David Gomez-Cabrero
| Snapshot Date: 2018-10-15 16:45:08 -0400 (Mon, 15 Oct 2018) |
| URL: https://git.bioconductor.org/packages/STATegRa |
| Branch: RELEASE_3_7 |
| Last Commit: 2618af2 |
| Last Changed Date: 2018-05-23 09:19:35 -0400 (Wed, 23 May 2018) |
| Back to Multiple platform build/check report for BioC 3.7 |
|
This page was generated on 2018-10-17 08:39:30 -0400 (Wed, 17 Oct 2018).
| Package 1438/1561 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||
| STATegRa 1.16.1 David Gomez-Cabrero
| malbec2 | Linux (Ubuntu 16.04.1 LTS) / x86_64 | OK | OK | OK | | ||||||
| tokay2 | Windows Server 2012 R2 Standard / x64 | OK | OK | [ OK ] | OK | | ||||||
| merida2 | OS X 10.11.6 El Capitan / x86_64 | OK | OK | OK | OK | |
| Package: STATegRa |
| Version: 1.16.1 |
| Command: C:\Users\biocbuild\bbs-3.7-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=C:\Users\biocbuild\bbs-3.7-bioc\R\library --no-vignettes --timings STATegRa_1.16.1.tar.gz |
| StartedAt: 2018-10-17 05:03:33 -0400 (Wed, 17 Oct 2018) |
| EndedAt: 2018-10-17 05:10:33 -0400 (Wed, 17 Oct 2018) |
| EllapsedTime: 419.3 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: STATegRa.Rcheck |
| Warnings: 0 |
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###
### Running command:
###
### C:\Users\biocbuild\bbs-3.7-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=C:\Users\biocbuild\bbs-3.7-bioc\R\library --no-vignettes --timings STATegRa_1.16.1.tar.gz
###
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##############################################################################
* using log directory 'C:/Users/biocbuild/bbs-3.7-bioc/meat/STATegRa.Rcheck'
* using R version 3.5.1 Patched (2018-07-24 r75005)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'STATegRa/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'STATegRa' version '1.16.1'
* package encoding: UTF-8
* 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 'STATegRa' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking 'build' directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* loading checks for arch 'i386'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* loading checks for arch 'x64'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
modelSelection,list-numeric-character: no visible binding for global
variable 'components'
modelSelection,list-numeric-character: no visible binding for global
variable 'mylabel'
plotVAF,caClass: no visible binding for global variable 'comp'
plotVAF,caClass: no visible binding for global variable 'VAF'
plotVAF,caClass: no visible binding for global variable 'block'
selectCommonComps,list-numeric: no visible binding for global variable
'comps'
selectCommonComps,list-numeric: no visible binding for global variable
'block'
selectCommonComps,list-numeric: no visible binding for global variable
'comp'
selectCommonComps,list-numeric: no visible binding for global variable
'ratio'
Undefined global functions or variables:
VAF block comp components comps mylabel ratio
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of 'data' directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking files in 'vignettes' ... OK
* checking examples ...
** running examples for arch 'i386' ... OK
Examples with CPU or elapsed time > 5s
user system elapsed
plotRes 6.65 0.08 6.74
plotVAF 5.58 0.08 5.65
omicsCompAnalysis 5.12 0.11 5.24
** running examples for arch 'x64' ... OK
Examples with CPU or elapsed time > 5s
user system elapsed
plotRes 6.56 0.11 6.67
plotVAF 5.41 0.09 5.50
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
** running tests for arch 'i386' ...
Running 'STATEgRa_Example.omicsCLUST.R'
Running 'STATEgRa_Example.omicsPCA.R'
Running 'STATegRa_Example.omicsNPC.R'
Running 'runTests.R'
OK
** running tests for arch 'x64' ...
Running 'STATEgRa_Example.omicsCLUST.R'
Running 'STATEgRa_Example.omicsPCA.R'
Running 'STATegRa_Example.omicsNPC.R'
Running 'runTests.R'
OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in 'inst/doc' ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: 1 NOTE
See
'C:/Users/biocbuild/bbs-3.7-bioc/meat/STATegRa.Rcheck/00check.log'
for details.
STATegRa.Rcheck/00install.out
##############################################################################
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###
### Running command:
###
### C:\cygwin\bin\curl.exe -O https://malbec2.bioconductor.org/BBS/3.7/bioc/src/contrib/STATegRa_1.16.1.tar.gz && rm -rf STATegRa.buildbin-libdir && mkdir STATegRa.buildbin-libdir && C:\Users\biocbuild\bbs-3.7-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=STATegRa.buildbin-libdir STATegRa_1.16.1.tar.gz && C:\Users\biocbuild\bbs-3.7-bioc\R\bin\R.exe CMD INSTALL STATegRa_1.16.1.zip && rm STATegRa_1.16.1.tar.gz STATegRa_1.16.1.zip
###
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% 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 3177k 100 3177k 0 0 39.3M 0 --:--:-- --:--:-- --:--:-- 43.0M
install for i386
* installing *source* package 'STATegRa' ...
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
converting help for package 'STATegRa'
finding HTML links ... done
STATegRa-defunct html
STATegRa html
STATegRaUsersGuide html
STATegRa_data html
STATegRa_data_TCGA_BRCA html
bioDist html
bioDistFeature html
bioDistFeaturePlot html
bioDistW html
bioDistWPlot html
bioDistclass html
bioMap html
caClass-class html
combiningMappings html
createOmicsExpressionSet html
getInitialData html
getLoadings html
getMethodInfo html
getPreprocessing html
getScores html
getVAF html
holistOmics html
modelSelection html
finding level-2 HTML links ... done
omicsCompAnalysis html
omicsNPC html
plotRes html
plotVAF html
** building package indices
** installing vignettes
** testing if installed package can be loaded
In R CMD INSTALL
install for x64
* installing *source* package 'STATegRa' ...
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'STATegRa' as STATegRa_1.16.1.zip
* DONE (STATegRa)
In R CMD INSTALL
In R CMD INSTALL
* installing to library 'C:/Users/biocbuild/bbs-3.7-bioc/R/library'
package 'STATegRa' successfully unpacked and MD5 sums checked
In R CMD INSTALL
|
STATegRa.Rcheck/tests_i386/runTests.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 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.
