155 0.42139935 0.9655885
Tag.8  -0.7917906 12.86353 0.47117217 0.9655885
> 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450987 13.73726 0.01327001 0.2919403
Tag.21 -1.7265897 13.38327 0.05683886 0.6252275
Tag.6  -1.6329910 12.81479 0.11460208 0.8404152
Tag.2   4.0861092 11.54121 0.16126207 0.8869414
Tag.16  0.9324975 13.57074 0.28103256 0.9669238
Tag.20  0.8543178 13.76364 0.30234789 0.9669238
Tag.12  0.7081149 14.31389 0.37917895 0.9669238
Tag.19 -0.7976633 13.31405 0.40762735 0.9669238
Tag.3  -0.7300478 13.54155 0.40856822 0.9669238
Tag.8  -0.7918243 12.86353 0.49005179 0.9669238
> 
> d2 <- estimateTagwiseDisp(d,trend="movingave",span=0.4,prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1005  0.1629  0.2064  0.2077  0.2585  0.3164 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450951 13.73726 0.02427872 0.5341319
Tag.21 -1.7265927 13.38327 0.05234833 0.5758316
Tag.6  -1.6330014 12.81479 0.12846308 0.8954397
Tag.2   4.0861092 11.54121 0.16280722 0.8954397
Tag.16  0.9324887 13.57074 0.24308201 0.9711975
Tag.20  0.8543044 13.76364 0.35534649 0.9711975
Tag.19 -0.7976535 13.31405 0.38873717 0.9711975
Tag.3  -0.7300525 13.54155 0.40001438 0.9711975
Tag.12  0.7080985 14.31389 0.43530227 0.9711975
Tag.8  -0.7918376 12.86353 0.49782701 0.9711975
> 
> summary(exactTest(d2,rejection.region="smallp")$table$PValue)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.02428 0.36369 0.55662 0.54319 0.78889 1.00000 
> summary(exactTest(d2,rejection.region="deviance")$table$PValue)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.02428 0.36369 0.55662 0.54319 0.78889 1.00000 
> 
> d2 <- estimateTagwiseDisp(d,trend="loess",span=0.8,prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1165  0.1449  0.1832  0.1848  0.2116  0.2825 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450979 13.73726 0.01546795 0.3402949
Tag.21 -1.7266049 13.38327 0.03545446 0.3899990
Tag.6  -1.6329841 12.81479 0.10632987 0.7797524
Tag.2   4.0861092 11.54121 0.16057893 0.8831841
Tag.16  0.9324935 13.57074 0.26348818 0.9658389
Tag.20  0.8543140 13.76364 0.31674090 0.9658389
Tag.19 -0.7976354 13.31405 0.35564858 0.9658389
Tag.3  -0.7300593 13.54155 0.38833737 0.9658389
Tag.12  0.7081041 14.31389 0.41513004 0.9658389
Tag.8  -0.7918152 12.86353 0.48483449 0.9658389
> 
> d2 <- estimateTagwiseDisp(d,trend="tricube",span=0.8,prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1165  0.1449  0.1832  0.1848  0.2116  0.2825 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450979 13.73726 0.01546795 0.3402949
Tag.21 -1.7266049 13.38327 0.03545446 0.3899990
Tag.6  -1.6329841 12.81479 0.10632987 0.7797524
Tag.2   4.0861092 11.54121 0.16057893 0.8831841
Tag.16  0.9324935 13.57074 0.26348818 0.9658389
Tag.20  0.8543140 13.76364 0.31674090 0.9658389
Tag.19 -0.7976354 13.31405 0.35564858 0.9658389
Tag.3  -0.7300593 13.54155 0.38833737 0.9658389
Tag.12  0.7081041 14.31389 0.41513004 0.9658389
Tag.8  -0.7918152 12.86353 0.48483449 0.9658389
> 
> # mglmOneWay
> design <- model.matrix(~group,data=d$samples)
> mglmOneWay(d[1:10,],design,dispersion=0.2)
$coefficients
         (Intercept)        group2
Tag.1  -1.000000e+08  0.000000e+00
Tag.2  -1.000000e+08  1.000000e+08
Tag.3   2.525729e+00 -5.108256e-01
Tag.4   2.525729e+00  1.484200e-01
Tag.5   2.140066e+00 -1.941560e-01
Tag.6   2.079442e+00 -1.163151e+00
Tag.7   2.014903e+00  2.363888e-01
Tag.8   1.945910e+00 -5.596158e-01
Tag.9   1.504077e+00  2.006707e-01
Tag.10  2.302585e+00  2.623643e-01

$fitted.values
       Sample1 Sample2 Sample3 Sample4
Tag.1      0.0     0.0     0.0     0.0
Tag.2      0.0     0.0     2.0     2.0
Tag.3     12.5    12.5     7.5     7.5
Tag.4     12.5    12.5    14.5    14.5
Tag.5      8.5     8.5     7.0     7.0
Tag.6      8.0     8.0     2.5     2.5
Tag.7      7.5     7.5     9.5     9.5
Tag.8      7.0     7.0     4.0     4.0
Tag.9      4.5     4.5     5.5     5.5
Tag.10    10.0    10.0    13.0    13.0

> mglmOneWay(d[1:10,],design,dispersion=0)
$coefficients
         (Intercept)        group2
Tag.1  -1.000000e+08  0.000000e+00
Tag.2  -1.000000e+08  1.000000e+08
Tag.3   2.525729e+00 -5.108256e-01
Tag.4   2.525729e+00  1.484200e-01
Tag.5   2.140066e+00 -1.941560e-01
Tag.6   2.079442e+00 -1.163151e+00
Tag.7   2.014903e+00  2.363888e-01
Tag.8   1.945910e+00 -5.596158e-01
Tag.9   1.504077e+00  2.006707e-01
Tag.10  2.302585e+00  2.623643e-01

$fitted.values
       Sample1 Sample2 Sample3 Sample4
Tag.1      0.0     0.0     0.0     0.0
Tag.2      0.0     0.0     2.0     2.0
Tag.3     12.5    12.5     7.5     7.5
Tag.4     12.5    12.5    14.5    14.5
Tag.5      8.5     8.5     7.0     7.0
Tag.6      8.0     8.0     2.5     2.5
Tag.7      7.5     7.5     9.5     9.5
Tag.8      7.0     7.0     4.0     4.0
Tag.9      4.5     4.5     5.5     5.5
Tag.10    10.0    10.0    13.0    13.0

> 
> fit <- glmFit(d,design,dispersion=d$common.dispersion,prior.count=0.5/4)
> lrt <- glmLRT(fit,coef=2)
> topTags(lrt)
Coefficient:  group2 
            logFC   logCPM        LR     PValue       FDR
Tag.17  2.0450964 13.73726 6.0485417 0.01391779 0.3058698
Tag.2   4.0861092 11.54121 4.8400340 0.02780635 0.3058698
Tag.21 -1.7265870 13.38327 4.0777825 0.04345065 0.3186381
Tag.6  -1.6329986 12.81479 3.0078205 0.08286364 0.4557500
Tag.16  0.9324996 13.57074 1.3477682 0.24566867 0.8276702
Tag.20  0.8543138 13.76364 1.1890032 0.27553071 0.8276702
Tag.19 -0.7976602 13.31405 0.9279151 0.33540526 0.8276702
Tag.12  0.7081170 14.31389 0.9095513 0.34023349 0.8276702
Tag.3  -0.7300410 13.54155 0.8300307 0.36226364 0.8276702
Tag.8  -0.7917906 12.86353 0.7830377 0.37621371 0.8276702
> 
> fit <- glmFit(d,design,dispersion=d$common.dispersion,prior.count=0.5)
> summary(fit$coefficients)
  (Intercept)         group2        
 Min.   :-7.604   Min.   :-1.13681  
 1st Qu.:-4.895   1st Qu.:-0.32341  
 Median :-4.713   Median : 0.15083  
 Mean   :-4.940   Mean   : 0.07817  
 3rd Qu.:-4.524   3rd Qu.: 0.35163  
 Max.   :-4.107   Max.   : 1.60864  
> 
> fit <- glmFit(d,design,prior.count=0.5/4)
> lrt <- glmLRT(fit,coef=2)
> topTags(lrt)
Coefficient:  group2 
            logFC   logCPM        LR     PValue       FDR
Tag.17  2.0450964 13.73726 6.0485417 0.01391779 0.3058698
Tag.2   4.0861092 11.54121 4.8400340 0.02780635 0.3058698
Tag.21 -1.7265870 13.38327 4.0777825 0.04345065 0.3186381
Tag.6  -1.6329986 12.81479 3.0078205 0.08286364 0.4557500
Tag.16  0.9324996 13.57074 1.3477682 0.24566867 0.8276702
Tag.20  0.8543138 13.76364 1.1890032 0.27553071 0.8276702
Tag.19 -0.7976602 13.31405 0.9279151 0.33540526 0.8276702
Tag.12  0.7081170 14.31389 0.9095513 0.34023349 0.8276702
Tag.3  -0.7300410 13.54155 0.8300307 0.36226364 0.8276702
Tag.8  -0.7917906 12.86353 0.7830377 0.37621371 0.8276702
> 
> dglm <- estimateGLMCommonDisp(d,design)
> dglm$common.dispersion
[1] 0.2033282
> dglm <- estimateGLMTagwiseDisp(dglm,design,prior.df=20)
> summary(dglm$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1756  0.1879  0.1998  0.2031  0.2135  0.2578 
> fit <- glmFit(dglm,design,prior.count=0.5/4)
> lrt <- glmLRT(fit,coef=2)
> topTags(lrt)
Coefficient:  group2 
            logFC   logCPM        LR      PValue       FDR
Tag.17  2.0450988 13.73727 6.8001118 0.009115216 0.2005348
Tag.2   4.0861092 11.54122 4.8594088 0.027495756 0.2872068
Tag.21 -1.7265904 13.38327 4.2537154 0.039164558 0.2872068
Tag.6  -1.6329904 12.81479 3.1763761 0.074710253 0.4109064
Tag.16  0.9324970 13.57074 1.4126709 0.234613512 0.8499599
Tag.20  0.8543183 13.76364 1.2721097 0.259371274 0.8499599
Tag.19 -0.7976614 13.31405 0.9190392 0.337727381 0.8499599
Tag.12  0.7081163 14.31389 0.9014515 0.342392806 0.8499599
Tag.3  -0.7300488 13.54155 0.8817937 0.347710872 0.8499599
Tag.8  -0.7918166 12.86353 0.7356185 0.391068049 0.8603497
> dglm <- estimateGLMTrendedDisp(dglm,design)
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1522  0.1676  0.1740  0.1887  0.2000  0.3469 
> dglm <- estimateGLMTrendedDisp(dglm,design,method="power")
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1522  0.1676  0.1740  0.1887  0.2000  0.3469 
> dglm <- estimateGLMTrendedDisp(dglm,design,method="spline")
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.09353 0.11082 0.15463 0.19006 0.23050 0.52006 
> dglm <- estimateGLMTrendedDisp(dglm,design,method="bin.spline")
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1997  0.1997  0.1997  0.1997  0.1997  0.1997 
> dglm <- estimateGLMTagwiseDisp(dglm,design,prior.df=20)
> summary(dglm$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1385  0.1792  0.1964  0.1935  0.2026  0.2709 
> 
> dglm2 <- estimateDisp(dglm, design)
> summary(dglm2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1652  0.1740  0.1821  0.1852  0.1909  0.2259 
> dglm2 <- estimateDisp(dglm, design, prior.df=20)
> summary(dglm2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1527  0.1669  0.1814  0.1858  0.1951  0.2497 
> dglm2 <- estimateDisp(dglm, design, robust=TRUE)
> summary(dglm2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1652  0.1735  0.1822  0.1854  0.1905  0.2280 
> 
> # Continuous trend
> nlibs <- 3
> ntags <- 1000
> dispersion.true <- 0.1
> # Make first transcript respond to covariate x
> x <- 0:2
> design <- model.matrix(~x)
> beta.true <- cbind(Beta1=2,Beta2=c(2,rep(0,ntags-1)))
> mu.true <- 2^(beta.true %*% t(design))
> # Generate count data
> y <- rnbinom(ntags*nlibs,mu=mu.true,size=1/dispersion.true)
> y <- matrix(y,ntags,nlibs)
> colnames(y) <- c("x0","x1","x2")
> rownames(y) <- paste("Gene",1:ntags,sep="")
> d <- DGEList(y)
> d <- calcNormFactors(d,method="TMM")
> fit <- glmFit(d, design, dispersion=dispersion.true, prior.count=0.5/3)
> results <- glmLRT(fit, coef=2)
> topTags(results)
Coefficient:  x 
            logFC   logCPM        LR       PValue          FDR
Gene1    2.907024 13.56183 38.738512 4.845536e-10 4.845536e-07
Gene61   2.855317 10.27136 10.738307 1.049403e-03 5.247015e-01
Gene62  -2.123902 10.53174  8.818703 2.981585e-03 8.334760e-01
Gene134 -1.949073 10.53355  8.125889 4.363759e-03 8.334760e-01
Gene740 -1.610046 10.94907  8.013408 4.643227e-03 8.334760e-01
Gene354  2.022698 10.45066  7.826308 5.149118e-03 8.334760e-01
Gene5    1.856816 10.45249  7.214238 7.232750e-03 8.334760e-01
Gene746 -1.798331 10.53094  6.846262 8.882693e-03 8.334760e-01
Gene110  1.623148 10.68607  6.737984 9.438120e-03 8.334760e-01
Gene383  1.637140 10.75412  6.687530 9.708965e-03 8.334760e-01
> d1 <- estimateGLMCommonDisp(d, design, verbose=TRUE)
Disp = 0.10253 , BCV = 0.3202 
> glmFit(d,design,dispersion=dispersion.true, prior.count=0.5/3)
An object of class "DGEGLM"
$coefficients
      (Intercept)          x
Gene1   -7.391745  2.0149958
Gene2   -7.318483 -0.7611895
Gene3   -6.831702 -0.1399478
Gene4   -7.480255  0.5172002
Gene5   -8.747793  1.2870467
995 more rows ...