> BiocGenerics:::testPackage("STATegRa")
Common components
[1] 2
Distinctive components
[[1]]
[1] 0
[[2]]
[1] 0
Common components
[1] 2
Distinctive components
[[1]]
[1] 1
[[2]]
[1] 1
Common components
[1] 2
Distinctive components
[[1]]
[1] 2
[[2]]
[1] 2
RUNIT TEST PROTOCOL -- Wed Oct 17 05:08:03 2018
***********************************************
Number of test functions: 4
Number of errors: 0
Number of failures: 0
1 Test Suite :
STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures
Number of test functions: 4
Number of errors: 0
Number of failures: 0
Warning messages:
1: In rownames(pData) == colnames(exprs) :
longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
>
> proc.time()
user system elapsed
4.10 0.34 10.37
|
STATegRa.Rcheck/tests_x64/runTests.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-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.
> BiocGenerics:::testPackage("STATegRa")
Common components
[1] 2
Distinctive components
[[1]]
[1] 0
[[2]]
[1] 0
Common components
[1] 2
Distinctive components
[[1]]
[1] 1
[[2]]
[1] 1
Common components
[1] 2
Distinctive components
[[1]]
[1] 2
[[2]]
[1] 2
RUNIT TEST PROTOCOL -- Wed Oct 17 05:10:16 2018
***********************************************
Number of test functions: 4
Number of errors: 0
Number of failures: 0
1 Test Suite :
STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures
Number of test functions: 4
Number of errors: 0
Number of failures: 0
Warning messages:
1: In rownames(pData) == colnames(exprs) :
longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
>
> proc.time()
user system elapsed
3.85 0.20 4.35
|
|
STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsCLUST.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 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.
> ###########################################
> ########### EXAMPLE OF THE OMICSCLUSTERING
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
>
> #############################################
> ## PART 1: CREATING a bioMap CLASS
> #############################################
> ####### This part creates or reads the map between features.
> ####### In the present example the map is downloaded from a resource.
> ####### then the class is created.
>
> #load("../data/STATegRa_S2.rda")
> data(STATegRa_S2)
>
> MAP.SYMBOL<-bioMap(name = "Symbol-miRNA",
+ metadata = list(type_v1="Gene",type_v2="miRNA",
+ source_database="targetscan.Hs.eg.db",
+ data_extraction="July2014"),
+ map=mapdata)
>
>
> #############################################
> ## PART 2: CREATING a bioDist CLASS
> #############################################
> ##### In the second part given a set of main features and surrogate feautres,
> ##### the profile of the main features is computed through the surrogate features.
>
> # Load Data
> data(STATegRa_S1)
> #load("../data/STATegRa.S1.Rdata")
>
> ## Create ExpressionSets
> # source("../R/STATegRa_omicsPCA_classes_and_methods.R")
> # Block1 - Expression data
> mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname"))
>
> # Create Gene-gene distance computed through miRNA data
> bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1),
+ reference = "Var1",
+ mapping = MAP.SYMBOL,
+ surrogateData = miRNA.ds, ### miRNA data
+ referenceData = mRNA.ds, ### mRNA data
+ maxitems=2,
+ selectionRule="sd",
+ expfac=NULL,
+ aggregation = "sum",
+ distance = "spearman",
+ noMappingDist = 0,
+ filtering = NULL,
+ name = "mRNAbymiRNA")
>
> require(Biobase)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, basename, cbind, colMeans, colSums, colnames,
dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
intersect, is.unsorted, lapply, lengths, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
>
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+ distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+ map.name = "id",
+ map.metadata = list(),
+ params = list())
>
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
>
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
>
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+ bioDistList = bioDistList,
+ weights=weights)
> length(bioDistWList)
[1] 4
>
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
>
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+ listDistW = bioDistWList,
+ method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
>
> #############################################
> ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
> #############################################
>
> ## IDH1
>
> IDH1.F<-bioDistFeature(Feature = "IDH1" ,
+ listDistW = bioDistWList,
+ threshold.cor=0.7)
> bioDistFeaturePlot(data=IDH1.F)
>
> ## PDGFRA
>
> #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.7)
> #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
>
> ## EGFR
> #EGFR.F<-bioDistFeature(Feature = "EGFR" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.7)
> #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
>
> ## MGMT
> #MGMT.F<-bioDistFeature(Feature = "MGMT" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.5)
> #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
>
>
>
>
>
> proc.time()
user system elapsed
32.26 1.18 33.59
|
STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsCLUST.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-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.
> ###########################################
> ########### EXAMPLE OF THE OMICSCLUSTERING
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
>
> #############################################
> ## PART 1: CREATING a bioMap CLASS
> #############################################
> ####### This part creates or reads the map between features.
> ####### In the present example the map is downloaded from a resource.
> ####### then the class is created.
>
> #load("../data/STATegRa_S2.rda")
> data(STATegRa_S2)
>
> MAP.SYMBOL<-bioMap(name = "Symbol-miRNA",
+ metadata = list(type_v1="Gene",type_v2="miRNA",
+ source_database="targetscan.Hs.eg.db",
+ data_extraction="July2014"),
+ map=mapdata)
>
>
> #############################################
> ## PART 2: CREATING a bioDist CLASS
> #############################################
> ##### In the second part given a set of main features and surrogate feautres,
> ##### the profile of the main features is computed through the surrogate features.
>
> # Load Data
> data(STATegRa_S1)
> #load("../data/STATegRa.S1.Rdata")
>
> ## Create ExpressionSets
> # source("../R/STATegRa_omicsPCA_classes_and_methods.R")
> # Block1 - Expression data
> mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname"))
>
> # Create Gene-gene distance computed through miRNA data
> bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1),
+ reference = "Var1",
+ mapping = MAP.SYMBOL,
+ surrogateData = miRNA.ds, ### miRNA data
+ referenceData = mRNA.ds, ### mRNA data
+ maxitems=2,
+ selectionRule="sd",
+ expfac=NULL,
+ aggregation = "sum",
+ distance = "spearman",
+ noMappingDist = 0,
+ filtering = NULL,
+ name = "mRNAbymiRNA")
>
> require(Biobase)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, basename, cbind, colMeans, colSums, colnames,
dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
intersect, is.unsorted, lapply, lengths, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
>
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+ distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+ map.name = "id",
+ map.metadata = list(),
+ params = list())
>
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
>
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
>
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+ bioDistList = bioDistList,
+ weights=weights)
> length(bioDistWList)
[1] 4
>
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
>
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+ listDistW = bioDistWList,
+ method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
4: In plot.window(...) :
relative range of values = 0 * EPS, is small (axis 2)
5: In plot.window(...) :
relative range of values = 0 * EPS, is small (axis 2)
6: In plot.window(...) :
relative range of values = 0 * EPS, is small (axis 2)
7: In plot.window(...) :
relative range of values = 0 * EPS, is small (axis 2)
>
> #############################################
> ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
> #############################################
>
> ## IDH1
>
> IDH1.F<-bioDistFeature(Feature = "IDH1" ,
+ listDistW = bioDistWList,
+ threshold.cor=0.7)
> bioDistFeaturePlot(data=IDH1.F)
>
> ## PDGFRA
>
> #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.7)
> #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
>
> ## EGFR
> #EGFR.F<-bioDistFeature(Feature = "EGFR" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.7)
> #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
>
> ## MGMT
> #MGMT.F<-bioDistFeature(Feature = "MGMT" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.5)
> #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
>
>
>
>
>
> proc.time()
user system elapsed
27.14 0.92 28.17
|
|
STATegRa.Rcheck/tests_i386/STATegRa_Example.omicsNPC.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 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.
> rm(list = ls())
> require("STATegRa")
Loading required package: STATegRa
> # Load the data
> data("TCGA_BRCA_Batch_93")
> # Setting dataTypes
> dataTypes <- c("count", "count", "continuous")
> # Setting methods to combine pvalues
> combMethods = c("Fisher", "Liptak", "Tippett")
> # Setting number of permutations
> numPerms = 1000
> # Setting number of cores
> numCores = 1
> # Setting holistOmics to print out the steps that it performs.
> verbose = TRUE
> # Run holistOmics analysis.
> output <- omicsNPC(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes, combMethods = combMethods, numPerms = numPerms, numCores = numCores, verbose = verbose)
Compute initial statistics on data
Building NULL distributions by permuting data
Compute pseudo p-values based on NULL distributions...
NPC p-values calculation...
>
> proc.time()
user system elapsed
64.87 0.23 65.23
|
STATegRa.Rcheck/tests_x64/STATegRa_Example.omicsNPC.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-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.
> rm(list = ls())
> require("STATegRa")
Loading required package: STATegRa
> # Load the data
> data("TCGA_BRCA_Batch_93")
> # Setting dataTypes
> dataTypes <- c("count", "count", "continuous")
> # Setting methods to combine pvalues
> combMethods = c("Fisher", "Liptak", "Tippett")
> # Setting number of permutations
> numPerms = 1000
> # Setting number of cores
> numCores = 1
> # Setting holistOmics to print out the steps that it performs.
> verbose = TRUE
> # Run holistOmics analysis.
> output <- omicsNPC(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes, combMethods = combMethods, numPerms = numPerms, numCores = numCores, verbose = verbose)
Compute initial statistics on data
Building NULL distributions by permuting data
Compute pseudo p-values based on NULL distributions...
NPC p-values calculation...
>
> proc.time()
user system elapsed
83.04 0.20 83.37
|
|
STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsPCA.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 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.
> ###########################################
> ########### EXAMPLE OF THE OMICSPCA
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
>
> # g_legend (not exported by STATegRa any more)
> ## code from https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs
> g_legend<-function(a.gplot){
+ tmp <- ggplot_gtable(ggplot_build(a.gplot))
+ leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
+ legend <- tmp$grobs[[leg]]
+ return(legend)}
>
> #########################
> ## PART 1. Load data
>
> ## Load data
> data(STATegRa_S3)
>
> ls()
[1] "Block1.PCA" "Block2.PCA" "ed.PCA" "g_legend"
>
> ## Create ExpressionSets
> # Block1 - Expression data
> B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname"))
>
> #########################
> ## PART 2. Model Selection
>
> require(grid)
Loading required package: grid
> require(gridExtra)
Loading required package: gridExtra
> require(ggplot2)
Loading required package: ggplot2
>
> ## Select the optimal components
> ms <- modelSelection(Input=list(B1,B2),Rmax=4,fac.sel="single%",varthreshold=0.03,center=TRUE,scale=TRUE,weight=TRUE)
Common components
[1] 2
Distinctive components
[[1]]
[1] 2
[[2]]
[1] 2
>
>
> #########################
> ## PART 3. Component Analysis
>
> ## 3.1 Component analysis of the three methods
> discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+ scale=TRUE,weight=TRUE)
> jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+ scale=TRUE,weight=TRUE)
> o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+ scale=TRUE,weight=TRUE)
>
> ## 3.2 Exploring scores structures
>
> # Exploring DISCO-SCA scores structure
> discoRes@scores$common ## Common scores
1 2
sample1 0.0781575859 0.0431547441
sample2 -0.1192221282 -0.0294022968
sample3 -0.0531408640 0.0746838113
sample4 0.0292971911 0.0006032619
sample5 0.0202090797 -0.0110455119
sample6 0.1226088393 -0.1053492363
sample7 0.1078931237 0.0322420258
sample8 0.1782891182 -0.1449331061
sample9 0.0468697296 0.0455171040
sample10 -0.0036032580 -0.0420078228
sample11 -0.0035566364 0.0566284634
sample12 0.1006129774 -0.0641394546
sample13 -0.1174412934 -0.0907476019
sample14 0.0981203500 -0.0617762823
sample15 0.0085337242 0.0086956651
sample16 0.0783146771 -0.1581333092
sample17 -0.1483610670 -0.0638580097
sample18 -0.0963084499 -0.0556686612
sample19 -0.0217242944 0.0720128416
sample20 -0.0635634026 0.0779610463
sample21 -0.0201843960 -0.1566381771
sample22 0.0218273786 0.0764057038
sample23 0.0852039236 0.0032763479
sample24 -0.1287181368 -0.1924429523
sample25 -0.0430575516 0.0456637803
sample26 -0.1453899677 -0.0541460477
sample27 -0.0197483736 0.1185594206
sample28 -0.1025339394 -0.0650656375
sample29 0.0706022442 0.0682932864
sample30 -0.1295623194 0.0066680109
sample31 0.1147449254 -0.1232726227
sample32 -0.0374308317 -0.0380247897
sample33 0.0599520671 -0.0136934097
sample34 -0.0984199353 -0.0375363841
sample35 -0.0543096663 0.0378036284
sample36 0.1403627647 0.0343641379
sample37 0.0228947184 0.0732693160
sample38 -0.0222073021 0.0962567283
sample39 -0.0941739144 -0.0215181006
sample40 0.0643806623 0.0687723274