$fitted.values
             x0        x1          x2
Gene1 2.3570471 18.954454 138.2791328
Gene2 2.5138172  1.089292   0.4282107
Gene3 4.1580452  3.750528   3.0690081
Gene4 2.1012460  3.769592   6.1349937
Gene5 0.5080377  2.136398   8.1502486
995 more rows ...

$deviance
[1] 6.38037545 1.46644913 1.38532340 0.01593969 1.03894513
995 more elements ...

$iter
[1] 8 4 4 4 6
995 more elements ...

$failed
[1] FALSE FALSE FALSE FALSE FALSE
995 more elements ...

$method
[1] "levenberg"

$counts
      x0 x1  x2
Gene1  0 30 110
Gene2  2  2   0
Gene3  3  6   2
Gene4  2  4   6
Gene5  1  1   9
995 more rows ...

$unshrunk.coefficients
      (Intercept)          x
Gene1   -7.437763  2.0412762
Gene2   -7.373370 -0.8796273
Gene3   -6.870127 -0.1465014
Gene4   -7.552642  0.5410832
Gene5   -8.972372  1.3929679
995 more rows ...

$df.residual
[1] 1 1 1 1 1
995 more elements ...

$design
  (Intercept) x
1           1 0
2           1 1
3           1 2
attr(,"assign")
[1] 0 1

$offset
         [,1]     [,2]     [,3]
[1,] 8.295172 8.338525 8.284484
attr(,"class")
[1] "CompressedMatrix"
attr(,"Dims")
[1] 5 3
attr(,"repeat.row")
[1] TRUE
attr(,"repeat.col")
[1] FALSE
995 more rows ...

$dispersion
[1] 0.1

$prior.count
[1] 0.1666667

$samples
   group lib.size norm.factors
x0     1     4001    1.0008730
x1     1     4176    1.0014172
x2     1     3971    0.9977138

$AveLogCPM
[1] 13.561832  9.682757 10.447014 10.532113 10.452489
995 more elements ...

> 
> calcNormFactors(d$counts,method="TMMwsp")
       x0        x1        x2 
0.9992437 1.0077007 0.9931093 
> 
> d2 <- estimateDisp(d, design)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.05545 0.09511 0.11623 0.11014 0.13329 0.16861 
> d2 <- estimateDisp(d, design, prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.04203 0.08586 0.11280 0.11010 0.12369 0.37408 
> d2 <- estimateDisp(d, design, robust=TRUE)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.05545 0.09511 0.11623 0.11014 0.13329 0.16861 
> 
> # Exact tests
> y <- matrix(rnbinom(20,mu=10,size=3/2),5,4)
> group <- factor(c(1,1,2,2))
> ys <- splitIntoGroupsPseudo(y,group,pair=c(1,2))
> exactTestDoubleTail(ys$y1,ys$y2,dispersion=2/3)
[1] 0.1334396 0.6343568 0.7280015 0.7124912 0.3919258
> 
> y <- matrix(rnbinom(5*7,mu=10,size=3/2),5,7)
> group <- factor(c(1,1,2,2,3,3,3))
> ys <- splitIntoGroupsPseudo(y,group,pair=c(1,3))
> exactTestDoubleTail(ys$y1,ys$y2,dispersion=2/3)
[1] 1.0000000 0.4486382 1.0000000 0.9390317 0.4591241
> exactTestBetaApprox(ys$y1,ys$y2,dispersion=2/3)
[1] 1.0000000 0.4492969 1.0000000 0.9421695 0.4589194
> 
> y[1,3:4] <- 0
> design <- model.matrix(~group)
> fit <- glmFit(y,design,dispersion=2/3,prior.count=0.5/7)
> summary(fit$coefficients)
  (Intercept)         group2            group3        
 Min.   :-1.817   Min.   :-5.0171   Min.   :-0.64646  
 1st Qu.:-1.812   1st Qu.:-1.1565   1st Qu.:-0.13919  
 Median :-1.712   Median : 0.1994   Median :-0.10441  
 Mean   :-1.625   Mean   :-0.9523   Mean   :-0.04217  
 3rd Qu.:-1.429   3rd Qu.: 0.3755   3rd Qu.:-0.04305  
 Max.   :-1.356   Max.   : 0.8374   Max.   : 0.72227  
> 
> lrt <- glmLRT(fit,contrast=cbind(c(0,1,0),c(0,0,1)))
> topTags(lrt)
Coefficient:  LR test on 2 degrees of freedom 
     logFC.1    logFC.2   logCPM         LR      PValue        FDR
1 -7.2381060 -0.0621100 17.19071 10.7712171 0.004582051 0.02291026
5 -1.6684268 -0.9326507 17.33529  1.7309951 0.420842115 0.90967967
2  1.2080938  1.0420198 18.24544  1.0496688 0.591653347 0.90967967
4  0.5416704 -0.1506381 17.57744  0.3958596 0.820427427 0.90967967
3  0.2876249 -0.2008143 18.06216  0.1893255 0.909679672 0.90967967
> design <- model.matrix(~0+group)
> fit <- glmFit(y,design,dispersion=2/3,prior.count=0.5/7)
> lrt <- glmLRT(fit,contrast=cbind(c(-1,1,0),c(0,-1,1),c(-1,0,1)))
> topTags(lrt)
Coefficient:  LR test on 2 degrees of freedom 
     logFC.1    logFC.2    logFC.3   logCPM         LR      PValue        FDR
1 -7.2381060  7.1759960 -0.0621100 17.19071 10.7712171 0.004582051 0.02291026
5 -1.6684268  0.7357761 -0.9326507 17.33529  1.7309951 0.420842115 0.90967967
2  1.2080938 -0.1660740  1.0420198 18.24544  1.0496688 0.591653347 0.90967967
4  0.5416704 -0.6923084 -0.1506381 17.57744  0.3958596 0.820427427 0.90967967
3  0.2876249 -0.4884392 -0.2008143 18.06216  0.1893255 0.909679672 0.90967967
> 
> # simple Good-Turing algorithm runs.
> test1<-1:9
> freq1<-c(2018046, 449721, 188933, 105668, 68379, 48190, 35709, 37710, 22280)
> goodTuring(rep(test1, freq1))
$P0
[1] 0.3814719

$proportion
[1] 8.035111e-08 2.272143e-07 4.060582e-07 5.773690e-07 7.516705e-07
[6] 9.276808e-07 1.104759e-06 1.282549e-06 1.460837e-06

$count
[1] 1 2 3 4 5 6 7 8 9

$n
[1] 2018046  449721  188933  105668   68379   48190   35709   37710   22280

$n0
[1] 0

> test2<-c(312, 14491, 16401, 65124, 129797, 323321, 366051, 368599, 405261, 604962)
> goodTuring(test2)
$P0
[1] 0

$proportion
 [1] 0.0001362656 0.0063162959 0.0071487846 0.0283850925 0.0565733349
 [6] 0.1409223124 0.1595465235 0.1606570896 0.1766365144 0.2636777866

$count
 [1]    312  14491  16401  65124 129797 323321 366051 368599 405261 604962

$n
 [1] 1 1 1 1 1 1 1 1 1 1

$n0
[1] 0

> 
> 
> 
> proc.time()
   user  system elapsed 
   2.17    0.23    2.40 

edgeR.Rcheck/tests_x64/edgeR-Tests.Rout.save


R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

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Type 'contributors()' for more information and
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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.