sample41 -0.0327634958 0.1232187332
sample42 -0.0500431548 0.0292513651
sample43 -0.0184497195 -0.0233043282
sample44 0.1487889538 -0.1171212967
sample45 -0.1050778667 -0.1123141458
sample46 -0.1151191672 0.1093996443
sample47 -0.0962591623 0.0288418519
sample48 0.0004832819 0.0310376983
sample49 0.1135203869 -0.1213936494
sample50 -0.0123549776 0.1740761690
sample51 0.0550527361 -0.1258930248
sample52 0.0499118388 -0.0728580723
sample53 0.1119772601 -0.1588063479
sample54 -0.0360055740 -0.0228584988
sample55 0.0210418823 -0.0006750464
sample56 -0.0434171520 -0.0633131239
sample57 0.0197820571 -0.1150753902
sample58 0.0030440642 -0.0326126770
sample59 0.0500256505 -0.0129519457
sample60 0.0184279739 -0.0136217009
sample61 0.0150298780 -0.0635096164
sample62 -0.0304758899 0.0201237898
sample63 0.1102250221 -0.1285969340
sample64 0.1552586852 -0.0971185715
sample65 -0.0058503724 -0.0207103166
sample66 -0.0025607397 -0.0424284537
sample67 0.1546638237 0.0661580520
sample68 0.0536374067 0.0923605864
sample69 0.0640333030 -0.0082003314
sample70 0.0163521818 0.0663227381
sample71 -0.0102536028 0.1345964371
sample72 -0.0654191916 0.0196038266
sample73 -0.1048553298 -0.0220999014
sample74 0.0123800499 -0.0586155877
sample75 0.0392079743 0.0209726645
sample76 0.0648954452 0.0524760445
sample77 0.1172922572 0.0201201329
sample78 -0.1463072570 -0.0708400800
sample79 0.0265209014 0.1603423300
sample80 0.0279739218 0.0214153496
sample81 0.0079212254 0.0738495054
sample82 -0.1544234711 0.0361451177
sample83 -0.0494205596 0.0049941503
sample84 -0.0259039626 0.0346591140
sample85 0.1116487160 0.0031406399
sample86 -0.1306479206 0.0377157392
sample87 -0.0554777960 0.0459740287
sample88 -0.0301626686 -0.0382206190
sample89 -0.1016866269 -0.0694077215
sample90 0.0086821645 0.0201324536
sample91 0.1578629804 0.2097790355
sample92 0.0170933667 0.1655933807
sample93 -0.0979805071 0.0121500471
sample94 0.0131486304 0.0114929401
sample95 0.0315682593 0.0758916112
sample96 0.0024125984 0.0470184572
sample97 0.0634545962 -0.0270303971
sample98 -0.0359372531 0.0135466736
sample99 -0.1009167497 -0.1124713745
sample100 0.0551754078 -0.0246501860
sample101 -0.0080115836 0.1627405806
sample102 -0.0046450892 -0.0095475648
sample103 -0.0472520872 0.0940383538
sample104 0.0198157540 0.0591146107
sample105 -0.0400238952 0.0160949320
sample106 -0.0923810154 -0.0369004082
sample107 -0.1019372323 -0.0224966975
sample108 -0.0877091468 0.0128849427
sample109 0.0864820558 0.0901077674
sample110 -0.1223116475 0.0096108149
sample111 0.0257352710 0.0936278885
sample112 -0.0765285986 -0.0270378650
sample113 0.0258800002 -0.0377439649
sample114 0.0021141190 0.0882040012
sample115 0.0303455582 0.0723731807
sample116 0.0780504522 0.0685160221
sample117 0.0536894190 0.0912023687
sample118 0.0666649893 0.0236260253
sample119 0.1021872737 0.2325000544
sample120 0.0750216443 -0.0243346104
sample121 -0.0756937876 -0.0942970950
sample122 -0.0259631914 -0.0731922151
sample123 -0.1037844761 0.0369179564
sample124 0.0611205362 -0.0421648563
sample125 -0.0738472628 -0.0066943473
sample126 0.0972919153 -0.0762698283
sample127 0.0824699525 0.0096644553
sample128 -0.1249411372 -0.0929255586
sample129 -0.0734063804 0.0434315202
sample130 -0.0003500233 0.0309857747
sample131 0.0930183994 -0.0155969600
sample132 0.0736220786 -0.0732973066
sample133 -0.0498398341 0.0462455842
sample134 0.1644872528 -0.0720046712
sample135 -0.0752295121 -0.0003868828
sample136 0.0227149973 0.0495470315
sample137 0.0564721627 0.0288862140
sample138 0.0255986613 0.0610929345
sample139 0.0621218564 -0.0235856008
sample140 -0.0604148994 0.0435533122
sample141 0.0246743113 -0.0532630082
sample142 -0.0409563819 -0.0316234962
sample143 -0.0077356435 0.0476908699
sample144 0.0173241063 0.0156785752
sample145 0.0485467692 -0.1202739109
sample146 0.0419649934 0.0811241795
sample147 -0.0977304850 0.0274773451
sample148 0.0368253280 -0.0803969323
sample149 -0.0072864904 0.1533016654
sample150 0.1020825393 -0.0624821649
sample151 0.0305397280 0.0289336024
sample152 -0.0533595143 0.0638333475
sample153 -0.0891639283 -0.1799457385
sample154 -0.0727554475 0.0834130075
sample155 -0.0880665882 0.0220771524
sample156 -0.0276558782 0.0326601907
sample157 -0.1155031585 -0.0183635425
sample158 -0.0281506721 0.0104912342
sample159 0.0663233779 -0.0443809705
sample160 -0.0302644049 -0.0404300541
sample161 0.0114712983 0.0591082098
sample162 -0.1337091005 -0.1398132480
sample163 0.1330120805 -0.1688770115
sample164 -0.0150338064 -0.0028375830
sample165 0.0076518870 0.0164145636
sample166 0.0367791570 -0.0630614942
sample167 0.1111989841 -0.0030066329
sample168 -0.0672983002 -0.0446266477
sample169 -0.0413003743 -0.0224446073
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 -0.0420465091 0.0867866071
sample2 -0.0820848608 -0.0410969842
sample3 0.0155963687 -0.0195185496
sample4 -0.1001342222 -0.0410777488
sample5 -0.0153478865 -0.0253258004
sample6 0.0340243101 -0.0408223811
sample7 0.0722601517 0.0002324895
sample8 -0.0457614168 -0.0370008579
sample9 -0.0086218681 0.0820184597
sample10 -0.0423629682 -0.0083918397
sample11 0.0022590967 0.0787764475
sample12 0.0322076495 0.1479823306
sample13 -0.0293967002 -0.0306743947
sample14 0.0337433382 -0.0367508470
sample15 0.0815558485 0.1275614814
sample16 0.0508336669 0.0540603683
sample17 0.0062556848 0.0041024585
sample18 0.0705602713 -0.0351052887
sample19 -0.0476785605 -0.0509595332
sample20 0.0523023826 0.0715515181
sample21 -0.0119245506 -0.0376088350
sample22 0.0724455467 -0.0095633488
sample23 -0.0992529626 0.0134297880
sample24 -0.1595260231 0.0728681011
sample25 -0.0920661858 -0.0749749862
sample26 -0.0595566636 0.0848972716
sample27 0.0826573297 -0.0086745779
sample28 -0.0384832200 0.0440971856
sample29 0.0777736816 0.1735299814
sample30 0.1229474741 -0.0819017001
sample31 0.0579754832 -0.0238647136
sample32 0.0970367036 -0.0111434319
sample33 0.1017581071 -0.0630451498
sample34 0.0637903232 0.0377936705
sample35 0.0790002512 -0.0229731449
sample36 0.1224934034 -0.1274967047
sample37 0.1798847283 -0.1673445733
sample38 0.0466389537 0.0888154447
sample39 -0.0168694716 0.0421535787
sample40 0.1756417773 -0.1526660133
sample41 0.0042465240 0.0004925555
sample42 -0.0447825994 -0.0651501545
sample43 0.0482293007 -0.0253533128
sample44 -0.1986814717 -0.0545756889
sample45 -0.0741914728 0.0054712676
sample46 0.0478858260 -0.0007078991
sample47 0.0608214967 0.0481616328
sample48 -0.1381466230 0.0578299705
sample49 -0.0530625235 -0.1405525095
sample50 -0.0173654060 0.1602387232
sample51 0.0462460795 0.0303472490
sample52 0.0279998635 0.0280387451
sample53 0.0667503782 0.0237699653
sample54 0.0121813010 -0.0521354915
sample55 0.0182392250 0.0221326763
sample56 -0.0001306736 0.0030908791
sample57 0.0316578474 0.0530189952
sample58 0.0393892390 -0.0297801594
sample59 0.1278272584 -0.0546539384
sample60 0.1486964384 0.1069143084
sample61 0.0793069358 0.0569790658
sample62 0.1172821131 -0.0149209698
sample63 -0.0028809849 0.1300523147
sample64 0.0237298467 0.1073287854
sample65 -0.0126543603 0.0589810095
sample66 -0.0468231575 -0.0771067388
sample67 0.1494285357 -0.0769875617
sample68 0.0978021257 -0.0577362166
sample69 0.0403090413 0.0156038699
sample70 0.0221595097 0.0315437473
sample71 -0.0546334259 -0.0272394531
sample72 0.1107500858 -0.0537330005
sample73 0.0906756487 0.0579958785
sample74 0.0586515256 0.0121417688
sample75 0.0390511682 0.0349278772
sample76 -0.0022939317 -0.1676559787
sample77 -0.0232099727 -0.2067301069
sample78 -0.0929807359 -0.0434929379
sample79 -0.1619385548 -0.0378103106
sample80 0.0680390670 0.1424656753
sample81 -0.0530727703 -0.0358347649
sample82 0.0266849461 -0.0577448457
sample83 0.1517242146 -0.0448568357
sample84 -0.0570943971 -0.0273808805
sample85 0.1086273102 -0.1228129373
sample86 0.0833890506 -0.0442923553
sample87 0.0022040378 -0.0943908180
sample88 -0.0078274011 -0.1140505019
sample89 0.0611008117 -0.0094589184
sample90 0.0022941806 -0.0936254615
sample91 0.0433763074 0.3205973929
sample92 -0.1815222660 -0.0334667514
sample93 0.0267652640 0.0614426280
sample94 0.0181900186 0.0605088538
sample95 -0.0720316851 -0.0013040764
sample96 -0.0559674258 -0.0118787315
sample97 -0.0217420124 0.0195416876
sample98 0.0379197993 0.0588353328
sample99 -0.0792504718 -0.0151263926
sample100 0.0222101084 -0.0023322854
sample101 -0.0387091157 0.1224226026
sample102 -0.2094625106 -0.0516423153
sample103 0.0138554485 0.0301048478
sample104 -0.0807949581 -0.0162712889
sample105 -0.0520491018 -0.1229660761
sample106 -0.0192641698 -0.0185235613
sample107 0.0319014270 0.0405120896
sample108 -0.0140675019 0.0163422357
sample109 -0.1831860434 0.0613022320
sample110 -0.0292782935 -0.0199846674
sample111 -0.1423177179 0.0327351267
sample112 0.0426314064 -0.0029086761
sample113 -0.0771931098 0.0268741630
sample114 -0.0241570741 -0.0184080189
sample115 -0.1958958901 0.0460146997
sample116 -0.1394438774 -0.0530794623
sample117 -0.1672312870 -0.1386523169
sample118 -0.0448332105 -0.0117618367
sample119 -0.0910202360 0.2217436331
sample120 -0.0331404242 -0.0057270800
sample121 0.0307517708 0.1392505863
sample122 -0.0839835376 -0.0291984995
sample123 0.0239674781 -0.0642166825
sample124 -0.0909175393 0.0130428902
sample125 -0.0065361156 -0.1092631060
sample126 0.0935273894 0.1368277325
sample127 0.0035405030 0.0292755184
sample128 -0.0660349083 0.1018574512
sample129 0.0693670490 -0.0695429120
sample130 0.0008516867 -0.0669705078
sample131 0.0431012284 0.0174061308
sample132 -0.0637087175 0.0029382372
sample133 -0.0289465390 -0.0390817277
sample134 0.0446143752 0.0456332098
sample135 0.0712343133 0.0521628388
sample136 0.0596316893 0.0197292761
sample137 0.0793175118 -0.0380636195
sample138 -0.0973506651 -0.0454210717
sample139 0.0539868382 -0.1534331781
sample140 0.0850869958 0.0955805687
sample141 -0.0192721954 -0.0554447006
sample142 -0.0672293074 -0.0461314045
sample143 -0.0303707630 -0.0519258612
sample144 -0.0089351046 0.0145815394
sample145 -0.0638873676 0.0122266992
sample146 0.0585920481 0.0063076129
sample147 0.0894147084 -0.1124624590
sample148 -0.0216436822 -0.0615963272
sample149 -0.0515319345 -0.0839902359
sample150 0.0568230619 -0.0124472674
sample151 -0.0789514028 -0.0261824585
sample152 -0.0330696319 0.1306445089
sample153 -0.1752062417 0.1497752230
sample154 0.0421487620 -0.0037016037