> library(edgeR)
Loading required package: limma
> options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE)
> 
> set.seed(0); u <- runif(100)
> 
> # generate raw counts from NB, create list object
> y <- matrix(rnbinom(80,size=5,mu=10),nrow=20)
> y <- rbind(0,c(0,0,2,2),y)
> rownames(y) <- paste("Tag",1:nrow(y),sep=".")
> d <- DGEList(counts=y,group=rep(1:2,each=2),lib.size=1001:1004)
> 
> filterByExpr(d)
 Tag.1  Tag.2  Tag.3  Tag.4  Tag.5  Tag.6  Tag.7  Tag.8  Tag.9 Tag.10 Tag.11 
 FALSE  FALSE  FALSE   TRUE  FALSE  FALSE   TRUE  FALSE  FALSE   TRUE   TRUE 
Tag.12 Tag.13 Tag.14 Tag.15 Tag.16 Tag.17 Tag.18 Tag.19 Tag.20 Tag.21 Tag.22 
  TRUE   TRUE  FALSE  FALSE   TRUE   TRUE  FALSE  FALSE   TRUE   TRUE   TRUE 
> 
> # estimate common dispersion and find differences in expression
> d <- estimateCommonDisp(d)
> d$common.dispersion
[1] 0.210292
> de <- exactTest(d)
> summary(de$table)
     logFC             logCPM          PValue       
 Min.   :-1.7266   Min.   :10.96   Min.   :0.01976  
 1st Qu.:-0.4855   1st Qu.:13.21   1st Qu.:0.33120  
 Median : 0.2253   Median :13.37   Median :0.56514  
 Mean   : 0.1877   Mean   :13.26   Mean   :0.54504  
 3rd Qu.: 0.5258   3rd Qu.:13.70   3rd Qu.:0.81052  
 Max.   : 4.0861   Max.   :14.31   Max.   :1.00000  
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450964 13.73726 0.01975954 0.4347099
Tag.21 -1.7265870 13.38327 0.06131012 0.6744114
Tag.6  -1.6329986 12.81479 0.12446044 0.8982100
Tag.2   4.0861092 11.54121 0.16331090 0.8982100
Tag.16  0.9324996 13.57074 0.29050785 0.9655885
Tag.20  0.8543138 13.76364 0.31736609 0.9655885
Tag.12  0.7081170 14.31389 0.37271028 0.9655885
Tag.19 -0.7976602 13.31405 0.40166354 0.9655885
Tag.3  -0.7300410 13.54155 0.42139935 0.9655885
Tag.8  -0.7917906 12.86353 0.47117217 0.9655885
> 
> d2 <- estimateTagwiseDisp(d,trend="none",prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1757  0.1896  0.1989  0.2063  0.2185  0.2677 
> de <- exactTest(d2,dispersion="common")
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450964 13.73726 0.01975954 0.4347099
Tag.21 -1.7265870 13.38327 0.06131012 0.6744114
Tag.6  -1.6329986 12.81479 0.12446044 0.8982100
Tag.2   4.0861092 11.54121 0.16331090 0.8982100
Tag.16  0.9324996 13.57074 0.29050785 0.9655885
Tag.20  0.8543138 13.76364 0.31736609 0.9655885
Tag.12  0.7081170 14.31389 0.37271028 0.9655885
Tag.19 -0.7976602 13.31405 0.40166354 0.9655885
Tag.3  -0.7300410 13.54155 0.42139935 0.9655885
Tag.8  -0.7917906 12.86353 0.47117217 0.9655885
> 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450987 13.73726 0.01327001 0.2919403
Tag.21 -1.7265897 13.38327 0.05683886 0.6252275
Tag.6  -1.6329910 12.81479 0.11460208 0.8404152
Tag.2   4.0861092 11.54121 0.16126207 0.8869414
Tag.16  0.9324975 13.57074 0.28103256 0.9669238
Tag.20  0.8543178 13.76364 0.30234789 0.9669238
Tag.12  0.7081149 14.31389 0.37917895 0.9669238
Tag.19 -0.7976633 13.31405 0.40762735 0.9669238
Tag.3  -0.7300478 13.54155 0.40856822 0.9669238
Tag.8  -0.7918243 12.86353 0.49005179 0.9669238
> 
> d2 <- estimateTagwiseDisp(d,trend="movingave",span=0.4,prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1005  0.1629  0.2064  0.2077  0.2585  0.3164 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450951 13.73726 0.02427872 0.5341319
Tag.21 -1.7265927 13.38327 0.05234833 0.5758316
Tag.6  -1.6330014 12.81479 0.12846308 0.8954397
Tag.2   4.0861092 11.54121 0.16280722 0.8954397
Tag.16  0.9324887 13.57074 0.24308201 0.9711975
Tag.20  0.8543044 13.76364 0.35534649 0.9711975
Tag.19 -0.7976535 13.31405 0.38873717 0.9711975
Tag.3  -0.7300525 13.54155 0.40001438 0.9711975
Tag.12  0.7080985 14.31389 0.43530227 0.9711975
Tag.8  -0.7918376 12.86353 0.49782701 0.9711975
> 
> summary(exactTest(d2,rejection.region="smallp")$table$PValue)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.02428 0.36369 0.55662 0.54319 0.78889 1.00000 
> summary(exactTest(d2,rejection.region="deviance")$table$PValue)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.02428 0.36369 0.55662 0.54319 0.78889 1.00000 
> 
> d2 <- estimateTagwiseDisp(d,trend="loess",span=0.8,prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1165  0.1449  0.1832  0.1848  0.2116  0.2825 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450979 13.73726 0.01546795 0.3402949
Tag.21 -1.7266049 13.38327 0.03545446 0.3899990
Tag.6  -1.6329841 12.81479 0.10632987 0.7797524
Tag.2   4.0861092 11.54121 0.16057893 0.8831841
Tag.16  0.9324935 13.57074 0.26348818 0.9658389
Tag.20  0.8543140 13.76364 0.31674090 0.9658389
Tag.19 -0.7976354 13.31405 0.35564858 0.9658389
Tag.3  -0.7300593 13.54155 0.38833737 0.9658389
Tag.12  0.7081041 14.31389 0.41513004 0.9658389
Tag.8  -0.7918152 12.86353 0.48483449 0.9658389
> 
> d2 <- estimateTagwiseDisp(d,trend="tricube",span=0.8,prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1165  0.1449  0.1832  0.1848  0.2116  0.2825 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450979 13.73726 0.01546795 0.3402949
Tag.21 -1.7266049 13.38327 0.03545446 0.3899990
Tag.6  -1.6329841 12.81479 0.10632987 0.7797524
Tag.2   4.0861092 11.54121 0.16057893 0.8831841
Tag.16  0.9324935 13.57074 0.26348818 0.9658389
Tag.20  0.8543140 13.76364 0.31674090 0.9658389
Tag.19 -0.7976354 13.31405 0.35564858 0.9658389
Tag.3  -0.7300593 13.54155 0.38833737 0.9658389
Tag.12  0.7081041 14.31389 0.41513004 0.9658389
Tag.8  -0.7918152 12.86353 0.48483449 0.9658389
> 
> # mglmOneWay
> design <- model.matrix(~group,data=d$samples)
> mglmOneWay(d[1:10,],design,dispersion=0.2)
$coefficients
         (Intercept)        group2
Tag.1  -1.000000e+08  0.000000e+00
Tag.2  -1.000000e+08  1.000000e+08
Tag.3   2.525729e+00 -5.108256e-01
Tag.4   2.525729e+00  1.484200e-01
Tag.5   2.140066e+00 -1.941560e-01
Tag.6   2.079442e+00 -1.163151e+00
Tag.7   2.014903e+00  2.363888e-01
Tag.8   1.945910e+00 -5.596158e-01
Tag.9   1.504077e+00  2.006707e-01
Tag.10  2.302585e+00  2.623643e-01

$fitted.values
       Sample1 Sample2 Sample3 Sample4
Tag.1      0.0     0.0     0.0     0.0
Tag.2      0.0     0.0     2.0     2.0
Tag.3     12.5    12.5     7.5     7.5
Tag.4     12.5    12.5    14.5    14.5
Tag.5      8.5     8.5     7.0     7.0
Tag.6      8.0     8.0     2.5     2.5
Tag.7      7.5     7.5     9.5     9.5
Tag.8      7.0     7.0     4.0     4.0
Tag.9      4.5     4.5     5.5     5.5
Tag.10    10.0    10.0    13.0    13.0

> mglmOneWay(d[1:10,],design,dispersion=0)
$coefficients
         (Intercept)        group2
Tag.1  -1.000000e+08  0.000000e+00
Tag.2  -1.000000e+08  1.000000e+08
Tag.3   2.525729e+00 -5.108256e-01
Tag.4   2.525729e+00  1.484200e-01
Tag.5   2.140066e+00 -1.941560e-01
Tag.6   2.079442e+00 -1.163151e+00
Tag.7   2.014903e+00  2.363888e-01
Tag.8   1.945910e+00 -5.596158e-01
Tag.9   1.504077e+00  2.006707e-01
Tag.10  2.302585e+00  2.623643e-01

$fitted.values
       Sample1 Sample2 Sample3 Sample4
Tag.1      0.0     0.0     0.0     0.0
Tag.2      0.0     0.0     2.0     2.0
Tag.3     12.5    12.5     7.5     7.5
Tag.4     12.5    12.5    14.5    14.5
Tag.5      8.5     8.5     7.0     7.0
Tag.6      8.0     8.0     2.5     2.5
Tag.7      7.5     7.5     9.5     9.5
Tag.8      7.0     7.0     4.0     4.0
Tag.9      4.5     4.5     5.5     5.5
Tag.10    10.0    10.0    13.0    13.0

> 
> fit <- glmFit(d,design,dispersion=d$common.dispersion,prior.count=0.5/4)
> lrt <- glmLRT(fit,coef=2)
> topTags(lrt)
Coefficient:  group2 
            logFC   logCPM        LR     PValue       FDR
Tag.17  2.0450964 13.73726 6.0485417 0.01391779 0.3058698
Tag.2   4.0861092 11.54121 4.8400340 0.02780635 0.3058698
Tag.21 -1.7265870 13.38327 4.0777825 0.04345065 0.3186381
Tag.6  -1.6329986 12.81479 3.0078205 0.08286364 0.4557500
Tag.16  0.9324996 13.57074 1.3477682 0.24566867 0.8276702
Tag.20  0.8543138 13.76364 1.1890032 0.27553071 0.8276702
Tag.19 -0.7976602 13.31405 0.9279151 0.33540526 0.8276702
Tag.12  0.7081170 14.31389 0.9095513 0.34023349 0.8276702
Tag.3  -0.7300410 13.54155 0.8300307 0.36226364 0.8276702
Tag.8  -0.7917906 12.86353 0.7830377 0.37621371 0.8276702
> 
> fit <- glmFit(d,design,dispersion=d$common.dispersion,prior.count=0.5)
> summary(fit$coefficients)
  (Intercept)         group2        
 Min.   :-7.604   Min.   :-1.13681  
 1st Qu.:-4.895   1st Qu.:-0.32341  
 Median :-4.713   Median : 0.15083  
 Mean   :-4.940   Mean   : 0.07817  
 3rd Qu.:-4.524   3rd Qu.: 0.35163  
 Max.   :-4.107   Max.   : 1.60864  
> 
> fit <- glmFit(d,design,prior.count=0.5/4)
> lrt <- glmLRT(fit,coef=2)
> topTags(lrt)
Coefficient:  group2 
            logFC   logCPM        LR     PValue       FDR
Tag.17  2.0450964 13.73726 6.0485417 0.01391779 0.3058698
Tag.2   4.0861092 11.54121 4.8400340 0.02780635 0.3058698
Tag.21 -1.7265870 13.38327 4.0777825 0.04345065 0.3186381
Tag.6  -1.6329986 12.81479 3.0078205 0.08286364 0.4557500
Tag.16  0.9324996 13.57074 1.3477682 0.24566867 0.8276702
Tag.20  0.8543138 13.76364 1.1890032 0.27553071 0.8276702
Tag.19 -0.7976602 13.31405 0.9279151 0.33540526 0.8276702
Tag.12  0.7081170 14.31389 0.9095513 0.34023349 0.8276702
Tag.3  -0.7300410 13.54155 0.8300307 0.36226364 0.8276702
Tag.8  -0.7917906 12.86353 0.7830377 0.37621371 0.8276702
> 
> dglm <- estimateGLMCommonDisp(d,design)
> dglm$common.dispersion
[1] 0.2033282
> dglm <- estimateGLMTagwiseDisp(dglm,design,prior.df=20)
> summary(dglm$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1756  0.1879  0.1998  0.2031  0.2135  0.2578 
> fit <- glmFit(dglm,design,prior.count=0.5/4)
> lrt <- glmLRT(fit,coef=2)
> topTags(lrt)
Coefficient:  group2 
            logFC   logCPM        LR      PValue       FDR
Tag.17  2.0450988 13.73727 6.8001118 0.009115216 0.2005348
Tag.2   4.0861092 11.54122 4.8594088 0.027495756 0.2872068
Tag.21 -1.7265904 13.38327 4.2537154 0.039164558 0.2872068
Tag.6  -1.6329904 12.81479 3.1763761 0.074710253 0.4109064
Tag.16  0.9324970 13.57074 1.4126709 0.234613512 0.8499599
Tag.20  0.8543183 13.76364 1.2721097 0.259371274 0.8499599
Tag.19 -0.7976614 13.31405 0.9190392 0.337727381 0.8499599
Tag.12  0.7081163 14.31389 0.9014515 0.342392806 0.8499599
Tag.3  -0.7300488 13.54155 0.8817937 0.347710872 0.8499599
Tag.8  -0.7918166 12.86353 0.7356185 0.391068049 0.8603497
> dglm <- estimateGLMTrendedDisp(dglm,design)
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1522  0.1676  0.1740  0.1887  0.2000  0.3469 
> dglm <- estimateGLMTrendedDisp(dglm,design,method="power")
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1522  0.1676  0.1740  0.1887  0.2000  0.3469 
> dglm <- estimateGLMTrendedDisp(dglm,design,method="spline")
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.09353 0.11082 0.15463 0.19006 0.23050 0.52006 
> dglm <- estimateGLMTrendedDisp(dglm,design,method="bin.spline")
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1997  0.1997  0.1997  0.1997  0.1997  0.1997 
> dglm <- estimateGLMTagwiseDisp(dglm,design,prior.df=20)
> summary(dglm$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1385  0.1792  0.1964  0.1935  0.2026  0.2709 
> 
> dglm2 <- estimateDisp(dglm, design)
> summary(dglm2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1652  0.1740  0.1821  0.1852  0.1909  0.2259 
> dglm2 <- estimateDisp(dglm, design, prior.df=20)
> summary(dglm2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1527  0.1669  0.1814  0.1858  0.1951  0.2497 
> dglm2 <- estimateDisp(dglm, design, robust=TRUE)
> summary(dglm2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1652  0.1735  0.1822  0.1854  0.1905  0.2280 
> 
> # Continuous trend
> nlibs <- 3
> ntags <- 1000
> dispersion.true <- 0.1
> # Make first transcript respond to covariate x
> x <- 0:2
> design <- model.matrix(~x)
> beta.true <- cbind(Beta1=2,Beta2=c(2,rep(0,ntags-1)))
> mu.true <- 2^(beta.true %*% t(design))
> # Generate count data
> y <- rnbinom(ntags*nlibs,mu=mu.true,size=1/dispersion.true)
> y <- matrix(y,ntags,nlibs)
> colnames(y) <- c("x0","x1","x2")
> rownames(y) <- paste("Gene",1:ntags,sep="")
> d <- DGEList(y)
> d <- calcNormFactors(d,method="TMM")
> fit <- glmFit(d, design, dispersion=dispersion.true, prior.count=0.5/3)
> results <- glmLRT(fit, coef=2)
> topTags(results)
Coefficient:  x 
            logFC   logCPM        LR       PValue          FDR
Gene1    2.907024 13.56183 38.738512 4.845536e-10 4.845536e-07
Gene61   2.855317 10.27136 10.738307 1.049403e-03 5.247015e-01
Gene62  -2.123902 10.53174  8.818703 2.981585e-03 8.334760e-01
Gene134 -1.949073 10.53355  8.125889 4.363759e-03 8.334760e-01
Gene740 -1.610046 10.94907  8.013408 4.643227e-03 8.334760e-01
Gene354  2.022698 10.45066  7.826308 5.149118e-03 8.334760e-01
Gene5    1.856816 10.45249  7.214238 7.232750e-03 8.334760e-01
Gene746 -1.798331 10.53094  6.846262 8.882693e-03 8.334760e-01
Gene110  1.623148 10.68607  6.737984 9.438120e-03 8.334760e-01
Gene383  1.637140 10.75412  6.687530 9.708965e-03 8.334760e-01
> d1 <- estimateGLMCommonDisp(d, design, verbose=TRUE)
Disp = 0.10253 , BCV = 0.3202 
> glmFit(d,design,dispersion=dispersion.true, prior.count=0.5/3)
An object of class "DGEGLM"
$coefficients
      (Intercept)          x
Gene1   -7.391745  2.0149958
Gene2   -7.318483 -0.7611895
Gene3   -6.831702 -0.1399478
Gene4   -7.480255  0.5172002
Gene5   -8.747793  1.2870467
995 more rows ...

$fitted.values
             x0        x1          x2
Gene1 2.3570471 18.954454 138.2791328
Gene2 2.5138172  1.089292   0.4282107
Gene3 4.1580452  3.750528   3.0690081
Gene4 2.1012460  3.769592   6.1349937
Gene5 0.5080377  2.136398   8.1502486
995 more rows ...

$deviance
[1] 6.38037545 1.46644913 1.38532340 0.01593969 1.03894513
995 more elements ...

$iter
[1] 8 4 4 4 6
995 more elements ...

$failed
[1] FALSE FALSE FALSE FALSE FALSE
995 more elements ...

$method
[1] "levenberg"

$counts
      x0 x1  x2
Gene1  0 30 110
Gene2  2  2   0
Gene3  3  6   2
Gene4  2  4   6
Gene5  1  1   9
995 more rows ...

$unshrunk.coefficients
      (Intercept)          x
Gene1   -7.437763  2.0412762
Gene2   -7.373370 -0.8796273
Gene3   -6.870127 -0.1465014
Gene4   -7.552642  0.5410832
Gene5   -8.972372  1.3929679
995 more rows ...

$df.residual
[1] 1 1 1 1 1
995 more elements ...

$design
  (Intercept) x
1           1 0
2           1 1
3           1 2
attr(,"assign")
[1] 0 1

$offset
         [,1]     [,2]     [,3]
[1,] 8.295172 8.338525 8.284484
attr(,"class")
[1] "CompressedMatrix"
attr(,"Dims")
[1] 5 3
attr(,"repeat.row")
[1] TRUE
attr(,"repeat.col")
[1] FALSE
995 more rows ...

$dispersion
[1] 0.1

$prior.count
[1] 0.1666667

$samples
   group lib.size norm.factors
x0     1     4001    1.0008730
x1     1     4176    1.0014172
x2     1     3971    0.9977138

$AveLogCPM
[1] 13.561832  9.682757 10.447014 10.532113 10.452489
995 more elements ...

> 
> calcNormFactors(d$counts,method="TMMwsp")
       x0        x1        x2 
0.9992437 1.0077007 0.9931093 
> 
> d2 <- estimateDisp(d, design)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.05545 0.09511 0.11623 0.11014 0.13329 0.16861 
> d2 <- estimateDisp(d, design, prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.04203 0.08586 0.11280 0.11010 0.12369 0.37408 
> d2 <- estimateDisp(d, design, robust=TRUE)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.05545 0.09511 0.11623 0.11014 0.13329 0.16861 
> 
> # Exact tests
> y <- matrix(rnbinom(20,mu=10,size=3/2),5,4)
> group <- factor(c(1,1,2,2))
> ys <- splitIntoGroupsPseudo(y,group,pair=c(1,2))
> exactTestDoubleTail(ys$y1,ys$y2,dispersion=2/3)
[1] 0.1334396 0.6343568 0.7280015 0.7124912 0.3919258
> 
> y <- matrix(rnbinom(5*7,mu=10,size=3/2),5,7)
> group <- factor(c(1,1,2,2,3,3,3))
> ys <- splitIntoGroupsPseudo(y,group,pair=c(1,3))
> exactTestDoubleTail(ys$y1,ys$y2,dispersion=2/3)
[1] 1.0000000 0.4486382 1.0000000 0.9390317 0.4591241
> exactTestBetaApprox(ys$y1,ys$y2,dispersion=2/3)
[1] 1.0000000 0.4492969 1.0000000 0.9421695 0.4589194
> 
> y[1,3:4] <- 0
> design <- model.matrix(~group)
> fit <- glmFit(y,design,dispersion=2/3,prior.count=0.5/7)
> summary(fit$coefficients)
  (Intercept)         group2            group3        
 Min.   :-1.817   Min.   :-5.0171   Min.   :-0.64646  
 1st Qu.:-1.812   1st Qu.:-1.1565   1st Qu.:-0.13919  
 Median :-1.712   Median : 0.1994   Median :-0.10441  
 Mean   :-1.625   Mean   :-0.9523   Mean   :-0.04217  
 3rd Qu.:-1.429   3rd Qu.: 0.3755   3rd Qu.:-0.04305  
 Max.   :-1.356   Max.   : 0.8374   Max.   : 0.72227  
> 
> lrt <- glmLRT(fit,contrast=cbind(c(0,1,0),c(0,0,1)))
> topTags(lrt)
Coefficient:  LR test on 2 degrees of freedom 
     logFC.1    logFC.2   logCPM         LR      PValue        FDR
1 -7.2381060 -0.0621100 17.19071 10.7712171 0.004582051 0.02291026
5 -1.6684268 -0.9326507 17.33529  1.7309951 0.420842115 0.90967967
2  1.2080938  1.0420198 18.24544  1.0496688 0.591653347 0.90967967
4  0.5416704 -0.1506381 17.57744  0.3958596 0.820427427 0.90967967
3  0.2876249 -0.2008143 18.06216  0.1893255 0.909679672 0.90967967
> design <- model.matrix(~0+group)
> fit <- glmFit(y,design,dispersion=2/3,prior.count=0.5/7)
> lrt <- glmLRT(fit,contrast=cbind(c(-1,1,0),c(0,-1,1),c(-1,0,1)))
> topTags(lrt)
Coefficient:  LR test on 2 degrees of freedom 
     logFC.1    logFC.2    logFC.3   logCPM         LR      PValue        FDR
1 -7.2381060  7.1759960 -0.0621100 17.19071 10.7712171 0.004582051 0.02291026
5 -1.6684268  0.7357761 -0.9326507 17.33529  1.7309951 0.420842115 0.90967967
2  1.2080938 -0.1660740  1.0420198 18.24544  1.0496688 0.591653347 0.90967967
4  0.5416704 -0.6923084 -0.1506381 17.57744  0.3958596 0.820427427 0.90967967
3  0.2876249 -0.4884392 -0.2008143 18.06216  0.1893255 0.909679672 0.90967967
> 
> # simple Good-Turing algorithm runs.
> test1<-1:9
> freq1<-c(2018046, 449721, 188933, 105668, 68379, 48190, 35709, 37710, 22280)
> goodTuring(rep(test1, freq1))
$P0
[1] 0.3814719

$proportion
[1] 8.035111e-08 2.272143e-07 4.060582e-07 5.773690e-07 7.516705e-07
[6] 9.276808e-07 1.104759e-06 1.282549e-06 1.460837e-06

$count
[1] 1 2 3 4 5 6 7 8 9

$n
[1] 2018046  449721  188933  105668   68379   48190   35709   37710   22280

$n0
[1] 0

> test2<-c(312, 14491, 16401, 65124, 129797, 323321, 366051, 368599, 405261, 604962)
> goodTuring(test2)
$P0
[1] 0

$proportion
 [1] 0.0001362656 0.0063162959 0.0071487846 0.0283850925 0.0565733349
 [6] 0.1409223124 0.1595465235 0.1606570896 0.1766365144 0.2636777866

$count
 [1]    312  14491  16401  65124 129797 323321 366051 368599 405261 604962

$n
 [1] 1 1 1 1 1 1 1 1 1 1

$n0
[1] 0

> 
> 
> 
> proc.time()
   user  system elapsed 
   2.17    0.23    2.40 

edgeR.Rcheck/tests_i386/edgeR-Tests.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.

> library(edgeR)
Loading required package: limma
> options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE)
> 
> set.seed(0); u <- runif(100)
> 
> # generate raw counts from NB, create list object
> y <- matrix(rnbinom(80,size=5,mu=10),nrow=20)
> y <- rbind(0,c(0,0,2,2),y)
> rownames(y) <- paste("Tag",1:nrow(y),sep=".")
> d <- DGEList(counts=y,group=rep(1:2,each=2),lib.size=1001:1004)
> 
> filterByExpr(d)
 Tag.1  Tag.2  Tag.3  Tag.4  Tag.5  Tag.6  Tag.7  Tag.8  Tag.9 Tag.10 Tag.11 
 FALSE  FALSE  FALSE   TRUE  FALSE  FALSE   TRUE  FALSE  FALSE   TRUE   TRUE 
Tag.12 Tag.13 Tag.14 Tag.15 Tag.16 Tag.17 Tag.18 Tag.19 Tag.20 Tag.21 Tag.22 
  TRUE   TRUE  FALSE  FALSE   TRUE   TRUE  FALSE  FALSE   TRUE   TRUE   TRUE 
> 
> # estimate common dispersion and find differences in expression
> d <- estimateCommonDisp(d)
> d$common.dispersion
[1] 0.210292
> de <- exactTest(d)
> summary(de$table)
     logFC             logCPM          PValue       
 Min.   :-1.7266   Min.   :10.96   Min.   :0.01976  
 1st Qu.:-0.4855   1st Qu.:13.21   1st Qu.:0.33120  
 Median : 0.2253   Median :13.37   Median :0.56514  
 Mean   : 0.1877   Mean   :13.26   Mean   :0.54504  
 3rd Qu.: 0.5258   3rd Qu.:13.70   3rd Qu.:0.81052  
 Max.   : 4.0861   Max.   :14.31   Max.   :1.00000  
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450964 13.73726 0.01975954 0.4347099
Tag.21 -1.7265870 13.38327 0.06131012 0.6744114
Tag.6  -1.6329986 12.81479 0.12446044 0.8982100
Tag.2   4.0861092 11.54121 0.16331090 0.8982100
Tag.16  0.9324996 13.57074 0.29050785 0.9655885
Tag.20  0.8543138 13.76364 0.31736609 0.9655885
Tag.12  0.7081170 14.31389 0.37271028 0.9655885
Tag.19 -0.7976602 13.31405 0.40166354 0.9655885
Tag.3  -0.7300410 13.54155 0.42139935 0.9655885
Tag.8  -0.7917906 12.86353 0.47117217 0.9655885
> 
> d2 <- estimateTagwiseDisp(d,trend="none",prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1757  0.1896  0.1989  0.2063  0.2185  0.2677 
> de <- exactTest(d2,dispersion="common")
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450964 13.73726 0.01975954 0.4347099
Tag.21 -1.7265870 13.38327 0.06131012 0.6744114
Tag.6  -1.6329986 12.81479 0.12446044 0.8982100
Tag.2   4.0861092 11.54121 0.16331090 0.8982100
Tag.16  0.9324996 13.57074 0.29050785 0.9655885
Tag.20  0.8543138 13.76364 0.31736609 0.9655885
Tag.12  0.7081170 14.31389 0.37271028 0.9655885
Tag.19 -0.7976602 13.31405 0.40166354 0.9655885
Tag.3  -0.7300410 13.54155 0.42139935 0.9655885
Tag.8  -0.7917906 12.86353 0.47117217 0.9655885
> 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450987 13.73726 0.01327001 0.2919403
Tag.21 -1.7265897 13.38327 0.05683886 0.6252275
Tag.6  -1.6329910 12.81479 0.11460208 0.8404152
Tag.2   4.0861092 11.54121 0.16126207 0.8869414
Tag.16  0.9324975 13.57074 0.28103256 0.9669238
Tag.20  0.8543178 13.76364 0.30234789 0.9669238
Tag.12  0.7081149 14.31389 0.37917895 0.9669238
Tag.19 -0.7976633 13.31405 0.40762735 0.9669238
Tag.3  -0.7300478 13.54155 0.40856822 0.9669238
Tag.8  -0.7918243 12.86353 0.49005179 0.9669238
> 
> d2 <- estimateTagwiseDisp(d,trend="movingave",span=0.4,prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1005  0.1629  0.2064  0.2077  0.2585  0.3164 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450951 13.73726 0.02427872 0.5341319
Tag.21 -1.7265927 13.38327 0.05234833 0.5758316
Tag.6  -1.6330014 12.81479 0.12846308 0.8954397
Tag.2   4.0861092 11.54121 0.16280722 0.8954397
Tag.16  0.9324887 13.57074 0.24308201 0.9711975
Tag.20  0.8543044 13.76364 0.35534649 0.9711975
Tag.19 -0.7976535 13.31405 0.38873717 0.9711975
Tag.3  -0.7300525 13.54155 0.40001438 0.9711975
Tag.12  0.7080985 14.31389 0.43530227 0.9711975
Tag.8  -0.7918376 12.86353 0.49782701 0.9711975
> 
> summary(exactTest(d2,rejection.region="smallp")$table$PValue)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.02428 0.36369 0.55662 0.54319 0.78889 1.00000 
> summary(exactTest(d2,rejection.region="deviance")$table$PValue)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.02428 0.36369 0.55662 0.54319 0.78889 1.00000 
> 
> d2 <- estimateTagwiseDisp(d,trend="loess",span=0.8,prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1165  0.1449  0.1832  0.1848  0.2116  0.2825 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450979 13.73726 0.01546795 0.3402949
Tag.21 -1.7266049 13.38327 0.03545446 0.3899990
Tag.6  -1.6329841 12.81479 0.10632987 0.7797524
Tag.2   4.0861092 11.54121 0.16057893 0.8831841
Tag.16  0.9324935 13.57074 0.26348818 0.9658389
Tag.20  0.8543140 13.76364 0.31674090 0.9658389
Tag.19 -0.7976354 13.31405 0.35564858 0.9658389
Tag.3  -0.7300593 13.54155 0.38833737 0.9658389
Tag.12  0.7081041 14.31389 0.41513004 0.9658389
Tag.8  -0.7918152 12.86353 0.48483449 0.9658389
> 
> d2 <- estimateTagwiseDisp(d,trend="tricube",span=0.8,prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1165  0.1449  0.1832  0.1848  0.2116  0.2825 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450979 13.73726 0.01546795 0.3402949
Tag.21 -1.7266049 13.38327 0.03545446 0.3899990
Tag.6  -1.6329841 12.81479 0.10632987 0.7797524
Tag.2   4.0861092 11.54121 0.16057893 0.8831841
Tag.16  0.9324935 13.57074 0.26348818 0.9658389
Tag.20  0.8543140 13.76364 0.31674090 0.9658389
Tag.19 -0.7976354 13.31405 0.35564858 0.9658389
Tag.3  -0.7300593 13.54155 0.38833737 0.9658389
Tag.12  0.7081041 14.31389 0.41513004 0.9658389
Tag.8  -0.7918152 12.86353 0.48483449 0.9658389
> 
> # mglmOneWay
> design <- model.matrix(~group,data=d$samples)
> mglmOneWay(d[1:10,],design,dispersion=0.2)
$coefficients
         (Intercept)        group2
Tag.1  -1.000000e+08  0.000000e+00
Tag.2  -1.000000e+08  1.000000e+08
Tag.3   2.525729e+00 -5.108256e-01
Tag.4   2.525729e+00  1.484200e-01
Tag.5   2.140066e+00 -1.941560e-01
Tag.6   2.079442e+00 -1.163151e+00
Tag.7   2.014903e+00  2.363888e-01
Tag.8   1.945910e+00 -5.596158e-01
Tag.9   1.504077e+00  2.006707e-01
Tag.10  2.302585e+00  2.623643e-01