sample155 0.0680198071 0.0095704447
sample156 0.0388948329 0.1057558647
sample157 0.0314764756 0.0561364915
sample158 0.0329629697 0.0353944030
sample159 -0.0398459437 -0.1007369050
sample160 0.0424906772 0.0108493187
sample161 -0.0888340862 -0.0679693426
sample162 -0.0027568904 0.1237847270
sample163 -0.0126225923 0.0725439483
sample164 -0.0566786591 -0.0458318909
sample165 -0.0315331531 -0.0236359852
sample166 -0.0612107307 -0.0425225919
sample167 0.0142729555 0.0179307101
sample168 -0.0169541262 -0.0769615328
sample169 0.0675063782 0.0131499617
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 -0.0012331503 1.635716e-01
sample2 -0.0724353105 6.022147e-03
sample3 -0.0188459932 1.080029e-01
sample4 0.0390143194 -3.106620e-04
sample5 0.1774810677 2.996427e-02
sample6 -0.0451446346 3.455899e-02
sample7 -0.0226463581 7.019207e-03
sample8 -0.1033684362 9.857957e-03
sample9 0.1350014092 -8.979117e-02
sample10 0.1259884553 5.097936e-02
sample11 0.0979790799 -7.086569e-02
sample12 -0.0863020823 8.620321e-02
sample13 -0.1381401884 -1.827998e-01
sample14 -0.0615074680 2.642809e-02
sample15 0.0381600571 3.101600e-02
sample16 -0.0048779242 -1.271018e-03
sample17 -0.0788483125 1.547608e-02
sample18 -0.0884189446 3.795480e-02
sample19 0.0703043572 1.084003e-01
sample20 -0.0025581547 -7.975970e-02
sample21 0.0941596867 4.126894e-02
sample22 -0.0550270933 7.806617e-02
sample23 0.0679492929 4.102075e-02
sample24 -0.1310969233 -1.649282e-01
sample25 0.0113583616 4.426900e-02
sample26 -0.1402948789 -2.016457e-02
sample27 0.0261565841 -1.589964e-03
sample28 -0.0724200720 -5.850509e-02
sample29 -0.0330054881 -2.062096e-03
sample30 -0.0228750421 2.015347e-02
sample31 -0.0635070255 6.670370e-02
sample32 0.0685100025 4.955247e-02
sample33 -0.0777764879 1.272070e-01
sample34 0.0157842128 3.024312e-02
sample35 -0.0529628191 -1.500981e-01
sample36 0.0070907599 -2.025321e-01
sample37 -0.0442412250 -1.802109e-01
sample38 -0.0781508467 3.676298e-02
sample39 0.0120330158 3.388884e-02
sample40 -0.0473284243 -1.471581e-01
sample41 0.0228192073 2.673457e-02
sample42 -0.0245361788 7.960878e-02
sample43 0.1036362084 8.229577e-02
sample44 -0.1012234586 -7.049241e-02
sample45 0.0013726919 2.451070e-02
sample46 -0.0558506610 -2.948572e-03
sample47 -0.0380478824 -4.554236e-02
sample48 0.0784340496 -4.888894e-02
sample49 -0.0605167916 1.162473e-02
sample50 0.0530082788 2.737810e-02
sample51 0.1514645408 -5.678261e-02
sample52 0.1860935949 -1.246711e-01
sample53 -0.0064179548 2.701060e-02
sample54 0.0697037596 2.308413e-02
sample55 0.1633577681 -1.366435e-02
sample56 0.1011484028 -4.682134e-02
sample57 0.1730374361 -1.609594e-01
sample58 -0.0071384871 1.666952e-02
sample59 -0.0030458605 -3.005374e-02
sample60 0.0215841841 -2.665887e-01
sample61 0.1510585256 -1.002384e-01
sample62 -0.0925531650 4.845730e-02
sample63 -0.0596315225 4.137108e-02
sample64 -0.0449227175 2.600951e-03
sample65 0.0939382341 4.406949e-02
sample66 0.1063397865 5.710077e-02
sample67 -0.0201581279 -2.361746e-01
sample68 0.0037208163 -2.418542e-02
sample69 -0.0645161902 1.155618e-01
sample70 -0.1013439699 1.351780e-01
sample71 -0.0016466194 2.976772e-02
sample72 0.0328895321 2.835773e-02
sample73 0.0275080419 5.148153e-02
sample74 0.1341718474 7.895302e-02
sample75 0.0951576634 3.943146e-02
sample76 -0.0864720079 -3.035052e-02
sample77 -0.1035749538 2.545327e-02
sample78 -0.1575647761 -4.939472e-02
sample79 0.0189138253 -4.874692e-02
sample80 0.1384142720 -4.317661e-05
sample81 -0.0118846653 6.357908e-02
sample82 -0.1675306736 -3.533965e-02
sample83 -0.0065671203 7.812499e-02
sample84 0.1486890692 3.109094e-02
sample85 -0.0532720545 -7.417986e-02
sample86 -0.1138475023 1.823341e-05
sample87 0.0432865816 -6.080499e-02
sample88 0.0433451082 -1.402486e-01
sample89 0.0331204858 1.395430e-02
sample90 -0.0607413434 8.610387e-02
sample91 -0.0566264113 -1.303770e-01
sample92 -0.0359580869 -1.061605e-01
sample93 -0.0433646424 4.443611e-02
sample94 -0.0477292036 1.059571e-01
sample95 -0.0249595919 3.980509e-02
sample96 0.0035217662 9.293930e-02
sample97 -0.0066051797 1.527234e-01
sample98 0.0020367092 5.579515e-02
sample99 -0.0886621488 3.728376e-02
sample100 -0.1091259560 3.560402e-02
sample101 -0.0739723925 4.317884e-02
sample102 0.0574455906 2.784084e-02
sample103 0.0142733628 -9.706357e-03
sample104 0.0710395529 -4.068333e-02
sample105 0.0980829978 3.452996e-02
sample106 -0.0254260467 -3.628932e-02
sample107 -0.0160654925 9.173399e-02
sample108 -0.0200988279 2.379699e-02
sample109 -0.0389781916 -1.692314e-02
sample110 -0.0326305262 -2.988086e-02
sample111 0.0676936006 6.038246e-02
sample112 0.0167883511 -5.336915e-03
sample113 0.0969214094 2.757701e-02
sample114 -0.0026397970 9.209101e-02
sample115 -0.0308049505 -1.603747e-02
sample116 -0.1240306515 -1.272998e-01
sample117 0.0334728586 -5.392664e-02
sample118 -0.1037152224 -6.252439e-02
sample119 -0.1064170880 -1.196218e-01
sample120 -0.0771357556 1.004935e-01
sample121 -0.0129352223 -3.181914e-02
sample122 0.0847487769 5.568463e-02
sample123 -0.0041335593 -7.693538e-03
sample124 -0.0583461995 8.396475e-02
sample125 0.0634843304 5.232568e-02
sample126 -0.0662581948 1.091730e-01