$fitted.values
       Sample1 Sample2 Sample3 Sample4
Tag.1      0.0     0.0     0.0     0.0
Tag.2      0.0     0.0     2.0     2.0
Tag.3     12.5    12.5     7.5     7.5
Tag.4     12.5    12.5    14.5    14.5
Tag.5      8.5     8.5     7.0     7.0
Tag.6      8.0     8.0     2.5     2.5
Tag.7      7.5     7.5     9.5     9.5
Tag.8      7.0     7.0     4.0     4.0
Tag.9      4.5     4.5     5.5     5.5
Tag.10    10.0    10.0    13.0    13.0

> mglmOneWay(d[1:10,],design,dispersion=0)
$coefficients
         (Intercept)        group2
Tag.1  -1.000000e+08  0.000000e+00
Tag.2  -1.000000e+08  1.000000e+08
Tag.3   2.525729e+00 -5.108256e-01
Tag.4   2.525729e+00  1.484200e-01
Tag.5   2.140066e+00 -1.941560e-01
Tag.6   2.079442e+00 -1.163151e+00
Tag.7   2.014903e+00  2.363888e-01
Tag.8   1.945910e+00 -5.596158e-01
Tag.9   1.504077e+00  2.006707e-01
Tag.10  2.302585e+00  2.623643e-01

$fitted.values
       Sample1 Sample2 Sample3 Sample4
Tag.1      0.0     0.0     0.0     0.0
Tag.2      0.0     0.0     2.0     2.0
Tag.3     12.5    12.5     7.5     7.5
Tag.4     12.5    12.5    14.5    14.5
Tag.5      8.5     8.5     7.0     7.0
Tag.6      8.0     8.0     2.5     2.5
Tag.7      7.5     7.5     9.5     9.5
Tag.8      7.0     7.0     4.0     4.0
Tag.9      4.5     4.5     5.5     5.5
Tag.10    10.0    10.0    13.0    13.0

> 
> fit <- glmFit(d,design,dispersion=d$common.dispersion,prior.count=0.5/4)
> lrt <- glmLRT(fit,coef=2)
> topTags(lrt)
Coefficient:  group2 
            logFC   logCPM        LR     PValue       FDR
Tag.17  2.0450964 13.73726 6.0485417 0.01391779 0.3058698
Tag.2   4.0861092 11.54121 4.8400340 0.02780635 0.3058698
Tag.21 -1.7265870 13.38327 4.0777825 0.04345065 0.3186381
Tag.6  -1.6329986 12.81479 3.0078205 0.08286364 0.4557500
Tag.16  0.9324996 13.57074 1.3477682 0.24566867 0.8276702
Tag.20  0.8543138 13.76364 1.1890032 0.27553071 0.8276702
Tag.19 -0.7976602 13.31405 0.9279151 0.33540526 0.8276702
Tag.12  0.7081170 14.31389 0.9095513 0.34023349 0.8276702
Tag.3  -0.7300410 13.54155 0.8300307 0.36226364 0.8276702
Tag.8  -0.7917906 12.86353 0.7830377 0.37621371 0.8276702
> 
> fit <- glmFit(d,design,dispersion=d$common.dispersion,prior.count=0.5)
> summary(fit$coefficients)
  (Intercept)         group2        
 Min.   :-7.604   Min.   :-1.13681  
 1st Qu.:-4.895   1st Qu.:-0.32341  
 Median :-4.713   Median : 0.15083  
 Mean   :-4.940   Mean   : 0.07817  
 3rd Qu.:-4.524   3rd Qu.: 0.35163  
 Max.   :-4.107   Max.   : 1.60864  
> 
> fit <- glmFit(d,design,prior.count=0.5/4)
> lrt <- glmLRT(fit,coef=2)
> topTags(lrt)
Coefficient:  group2 
            logFC   logCPM        LR     PValue       FDR
Tag.17  2.0450964 13.73726 6.0485417 0.01391779 0.3058698
Tag.2   4.0861092 11.54121 4.8400340 0.02780635 0.3058698
Tag.21 -1.7265870 13.38327 4.0777825 0.04345065 0.3186381
Tag.6  -1.6329986 12.81479 3.0078205 0.08286364 0.4557500
Tag.16  0.9324996 13.57074 1.3477682 0.24566867 0.8276702
Tag.20  0.8543138 13.76364 1.1890032 0.27553071 0.8276702
Tag.19 -0.7976602 13.31405 0.9279151 0.33540526 0.8276702
Tag.12  0.7081170 14.31389 0.9095513 0.34023349 0.8276702
Tag.3  -0.7300410 13.54155 0.8300307 0.36226364 0.8276702
Tag.8  -0.7917906 12.86353 0.7830377 0.37621371 0.8276702
> 
> dglm <- estimateGLMCommonDisp(d,design)
> dglm$common.dispersion
[1] 0.2033282
> dglm <- estimateGLMTagwiseDisp(dglm,design,prior.df=20)
> summary(dglm$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1756  0.1879  0.1998  0.2031  0.2135  0.2578 
> fit <- glmFit(dglm,design,prior.count=0.5/4)
> lrt <- glmLRT(fit,coef=2)
> topTags(lrt)
Coefficient:  group2 
            logFC   logCPM        LR      PValue       FDR
Tag.17  2.0450988 13.73727 6.8001118 0.009115216 0.2005348
Tag.2   4.0861092 11.54122 4.8594088 0.027495756 0.2872068
Tag.21 -1.7265904 13.38327 4.2537154 0.039164558 0.2872068
Tag.6  -1.6329904 12.81479 3.1763761 0.074710253 0.4109064
Tag.16  0.9324970 13.57074 1.4126709 0.234613512 0.8499599
Tag.20  0.8543183 13.76364 1.2721097 0.259371274 0.8499599
Tag.19 -0.7976614 13.31405 0.9190392 0.337727381 0.8499599
Tag.12  0.7081163 14.31389 0.9014515 0.342392806 0.8499599
Tag.3  -0.7300488 13.54155 0.8817937 0.347710872 0.8499599
Tag.8  -0.7918166 12.86353 0.7356185 0.391068049 0.8603497
> dglm <- estimateGLMTrendedDisp(dglm,design)
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1522  0.1676  0.1740  0.1887  0.2000  0.3469 
> dglm <- estimateGLMTrendedDisp(dglm,design,method="power")
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1522  0.1676  0.1740  0.1887  0.2000  0.3469 
> dglm <- estimateGLMTrendedDisp(dglm,design,method="spline")
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.09353 0.11082 0.15463 0.19006 0.23050 0.52006 
> dglm <- estimateGLMTrendedDisp(dglm,design,method="bin.spline")
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1997  0.1997  0.1997  0.1997  0.1997  0.1997 
> dglm <- estimateGLMTagwiseDisp(dglm,design,prior.df=20)
> summary(dglm$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1385  0.1792  0.1964  0.1935  0.2026  0.2709 
> 
> dglm2 <- estimateDisp(dglm, design)
> summary(dglm2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1652  0.1740  0.1821  0.1852  0.1909  0.2259 
> dglm2 <- estimateDisp(dglm, design, prior.df=20)
> summary(dglm2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1527  0.1669  0.1814  0.1858  0.1951  0.2497 
> dglm2 <- estimateDisp(dglm, design, robust=TRUE)
> summary(dglm2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1652  0.1735  0.1822  0.1854  0.1905  0.2280 
> 
> # Continuous trend
> nlibs <- 3
> ntags <- 1000
> dispersion.true <- 0.1
> # Make first transcript respond to covariate x
> x <- 0:2
> design <- model.matrix(~x)
> beta.true <- cbind(Beta1=2,Beta2=c(2,rep(0,ntags-1)))
> mu.true <- 2^(beta.true %*% t(design))
> # Generate count data
> y <- rnbinom(ntags*nlibs,mu=mu.true,size=1/dispersion.true)
> y <- matrix(y,ntags,nlibs)
> colnames(y) <- c("x0","x1","x2")
> rownames(y) <- paste("Gene",1:ntags,sep="")
> d <- DGEList(y)
> d <- calcNormFactors(d,method="TMM")
> fit <- glmFit(d, design, dispersion=dispersion.true, prior.count=0.5/3)
> results <- glmLRT(fit, coef=2)
> topTags(results)
Coefficient:  x 
            logFC   logCPM        LR       PValue          FDR
Gene1    2.907024 13.56183 38.738512 4.845536e-10 4.845536e-07
Gene61   2.855317 10.27136 10.738307 1.049403e-03 5.247015e-01
Gene62  -2.123902 10.53174  8.818703 2.981585e-03 8.334760e-01
Gene134 -1.949073 10.53355  8.125889 4.363759e-03 8.334760e-01
Gene740 -1.610046 10.94907  8.013408 4.643227e-03 8.334760e-01
Gene354  2.022698 10.45066  7.826308 5.149118e-03 8.334760e-01
Gene5    1.856816 10.45249  7.214238 7.232750e-03 8.334760e-01
Gene746 -1.798331 10.53094  6.846262 8.882693e-03 8.334760e-01
Gene110  1.623148 10.68607  6.737984 9.438120e-03 8.334760e-01
Gene383  1.637140 10.75412  6.687530 9.708965e-03 8.334760e-01
> d1 <- estimateGLMCommonDisp(d, design, verbose=TRUE)
Disp = 0.10253 , BCV = 0.3202 
> glmFit(d,design,dispersion=dispersion.true, prior.count=0.5/3)
An object of class "DGEGLM"
$coefficients
      (Intercept)          x
Gene1   -7.391745  2.0149958
Gene2   -7.318483 -0.7611895
Gene3   -6.831702 -0.1399478
Gene4   -7.480255  0.5172002
Gene5   -8.747793  1.2870467
995 more rows ...

$fitted.values
             x0        x1          x2
Gene1 2.3570471 18.954454 138.2791328
Gene2 2.5138172  1.089292   0.4282107
Gene3 4.1580452  3.750528   3.0690081
Gene4 2.1012460  3.769592   6.1349937
Gene5 0.5080377  2.136398   8.1502486
995 more rows ...

$deviance
[1] 6.38037545 1.46644913 1.38532340 0.01593969 1.03894513
995 more elements ...

$iter
[1] 8 4 4 4 6
995 more elements ...

$failed
[1] FALSE FALSE FALSE FALSE FALSE
995 more elements ...

$method
[1] "levenberg"

$counts
      x0 x1  x2
Gene1  0 30 110
Gene2  2  2   0
Gene3  3  6   2
Gene4  2  4   6
Gene5  1  1   9
995 more rows ...

$unshrunk.coefficients
      (Intercept)          x
Gene1   -7.437763  2.0412762
Gene2   -7.373370 -0.8796273
Gene3   -6.870127 -0.1465014
Gene4   -7.552642  0.5410832
Gene5   -8.972372  1.3929679
995 more rows ...

$df.residual
[1] 1 1 1 1 1
995 more elements ...

$design
  (Intercept) x
1           1 0
2           1 1
3           1 2
attr(,"assign")
[1] 0 1

$offset
         [,1]     [,2]     [,3]
[1,] 8.295172 8.338525 8.284484
attr(,"class")
[1] "CompressedMatrix"
attr(,"Dims")
[1] 5 3
attr(,"repeat.row")
[1] TRUE
attr(,"repeat.col")
[1] FALSE
995 more rows ...

$dispersion
[1] 0.1

$prior.count
[1] 0.1666667

$samples
   group lib.size norm.factors
x0     1     4001    1.0008730
x1     1     4176    1.0014172
x2     1     3971    0.9977138

$AveLogCPM
[1] 13.561832  9.682757 10.447014 10.532113 10.452489
995 more elements ...

> 
> calcNormFactors(d$counts,method="TMMwsp")
       x0        x1        x2 
0.9992437 1.0077007 0.9931093 
> 
> d2 <- estimateDisp(d, design)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.05545 0.09511 0.11623 0.11014 0.13329 0.16861 
> d2 <- estimateDisp(d, design, prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.04203 0.08586 0.11280 0.11010 0.12369 0.37408 
> d2 <- estimateDisp(d, design, robust=TRUE)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.05545 0.09511 0.11623 0.11014 0.13329 0.16861 
> 
> # Exact tests
> y <- matrix(rnbinom(20,mu=10,size=3/2),5,4)
> group <- factor(c(1,1,2,2))
> ys <- splitIntoGroupsPseudo(y,group,pair=c(1,2))
> exactTestDoubleTail(ys$y1,ys$y2,dispersion=2/3)
[1] 0.1334396 0.6343568 0.7280015 0.7124912 0.3919258
> 
> y <- matrix(rnbinom(5*7,mu=10,size=3/2),5,7)
> group <- factor(c(1,1,2,2,3,3,3))
> ys <- splitIntoGroupsPseudo(y,group,pair=c(1,3))
> exactTestDoubleTail(ys$y1,ys$y2,dispersion=2/3)
[1] 1.0000000 0.4486382 1.0000000 0.9390317 0.4591241
> exactTestBetaApprox(ys$y1,ys$y2,dispersion=2/3)
[1] 1.0000000 0.4492969 1.0000000 0.9421695 0.4589194
> 
> y[1,3:4] <- 0
> design <- model.matrix(~group)
> fit <- glmFit(y,design,dispersion=2/3,prior.count=0.5/7)
> summary(fit$coefficients)
  (Intercept)         group2            group3        
 Min.   :-1.817   Min.   :-5.0171   Min.   :-0.64646  
 1st Qu.:-1.812   1st Qu.:-1.1565   1st Qu.:-0.13919  
 Median :-1.712   Median : 0.1994   Median :-0.10441  
 Mean   :-1.625   Mean   :-0.9523   Mean   :-0.04217  
 3rd Qu.:-1.429   3rd Qu.: 0.3755   3rd Qu.:-0.04305  
 Max.   :-1.356   Max.   : 0.8374   Max.   : 0.72227  
> 
> lrt <- glmLRT(fit,contrast=cbind(c(0,1,0),c(0,0,1)))
> topTags(lrt)
Coefficient:  LR test on 2 degrees of freedom 
     logFC.1    logFC.2   logCPM         LR      PValue        FDR
1 -7.2381060 -0.0621100 17.19071 10.7712171 0.004582051 0.02291026
5 -1.6684268 -0.9326507 17.33529  1.7309951 0.420842115 0.90967967
2  1.2080938  1.0420198 18.24544  1.0496688 0.591653347 0.90967967
4  0.5416704 -0.1506381 17.57744  0.3958596 0.820427427 0.90967967
3  0.2876249 -0.2008143 18.06216  0.1893255 0.909679672 0.90967967
> design <- model.matrix(~0+group)
> fit <- glmFit(y,design,dispersion=2/3,prior.count=0.5/7)
> lrt <- glmLRT(fit,contrast=cbind(c(-1,1,0),c(0,-1,1),c(-1,0,1)))
> topTags(lrt)
Coefficient:  LR test on 2 degrees of freedom 
     logFC.1    logFC.2    logFC.3   logCPM         LR      PValue        FDR
1 -7.2381060  7.1759960 -0.0621100 17.19071 10.7712171 0.004582051 0.02291026
5 -1.6684268  0.7357761 -0.9326507 17.33529  1.7309951 0.420842115 0.90967967
2  1.2080938 -0.1660740  1.0420198 18.24544  1.0496688 0.591653347 0.90967967
4  0.5416704 -0.6923084 -0.1506381 17.57744  0.3958596 0.820427427 0.90967967
3  0.2876249 -0.4884392 -0.2008143 18.06216  0.1893255 0.909679672 0.90967967
> 
> # simple Good-Turing algorithm runs.
> test1<-1:9
> freq1<-c(2018046, 449721, 188933, 105668, 68379, 48190, 35709, 37710, 22280)
> goodTuring(rep(test1, freq1))
$P0
[1] 0.3814719

$proportion
[1] 8.035111e-08 2.272143e-07 4.060582e-07 5.773690e-07 7.516705e-07
[6] 9.276808e-07 1.104759e-06 1.282549e-06 1.460837e-06

$count
[1] 1 2 3 4 5 6 7 8 9

$n
[1] 2018046  449721  188933  105668   68379   48190   35709   37710   22280

$n0
[1] 0

> test2<-c(312, 14491, 16401, 65124, 129797, 323321, 366051, 368599, 405261, 604962)
> goodTuring(test2)
$P0
[1] 0

$proportion
 [1] 0.0001362656 0.0063162959 0.0071487846 0.0283850925 0.0565733349
 [6] 0.1409223124 0.1595465235 0.1606570896 0.1766365144 0.2636777866

$count
 [1]    312  14491  16401  65124 129797 323321 366051 368599 405261 604962

$n
 [1] 1 1 1 1 1 1 1 1 1 1

$n0
[1] 0

> 
> 
> 
> proc.time()
   user  system elapsed 
   2.53    0.06    2.57 

edgeR.Rcheck/tests_x64/edgeR-Tests.Rout


R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

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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.

> library(edgeR)
Loading required package: limma
> options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE)
> 
> set.seed(0); u <- runif(100)
> 
> # generate raw counts from NB, create list object
> y <- matrix(rnbinom(80,size=5,mu=10),nrow=20)
> y <- rbind(0,c(0,0,2,2),y)
> rownames(y) <- paste("Tag",1:nrow(y),sep=".")
> d <- DGEList(counts=y,group=rep(1:2,each=2),lib.size=1001:1004)
> 
> filterByExpr(d)
 Tag.1  Tag.2  Tag.3  Tag.4  Tag.5  Tag.6  Tag.7  Tag.8  Tag.9 Tag.10 Tag.11 
 FALSE  FALSE  FALSE   TRUE  FALSE  FALSE   TRUE  FALSE  FALSE   TRUE   TRUE 
Tag.12 Tag.13 Tag.14 Tag.15 Tag.16 Tag.17 Tag.18 Tag.19 Tag.20 Tag.21 Tag.22 
  TRUE   TRUE  FALSE  FALSE   TRUE   TRUE  FALSE  FALSE   TRUE   TRUE   TRUE 
> 
> # estimate common dispersion and find differences in expression
> d <- estimateCommonDisp(d)
> d$common.dispersion
[1] 0.210292
> de <- exactTest(d)
> summary(de$table)
     logFC             logCPM          PValue       
 Min.   :-1.7266   Min.   :10.96   Min.   :0.01976  
 1st Qu.:-0.4855   1st Qu.:13.21   1st Qu.:0.33120  
 Median : 0.2253   Median :13.37   Median :0.56514  
 Mean   : 0.1877   Mean   :13.26   Mean   :0.54504  
 3rd Qu.: 0.5258   3rd Qu.:13.70   3rd Qu.:0.81052  
 Max.   : 4.0861   Max.   :14.31   Max.   :1.00000  
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450964 13.73726 0.01975954 0.4347099
Tag.21 -1.7265870 13.38327 0.06131012 0.6744114
Tag.6  -1.6329986 12.81479 0.12446044 0.8982100
Tag.2   4.0861092 11.54121 0.16331090 0.8982100
Tag.16  0.9324996 13.57074 0.29050785 0.9655885
Tag.20  0.8543138 13.76364 0.31736609 0.9655885
Tag.12  0.7081170 14.31389 0.37271028 0.9655885
Tag.19 -0.7976602 13.31405 0.40166354 0.9655885
Tag.3  -0.7300410 13.54155 0.42139935 0.9655885
Tag.8  -0.7917906 12.86353 0.47117217 0.9655885
> 
> d2 <- estimateTagwiseDisp(d,trend="none",prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1757  0.1896  0.1989  0.2063  0.2185  0.2677 
> de <- exactTest(d2,dispersion="common")
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450964 13.73726 0.01975954 0.4347099
Tag.21 -1.7265870 13.38327 0.06131012 0.6744114
Tag.6  -1.6329986 12.81479 0.12446044 0.8982100
Tag.2   4.0861092 11.54121 0.16331090 0.8982100
Tag.16  0.9324996 13.57074 0.29050785 0.9655885
Tag.20  0.8543138 13.76364 0.31736609 0.9655885
Tag.12  0.7081170 14.31389 0.37271028 0.9655885
Tag.19 -0.7976602 13.31405 0.40166354 0.9655885
Tag.3  -0.7300410 13.54155 0.42139935 0.9655885
Tag.8  -0.7917906 12.86353 0.47117217 0.9655885
> 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450987 13.73726 0.01327001 0.2919403
Tag.21 -1.7265897 13.38327 0.05683886 0.6252275
Tag.6  -1.6329910 12.81479 0.11460208 0.8404152
Tag.2   4.0861092 11.54121 0.16126207 0.8869414
Tag.16  0.9324975 13.57074 0.28103256 0.9669238
Tag.20  0.8543178 13.76364 0.30234789 0.9669238
Tag.12  0.7081149 14.31389 0.37917895 0.9669238
Tag.19 -0.7976633 13.31405 0.40762735 0.9669238
Tag.3  -0.7300478 13.54155 0.40856822 0.9669238
Tag.8  -0.7918243 12.86353 0.49005179 0.9669238
> 
> d2 <- estimateTagwiseDisp(d,trend="movingave",span=0.4,prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1005  0.1629  0.2064  0.2077  0.2585  0.3164 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450951 13.73726 0.02427872 0.5341319
Tag.21 -1.7265927 13.38327 0.05234833 0.5758316
Tag.6  -1.6330014 12.81479 0.12846308 0.8954397
Tag.2   4.0861092 11.54121 0.16280722 0.8954397
Tag.16  0.9324887 13.57074 0.24308201 0.9711975
Tag.20  0.8543044 13.76364 0.35534649 0.9711975
Tag.19 -0.7976535 13.31405 0.38873717 0.9711975
Tag.3  -0.7300525 13.54155 0.40001438 0.9711975
Tag.12  0.7080985 14.31389 0.43530227 0.9711975
Tag.8  -0.7918376 12.86353 0.49782701 0.9711975
> 
> summary(exactTest(d2,rejection.region="smallp")$table$PValue)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.02428 0.36369 0.55662 0.54319 0.78889 1.00000 
> summary(exactTest(d2,rejection.region="deviance")$table$PValue)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.02428 0.36369 0.55662 0.54319 0.78889 1.00000 
> 
> d2 <- estimateTagwiseDisp(d,trend="loess",span=0.8,prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1165  0.1449  0.1832  0.1848  0.2116  0.2825 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450979 13.73726 0.01546795 0.3402949
Tag.21 -1.7266049 13.38327 0.03545446 0.3899990
Tag.6  -1.6329841 12.81479 0.10632987 0.7797524
Tag.2   4.0861092 11.54121 0.16057893 0.8831841
Tag.16  0.9324935 13.57074 0.26348818 0.9658389
Tag.20  0.8543140 13.76364 0.31674090 0.9658389
Tag.19 -0.7976354 13.31405 0.35564858 0.9658389
Tag.3  -0.7300593 13.54155 0.38833737 0.9658389
Tag.12  0.7081041 14.31389 0.41513004 0.9658389
Tag.8  -0.7918152 12.86353 0.48483449 0.9658389
> 
> d2 <- estimateTagwiseDisp(d,trend="tricube",span=0.8,prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1165  0.1449  0.1832  0.1848  0.2116  0.2825 
> de <- exactTest(d2)
> topTags(de)
Comparison of groups:  2-1 
            logFC   logCPM     PValue       FDR
Tag.17  2.0450979 13.73726 0.01546795 0.3402949
Tag.21 -1.7266049 13.38327 0.03545446 0.3899990
Tag.6  -1.6329841 12.81479 0.10632987 0.7797524
Tag.2   4.0861092 11.54121 0.16057893 0.8831841
Tag.16  0.9324935 13.57074 0.26348818 0.9658389
Tag.20  0.8543140 13.76364 0.31674090 0.9658389
Tag.19 -0.7976354 13.31405 0.35564858 0.9658389
Tag.3  -0.7300593 13.54155 0.38833737 0.9658389
Tag.12  0.7081041 14.31389 0.41513004 0.9658389
Tag.8  -0.7918152 12.86353 0.48483449 0.9658389
> 
> # mglmOneWay
> design <- model.matrix(~group,data=d$samples)
> mglmOneWay(d[1:10,],design,dispersion=0.2)
$coefficients
         (Intercept)        group2
Tag.1  -1.000000e+08  0.000000e+00
Tag.2  -1.000000e+08  1.000000e+08
Tag.3   2.525729e+00 -5.108256e-01
Tag.4   2.525729e+00  1.484200e-01
Tag.5   2.140066e+00 -1.941560e-01
Tag.6   2.079442e+00 -1.163151e+00
Tag.7   2.014903e+00  2.363888e-01
Tag.8   1.945910e+00 -5.596158e-01
Tag.9   1.504077e+00  2.006707e-01
Tag.10  2.302585e+00  2.623643e-01