sample127 -0.0865025518 1.094172e-01
sample128 -0.0627821816 1.471094e-02
sample129 -0.0336274663 4.007777e-02
sample130 -0.0293518082 8.046087e-02
sample131 -0.0469196812 2.209386e-03
sample132 -0.0241745369 1.248608e-01
sample133 0.0907303748 -1.466698e-02
sample134 -0.0350841265 -7.539660e-02
sample135 0.0001334833 -9.185811e-03
sample136 -0.0335874809 9.860182e-02
sample137 -0.0640147304 7.554373e-02
sample138 0.0060964059 1.742782e-02
sample139 -0.0592082874 -5.615004e-02
sample140 0.0427988520 1.099465e-02
sample141 0.0618793436 9.301101e-02
sample142 0.0898552559 -3.573325e-02
sample143 0.0817390928 -8.880529e-02
sample144 0.0787754504 3.821394e-02
sample145 0.1085819542 -1.569461e-01
sample146 -0.0589555106 4.373237e-02
sample147 -0.0495328040 -7.278028e-03
sample148 0.1161590576 -9.078145e-03
sample149 -0.0121575747 -7.788462e-02
sample150 -0.0314511997 -3.520219e-02
sample151 0.0575380987 1.945391e-02
sample152 -0.0494540450 -7.025567e-02
sample153 -0.0941338360 -2.153270e-01
sample154 -0.0335928982 -2.078824e-02
sample155 0.0690459002 2.780362e-02
sample156 0.1039902323 6.292487e-02
sample157 -0.0408645830 -8.065518e-03
sample158 0.1018106304 -7.817031e-03
sample159 -0.0281732456 1.207260e-02
sample160 0.1643052880 -2.977822e-03
sample161 0.0374329978 -8.524590e-02
sample162 -0.0804538142 -8.349634e-02
sample163 -0.0743232161 1.406346e-02
sample164 0.1208804345 2.139523e-02
sample165 0.1608115933 -2.025161e-02
sample166 -0.0425947789 2.660801e-02
sample167 -0.0226849475 4.464257e-02
sample168 -0.0180737307 7.471686e-04
sample169 0.0190780159 -2.645426e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
[,1] [,2]
sample1 -0.0572060227 -1.729087e-02
sample2 0.0875245208 1.112588e-02
sample3 0.0403482602 -3.168994e-02
sample4 -0.0218345996 4.052760e-06
sample5 -0.0150905011 4.795041e-03
sample6 -0.0924362933 4.511003e-02
sample7 -0.0793066751 -1.243823e-02
sample8 -0.1342997187 6.215220e-02
sample9 -0.0338886944 -1.854401e-02
sample10 0.0020547173 1.749421e-02
sample11 0.0037275602 -2.364116e-02
sample12 -0.0753094533 2.772698e-02
sample13 0.0856160091 3.679963e-02
sample14 -0.0737457307 2.668452e-02
sample15 -0.0062111746 -3.554864e-03
sample16 -0.0602355268 6.675115e-02
sample17 0.1086768843 2.524534e-02
sample18 0.0702999472 2.231671e-02
sample19 0.0173785882 -3.024846e-02
sample20 0.0484173812 -3.310904e-02
sample21 0.0124657042 6.517144e-02
sample22 -0.0140989936 -3.159137e-02
sample23 -0.0627028403 -5.393710e-04
sample24 0.0919972100 7.909297e-02
sample25 0.0326998483 -1.945206e-02
sample26 0.1064741246 2.120849e-02
sample27 0.0166058995 -4.964993e-02
sample28 0.0743504770 2.614211e-02
sample29 -0.0511008491 -2.782647e-02
sample30 0.0962250842 -3.974893e-03
sample31 -0.0869563008 5.250819e-02
sample32 0.0271858919 1.552005e-02
sample33 -0.0448364581 6.243160e-03
sample34 0.0718415218 1.469396e-02
sample35 0.0403086451 -1.632629e-02
sample36 -0.1036402827 -1.304320e-02
sample37 -0.0159385744 -3.036525e-02
sample38 0.0182198369 -4.034805e-02
sample39 0.0690363619 8.058350e-03
sample40 -0.0467312750 -2.810325e-02
sample41 0.0263674438 -5.171216e-02
sample42 0.0374578960 -1.268634e-02
sample43 0.0132336869 9.536642e-03
sample44 -0.1119154428 5.028683e-02
sample45 0.0759639367 4.587903e-02
sample46 0.0871885519 -4.670385e-02
sample47 0.0721490571 -1.288540e-02
sample48 0.0005086144 -1.290565e-02
sample49 -0.0858177028 5.173760e-02
sample50 0.0118992665 -7.276215e-02
sample51 -0.0426446855 5.306205e-02
sample52 -0.0381605826 3.086785e-02
sample53 -0.0855757630 6.730043e-02
sample54 0.0261723092 9.184260e-03
sample55 -0.0156418304 4.682404e-04
sample56 0.0307831193 2.597550e-02
sample57 -0.0157242103 4.829381e-02
sample58 -0.0031174404 1.359898e-02
sample59 -0.0373001859 5.868397e-03
sample60 -0.0142609099 5.831654e-03
sample61 -0.0122255144 2.663579e-02
sample62 0.0228002942 -8.692265e-03
sample63 -0.0833127581 5.473229e-02
sample64 -0.1166548159 4.196500e-02
sample65 0.0038808902 8.568590e-03
sample66 0.0011561811 1.766612e-02
sample67 -0.1129311062 -2.608702e-02
sample68 -0.0382526429 -3.804045e-02
sample69 -0.0476502440 4.003241e-03
sample70 -0.0110329882 -2.752719e-02
sample71 0.0096850282 -5.627056e-02
sample72 0.0487124704 -8.800131e-03
sample73 0.0773058132 8.239864e-03
sample74 -0.0102488176 2.454957e-02
sample75 -0.0286613976 -8.387293e-03
sample76 -0.0472655595 -2.129315e-02
sample77 -0.0865043074 -7.296820e-03
sample78 0.1070293698 2.818346e-02
sample79 -0.0165060681 -6.659721e-02
sample80 -0.0206765949 -8.712112e-03
sample81 -0.0050943615 -3.079175e-02
sample82 0.1153622361 -1.647054e-02
sample83 0.0367979217 -2.538114e-03
sample84 0.0199463070 -1.468961e-02
sample85 -0.0827122185 -2.709824e-04
sample86 0.0969487314 -1.699897e-02
sample87 0.0421957457 -1.965953e-02
sample88 0.0215934743 1.566050e-02
sample89 0.0751559502 2.811652e-02
sample90 -0.0057328000 -8.283795e-03
sample91 -0.1134005268 -8.603522e-02
sample92 -0.0101689918 -6.894992e-02
sample93 0.0725967502 -6.003176e-03
sample94 -0.0096878852 -4.693081e-03
sample95 -0.0223502239 -3.139636e-02
sample96 -0.0013232863 -1.963604e-02
sample97 -0.0476541710 1.183660e-02
sample98 0.0269546160 -5.978398e-03