$fitted.values
       Sample1 Sample2 Sample3 Sample4
Tag.1      0.0     0.0     0.0     0.0
Tag.2      0.0     0.0     2.0     2.0
Tag.3     12.5    12.5     7.5     7.5
Tag.4     12.5    12.5    14.5    14.5
Tag.5      8.5     8.5     7.0     7.0
Tag.6      8.0     8.0     2.5     2.5
Tag.7      7.5     7.5     9.5     9.5
Tag.8      7.0     7.0     4.0     4.0
Tag.9      4.5     4.5     5.5     5.5
Tag.10    10.0    10.0    13.0    13.0

> mglmOneWay(d[1:10,],design,dispersion=0)
$coefficients
         (Intercept)        group2
Tag.1  -1.000000e+08  0.000000e+00
Tag.2  -1.000000e+08  1.000000e+08
Tag.3   2.525729e+00 -5.108256e-01
Tag.4   2.525729e+00  1.484200e-01
Tag.5   2.140066e+00 -1.941560e-01
Tag.6   2.079442e+00 -1.163151e+00
Tag.7   2.014903e+00  2.363888e-01
Tag.8   1.945910e+00 -5.596158e-01
Tag.9   1.504077e+00  2.006707e-01
Tag.10  2.302585e+00  2.623643e-01

$fitted.values
       Sample1 Sample2 Sample3 Sample4
Tag.1      0.0     0.0     0.0     0.0
Tag.2      0.0     0.0     2.0     2.0
Tag.3     12.5    12.5     7.5     7.5
Tag.4     12.5    12.5    14.5    14.5
Tag.5      8.5     8.5     7.0     7.0
Tag.6      8.0     8.0     2.5     2.5
Tag.7      7.5     7.5     9.5     9.5
Tag.8      7.0     7.0     4.0     4.0
Tag.9      4.5     4.5     5.5     5.5
Tag.10    10.0    10.0    13.0    13.0

> 
> fit <- glmFit(d,design,dispersion=d$common.dispersion,prior.count=0.5/4)
> lrt <- glmLRT(fit,coef=2)
> topTags(lrt)
Coefficient:  group2 
            logFC   logCPM        LR     PValue       FDR
Tag.17  2.0450964 13.73726 6.0485417 0.01391779 0.3058698
Tag.2   4.0861092 11.54121 4.8400340 0.02780635 0.3058698
Tag.21 -1.7265870 13.38327 4.0777825 0.04345065 0.3186381
Tag.6  -1.6329986 12.81479 3.0078205 0.08286364 0.4557500
Tag.16  0.9324996 13.57074 1.3477682 0.24566867 0.8276702
Tag.20  0.8543138 13.76364 1.1890032 0.27553071 0.8276702
Tag.19 -0.7976602 13.31405 0.9279151 0.33540526 0.8276702
Tag.12  0.7081170 14.31389 0.9095513 0.34023349 0.8276702
Tag.3  -0.7300410 13.54155 0.8300307 0.36226364 0.8276702
Tag.8  -0.7917906 12.86353 0.7830377 0.37621371 0.8276702
> 
> fit <- glmFit(d,design,dispersion=d$common.dispersion,prior.count=0.5)
> summary(fit$coefficients)
  (Intercept)         group2        
 Min.   :-7.604   Min.   :-1.13681  
 1st Qu.:-4.895   1st Qu.:-0.32341  
 Median :-4.713   Median : 0.15083  
 Mean   :-4.940   Mean   : 0.07817  
 3rd Qu.:-4.524   3rd Qu.: 0.35163  
 Max.   :-4.107   Max.   : 1.60864  
> 
> fit <- glmFit(d,design,prior.count=0.5/4)
> lrt <- glmLRT(fit,coef=2)
> topTags(lrt)
Coefficient:  group2 
            logFC   logCPM        LR     PValue       FDR
Tag.17  2.0450964 13.73726 6.0485417 0.01391779 0.3058698
Tag.2   4.0861092 11.54121 4.8400340 0.02780635 0.3058698
Tag.21 -1.7265870 13.38327 4.0777825 0.04345065 0.3186381
Tag.6  -1.6329986 12.81479 3.0078205 0.08286364 0.4557500
Tag.16  0.9324996 13.57074 1.3477682 0.24566867 0.8276702
Tag.20  0.8543138 13.76364 1.1890032 0.27553071 0.8276702
Tag.19 -0.7976602 13.31405 0.9279151 0.33540526 0.8276702
Tag.12  0.7081170 14.31389 0.9095513 0.34023349 0.8276702
Tag.3  -0.7300410 13.54155 0.8300307 0.36226364 0.8276702
Tag.8  -0.7917906 12.86353 0.7830377 0.37621371 0.8276702
> 
> dglm <- estimateGLMCommonDisp(d,design)
> dglm$common.dispersion
[1] 0.2033282
> dglm <- estimateGLMTagwiseDisp(dglm,design,prior.df=20)
> summary(dglm$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1756  0.1879  0.1998  0.2031  0.2135  0.2578 
> fit <- glmFit(dglm,design,prior.count=0.5/4)
> lrt <- glmLRT(fit,coef=2)
> topTags(lrt)
Coefficient:  group2 
            logFC   logCPM        LR      PValue       FDR
Tag.17  2.0450988 13.73727 6.8001118 0.009115216 0.2005348
Tag.2   4.0861092 11.54122 4.8594088 0.027495756 0.2872068
Tag.21 -1.7265904 13.38327 4.2537154 0.039164558 0.2872068
Tag.6  -1.6329904 12.81479 3.1763761 0.074710253 0.4109064
Tag.16  0.9324970 13.57074 1.4126709 0.234613512 0.8499599
Tag.20  0.8543183 13.76364 1.2721097 0.259371274 0.8499599
Tag.19 -0.7976614 13.31405 0.9190392 0.337727381 0.8499599
Tag.12  0.7081163 14.31389 0.9014515 0.342392806 0.8499599
Tag.3  -0.7300488 13.54155 0.8817937 0.347710872 0.8499599
Tag.8  -0.7918166 12.86353 0.7356185 0.391068049 0.8603497
> dglm <- estimateGLMTrendedDisp(dglm,design)
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1522  0.1676  0.1740  0.1887  0.2000  0.3469 
> dglm <- estimateGLMTrendedDisp(dglm,design,method="power")
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1522  0.1676  0.1740  0.1887  0.2000  0.3469 
> dglm <- estimateGLMTrendedDisp(dglm,design,method="spline")
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.09353 0.11082 0.15463 0.19006 0.23050 0.52006 
> dglm <- estimateGLMTrendedDisp(dglm,design,method="bin.spline")
> summary(dglm$trended.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1997  0.1997  0.1997  0.1997  0.1997  0.1997 
> dglm <- estimateGLMTagwiseDisp(dglm,design,prior.df=20)
> summary(dglm$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1385  0.1792  0.1964  0.1935  0.2026  0.2709 
> 
> dglm2 <- estimateDisp(dglm, design)
> summary(dglm2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1652  0.1740  0.1821  0.1852  0.1909  0.2259 
> dglm2 <- estimateDisp(dglm, design, prior.df=20)
> summary(dglm2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1527  0.1669  0.1814  0.1858  0.1951  0.2497 
> dglm2 <- estimateDisp(dglm, design, robust=TRUE)
> summary(dglm2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1652  0.1735  0.1822  0.1854  0.1905  0.2280 
> 
> # Continuous trend
> nlibs <- 3
> ntags <- 1000
> dispersion.true <- 0.1
> # Make first transcript respond to covariate x
> x <- 0:2
> design <- model.matrix(~x)
> beta.true <- cbind(Beta1=2,Beta2=c(2,rep(0,ntags-1)))
> mu.true <- 2^(beta.true %*% t(design))
> # Generate count data
> y <- rnbinom(ntags*nlibs,mu=mu.true,size=1/dispersion.true)
> y <- matrix(y,ntags,nlibs)
> colnames(y) <- c("x0","x1","x2")
> rownames(y) <- paste("Gene",1:ntags,sep="")
> d <- DGEList(y)
> d <- calcNormFactors(d,method="TMM")
> fit <- glmFit(d, design, dispersion=dispersion.true, prior.count=0.5/3)
> results <- glmLRT(fit, coef=2)
> topTags(results)
Coefficient:  x 
            logFC   logCPM        LR       PValue          FDR
Gene1    2.907024 13.56183 38.738512 4.845536e-10 4.845536e-07
Gene61   2.855317 10.27136 10.738307 1.049403e-03 5.247015e-01
Gene62  -2.123902 10.53174  8.818703 2.981585e-03 8.334760e-01
Gene134 -1.949073 10.53355  8.125889 4.363759e-03 8.334760e-01
Gene740 -1.610046 10.94907  8.013408 4.643227e-03 8.334760e-01
Gene354  2.022698 10.45066  7.826308 5.149118e-03 8.334760e-01
Gene5    1.856816 10.45249  7.214238 7.232750e-03 8.334760e-01
Gene746 -1.798331 10.53094  6.846262 8.882693e-03 8.334760e-01
Gene110  1.623148 10.68607  6.737984 9.438120e-03 8.334760e-01
Gene383  1.637140 10.75412  6.687530 9.708965e-03 8.334760e-01
> d1 <- estimateGLMCommonDisp(d, design, verbose=TRUE)
Disp = 0.10253 , BCV = 0.3202 
> glmFit(d,design,dispersion=dispersion.true, prior.count=0.5/3)
An object of class "DGEGLM"
$coefficients
      (Intercept)          x
Gene1   -7.391745  2.0149958
Gene2   -7.318483 -0.7611895
Gene3   -6.831702 -0.1399478
Gene4   -7.480255  0.5172002
Gene5   -8.747793  1.2870467
995 more rows ...

$fitted.values
             x0        x1          x2
Gene1 2.3570471 18.954454 138.2791328
Gene2 2.5138172  1.089292   0.4282107
Gene3 4.1580452  3.750528   3.0690081
Gene4 2.1012460  3.769592   6.1349937
Gene5 0.5080377  2.136398   8.1502486
995 more rows ...

$deviance
[1] 6.38037545 1.46644913 1.38532340 0.01593969 1.03894513
995 more elements ...

$iter
[1] 8 4 4 4 6
995 more elements ...

$failed
[1] FALSE FALSE FALSE FALSE FALSE
995 more elements ...

$method
[1] "levenberg"

$counts
      x0 x1  x2
Gene1  0 30 110
Gene2  2  2   0
Gene3  3  6   2
Gene4  2  4   6
Gene5  1  1   9
995 more rows ...

$unshrunk.coefficients
      (Intercept)          x
Gene1   -7.437763  2.0412762
Gene2   -7.373370 -0.8796273
Gene3   -6.870127 -0.1465014
Gene4   -7.552642  0.5410832
Gene5   -8.972372  1.3929679
995 more rows ...

$df.residual
[1] 1 1 1 1 1
995 more elements ...

$design
  (Intercept) x
1           1 0
2           1 1
3           1 2
attr(,"assign")
[1] 0 1

$offset
         [,1]     [,2]     [,3]
[1,] 8.295172 8.338525 8.284484
attr(,"class")
[1] "CompressedMatrix"
attr(,"Dims")
[1] 5 3
attr(,"repeat.row")
[1] TRUE
attr(,"repeat.col")
[1] FALSE
995 more rows ...

$dispersion
[1] 0.1

$prior.count
[1] 0.1666667

$samples
   group lib.size norm.factors
x0     1     4001    1.0008730
x1     1     4176    1.0014172
x2     1     3971    0.9977138

$AveLogCPM
[1] 13.561832  9.682757 10.447014 10.532113 10.452489
995 more elements ...

> 
> calcNormFactors(d$counts,method="TMMwsp")
       x0        x1        x2 
0.9992437 1.0077007 0.9931093 
> 
> d2 <- estimateDisp(d, design)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.05545 0.09511 0.11623 0.11014 0.13329 0.16861 
> d2 <- estimateDisp(d, design, prior.df=20)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.04203 0.08586 0.11280 0.11010 0.12369 0.37408 
> d2 <- estimateDisp(d, design, robust=TRUE)
> summary(d2$tagwise.dispersion)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.05545 0.09511 0.11623 0.11014 0.13329 0.16861 
> 
> # Exact tests
> y <- matrix(rnbinom(20,mu=10,size=3/2),5,4)
> group <- factor(c(1,1,2,2))
> ys <- splitIntoGroupsPseudo(y,group,pair=c(1,2))
> exactTestDoubleTail(ys$y1,ys$y2,dispersion=2/3)
[1] 0.1334396 0.6343568 0.7280015 0.7124912 0.3919258
> 
> y <- matrix(rnbinom(5*7,mu=10,size=3/2),5,7)
> group <- factor(c(1,1,2,2,3,3,3))
> ys <- splitIntoGroupsPseudo(y,group,pair=c(1,3))
> exactTestDoubleTail(ys$y1,ys$y2,dispersion=2/3)
[1] 1.0000000 0.4486382 1.0000000 0.9390317 0.4591241
> exactTestBetaApprox(ys$y1,ys$y2,dispersion=2/3)
[1] 1.0000000 0.4492969 1.0000000 0.9421695 0.4589194
> 
> y[1,3:4] <- 0
> design <- model.matrix(~group)
> fit <- glmFit(y,design,dispersion=2/3,prior.count=0.5/7)
> summary(fit$coefficients)
  (Intercept)         group2            group3        
 Min.   :-1.817   Min.   :-5.0171   Min.   :-0.64646  
 1st Qu.:-1.812   1st Qu.:-1.1565   1st Qu.:-0.13919  
 Median :-1.712   Median : 0.1994   Median :-0.10441  
 Mean   :-1.625   Mean   :-0.9523   Mean   :-0.04217  
 3rd Qu.:-1.429   3rd Qu.: 0.3755   3rd Qu.:-0.04305  
 Max.   :-1.356   Max.   : 0.8374   Max.   : 0.72227  
> 
> lrt <- glmLRT(fit,contrast=cbind(c(0,1,0),c(0,0,1)))
> topTags(lrt)
Coefficient:  LR test on 2 degrees of freedom 
     logFC.1    logFC.2   logCPM         LR      PValue        FDR
1 -7.2381060 -0.0621100 17.19071 10.7712171 0.004582051 0.02291026
5 -1.6684268 -0.9326507 17.33529  1.7309951 0.420842115 0.90967967
2  1.2080938  1.0420198 18.24544  1.0496688 0.591653347 0.90967967
4  0.5416704 -0.1506381 17.57744  0.3958596 0.820427427 0.90967967
3  0.2876249 -0.2008143 18.06216  0.1893255 0.909679672 0.90967967
> design <- model.matrix(~0+group)
> fit <- glmFit(y,design,dispersion=2/3,prior.count=0.5/7)
> lrt <- glmLRT(fit,contrast=cbind(c(-1,1,0),c(0,-1,1),c(-1,0,1)))
> topTags(lrt)
Coefficient:  LR test on 2 degrees of freedom 
     logFC.1    logFC.2    logFC.3   logCPM         LR      PValue        FDR
1 -7.2381060  7.1759960 -0.0621100 17.19071 10.7712171 0.004582051 0.02291026
5 -1.6684268  0.7357761 -0.9326507 17.33529  1.7309951 0.420842115 0.90967967
2  1.2080938 -0.1660740  1.0420198 18.24544  1.0496688 0.591653347 0.90967967
4  0.5416704 -0.6923084 -0.1506381 17.57744  0.3958596 0.820427427 0.90967967
3  0.2876249 -0.4884392 -0.2008143 18.06216  0.1893255 0.909679672 0.90967967
> 
> # simple Good-Turing algorithm runs.
> test1<-1:9
> freq1<-c(2018046, 449721, 188933, 105668, 68379, 48190, 35709, 37710, 22280)
> goodTuring(rep(test1, freq1))
$P0
[1] 0.3814719

$proportion
[1] 8.035111e-08 2.272143e-07 4.060582e-07 5.773690e-07 7.516705e-07
[6] 9.276808e-07 1.104759e-06 1.282549e-06 1.460837e-06

$count
[1] 1 2 3 4 5 6 7 8 9

$n
[1] 2018046  449721  188933  105668   68379   48190   35709   37710   22280

$n0
[1] 0

> test2<-c(312, 14491, 16401, 65124, 129797, 323321, 366051, 368599, 405261, 604962)
> goodTuring(test2)
$P0
[1] 0

$proportion
 [1] 0.0001362656 0.0063162959 0.0071487846 0.0283850925 0.0565733349
 [6] 0.1409223124 0.1595465235 0.1606570896 0.1766365144 0.2636777866

$count
 [1]    312  14491  16401  65124 129797 323321 366051 368599 405261 604962

$n
 [1] 1 1 1 1 1 1 1 1 1 1

$n0
[1] 0

> 
> 
> 
> proc.time()
   user  system elapsed 
   2.17    0.14    2.37 

Example timings

edgeR.Rcheck/examples_i386/edgeR-Ex.timings

nameusersystemelapsed
DGEList0.010.000.01
SE2DGEList000
WLEB0.020.000.02
addPriorCount000
adjustedProfileLik0.010.000.01
aveLogCPM000
binomTest000
calcNormFactors000
camera.DGEList0.080.000.08
catchSalmon000
cbind000
commonCondLogLikDerDelta000
condLogLikDerSize000
cpm000
cutWithMinN000
decidetestsDGE0.010.000.01
dglmStdResid000
diffSpliceDGE0.050.000.05
dim000
dispBinTrend0.230.000.23
dispCoxReid0.020.000.02
dispCoxReidInterpolateTagwise0.010.000.02
dispCoxReidSplineTrend0.380.000.37
dropEmptyLevels000
edgeRUsersGuide000
effectiveLibSizes000
equalizeLibSizes0.010.000.02
estimateCommonDisp0.020.000.01
estimateDisp0.130.000.13
estimateExonGenewisedisp000
estimateGLMCommonDisp0.040.000.04
estimateGLMRobustDisp0.30.00.3
estimateGLMTagwiseDisp0.080.000.08
estimateGLMTrendedDisp0.060.000.06
estimateTagwiseDisp0.020.000.02
estimateTrendedDisp0.150.000.15
exactTest0.020.000.02
expandAsMatrix000
filterByExpr000
getCounts000
getPriorN000
gini000
glmQLFTest0.230.000.23
glmTreat0.020.000.02
glmfit0.010.000.01
goana000
gof000
goodTuring0.020.000.02
head000
loessByCol000
maPlot0.050.000.05
makeCompressedMatrix000
maximizeInterpolant000
maximizeQuadratic000
meanvar0.070.000.07
mglm000
modelMatrixMeth000
movingAverageByCol000
nbinomDeviance000
nbinomUnitDeviance000
nearestReftoX000
nearestTSS2.890.052.94
plotBCV0.250.000.25
plotExonUsage0.020.000.02
plotMDS.DGEList0.020.000.01
plotQLDisp0.500.010.52
plotSmear0.350.020.37
predFC0.050.000.05
q2qnbinom0.000.020.02
read10X000
readDGE000
roast.DGEList0.080.000.07
romer.DGEList2.060.002.07
rowsum0.020.000.01
scaleOffset000
spliceVariants000
splitIntoGroups000
subsetting0.010.000.02
sumTechReps000
systematicSubset000
thinCounts000
topTags0.020.000.01
validDGEList000
weightedCondLogLikDerDelta000
zscoreNBinom000

edgeR.Rcheck/examples_x64/edgeR-Ex.timings

nameusersystemelapsed
DGEList000
SE2DGEList000
WLEB0.020.000.02
addPriorCount000
adjustedProfileLik0.010.000.01
aveLogCPM000
binomTest000
calcNormFactors000
camera.DGEList0.070.010.08
catchSalmon000
cbind000
commonCondLogLikDerDelta000
condLogLikDerSize000
cpm000
cutWithMinN000
decidetestsDGE0.020.000.01
dglmStdResid000
diffSpliceDGE0.040.000.05
dim000
dispBinTrend0.250.000.25
dispCoxReid0.020.000.01
dispCoxReidInterpolateTagwise0.020.000.02
dispCoxReidSplineTrend0.390.000.39
dropEmptyLevels000
edgeRUsersGuide000
effectiveLibSizes0.010.000.02
equalizeLibSizes0.020.000.01
estimateCommonDisp0.010.000.02
estimateDisp0.190.000.18
estimateExonGenewisedisp0.020.000.02
estimateGLMCommonDisp0.040.000.05
estimateGLMRobustDisp0.380.000.37
estimateGLMTagwiseDisp0.080.000.08
estimateGLMTrendedDisp0.060.000.06
estimateTagwiseDisp0.010.000.02
estimateTrendedDisp0.160.000.15
exactTest0.020.000.02
expandAsMatrix000
filterByExpr000
getCounts000
getPriorN000
gini000
glmQLFTest0.180.030.22
glmTreat0.020.000.01
glmfit0.020.000.02
goana000
gof000
goodTuring0.010.000.02
head000
loessByCol000
maPlot0.050.000.04
makeCompressedMatrix000
maximizeInterpolant000
maximizeQuadratic000
meanvar0.060.000.06
mglm000
modelMatrixMeth0.020.000.02
movingAverageByCol000
nbinomDeviance000
nbinomUnitDeviance000
nearestReftoX000
nearestTSS3.390.103.48
plotBCV0.230.000.24
plotExonUsage0.030.000.03
plotMDS.DGEList000
plotQLDisp0.270.000.26
plotSmear0.580.020.59
predFC0.050.000.05
q2qnbinom000
read10X000
readDGE000
roast.DGEList0.080.000.08
romer.DGEList2.110.002.11
rowsum000
scaleOffset000
spliceVariants000
splitIntoGroups0.000.020.01
subsetting000
sumTechReps000
systematicSubset000
thinCounts000
topTags0.010.000.02
validDGEList000
weightedCondLogLikDerDelta000
zscoreNBinom000