sample99 0.0728179461 4.597884e-02
sample100 -0.0413398038 1.079347e-02
sample101 0.0087536994 -6.796076e-02
sample102 0.0032509529 3.932612e-03
sample103 0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105 0.0294940465 -7.140722e-03
sample106 0.0686472054 1.462895e-02
sample107 0.0748635927 8.401339e-03
sample108 0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110 0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112 0.0570870472 1.066018e-02
sample113 -0.0200110554 1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586 1.089843e-02
sample121 0.0542443861 3.861344e-02
sample122 0.0178575357 3.027138e-02
sample123 0.0775020581 -1.636852e-02
sample124 -0.0460701050 1.814758e-02
sample125 0.0543846585 2.075898e-03
sample126 -0.0729417144 3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128 0.0908136899 3.758801e-02
sample129 0.0552445878 -1.879062e-02
sample130 0.0007128089 -1.294308e-02
sample131 -0.0693311345 7.357082e-03
sample132 -0.0556565156 3.126995e-02
sample133 0.0375870104 -1.977240e-02
sample134 -0.1229130924 3.159495e-02
sample135 0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201 1.040683e-02
sample140 0.0452288969 -1.876279e-02
sample141 -0.0189142561 2.247042e-02
sample142 0.0297545566 1.280524e-02
sample143 0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491 5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147 0.0726083535 -1.239968e-02
sample148 -0.0284795794 3.389732e-02
sample149 0.0082261455 -6.399305e-02
sample150 -0.0765013197 2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152 0.0403422737 -2.714879e-02
sample153 0.0629117719 7.425085e-02
sample154 0.0551622927 -3.548984e-02
sample155 0.0654439133 -1.005306e-02
sample156 0.0209310714 -1.390213e-02
sample157 0.0851522597 6.577150e-03
sample158 0.0208354599 -4.663078e-03
sample159 -0.0498794349 1.913257e-02
sample160 0.0216074437 1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162 0.0963663017 5.705881e-02
sample163 -0.1009542191 7.174224e-02
sample164 0.0109881996 1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357 2.673084e-02
sample167 -0.0825048036 2.278863e-03
sample168 0.0486147429 1.793843e-02
sample169 0.0302506727 8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
[,1] [,2]
sample1 -0.0621842115 -1.364509e-02
sample2 0.0944623785 9.720892e-03
sample3 0.0406196267 -2.236338e-02
sample4 -0.0229316496 -3.932487e-04
sample5 -0.0157330047 3.231033e-03
sample6 -0.0945794025 3.120720e-02
sample7 -0.0854427118 -1.052880e-02
sample8 -0.1376625920 4.286608e-02
sample9 -0.0377115311 -1.415134e-02
sample10 0.0035244506 1.280825e-02
sample11 0.0016639987 -1.717895e-02
sample12 -0.0781403168 1.884368e-02
sample13 0.0938400516 2.838858e-02
sample14 -0.0759839772 1.810989e-02
sample15 -0.0068340837 -2.705361e-03
sample16 -0.0590150849 4.757848e-02
sample17 0.1178805097 2.040526e-02
sample18 0.0767858320 1.756604e-02
sample19 0.0157112113 -2.172867e-02
sample20 0.0485318300 -2.327033e-02
sample21 0.0185928176 4.777095e-02
sample22 -0.0191358702 -2.329775e-02
sample23 -0.0672994194 -1.535656e-03
sample24 0.1047476642 5.935707e-02
sample25 0.0329844953 -1.358036e-02
sample26 0.1154952052 1.741529e-02
sample27 0.0133849853 -3.590922e-02
sample28 0.0821554039 2.042376e-02
sample29 -0.0567643690 -2.123848e-02
sample30 0.1016073931 -1.134728e-03
sample31 -0.0880396372 3.670548e-02
sample32 0.0300363338 1.182406e-02
sample33 -0.0467252272 3.739254e-03
sample34 0.0783666394 1.203777e-02
sample35 0.0424227097 -1.118559e-02
sample36 -0.1107646166 -1.143464e-02
sample37 -0.0191667664 -2.246060e-02
sample38 0.0155968095 -2.909621e-02
sample39 0.0746847148 7.148218e-03
sample40 -0.0517028178 -2.137267e-02
sample41 0.0234979494 -3.723018e-02
sample42 0.0388797356 -8.557228e-03
sample43 0.0149555568 7.210002e-03
sample44 -0.1150305613 3.461805e-02
sample45 0.0846146236 3.486020e-02
sample46 0.0884426404 -3.246853e-02
sample47 0.0748644971 -8.083045e-03
sample48 -0.0012033198 -9.403647e-03
sample49 -0.0872662737 3.616245e-02
sample50 0.0066941314 -5.284863e-02
sample51 -0.0411777630 3.791830e-02
sample52 -0.0379355780 2.180834e-02
sample53 -0.0851639886 4.751761e-02
sample54 0.0288006248 7.184424e-03
sample55 -0.0164920835 5.919925e-05
sample56 0.0355115616 1.951043e-02
sample57 -0.0141146068 3.492409e-02
sample58 -0.0015636132 9.862883e-03
sample59 -0.0390656483 3.590929e-03
sample60 -0.0139454780 3.963030e-03
sample61 -0.0106410274 1.919705e-02
sample62 0.0236748439 -5.922677e-03
sample63 -0.0846790877 3.839102e-02
sample64 -0.1202581015 2.846469e-02
sample65 0.0050548584 6.328644e-03
sample66 0.0028013072 1.291807e-02
sample67 -0.1231623009 -2.112565e-02
sample68 -0.0437782161 -2.845072e-02
sample69 -0.0501199692 2.053469e-03
sample70 -0.0140278645 -2.027157e-02
sample71 0.0057489505 -4.085977e-02
sample72 0.0511212704 -5.522408e-03
sample73 0.0828141409 7.431582e-03
sample74 -0.0085959456 1.772951e-02
sample75 -0.0312180394 -6.636869e-03
sample76 -0.0519051781 -1.640191e-02
sample77 -0.0925924762 -6.907800e-03
sample78 0.1163971046 2.251122e-02
sample79 -0.0240906926 -4.887766e-02
sample80 -0.0221327065 -6.730703e-03
sample81 -0.0072114968 -2.254399e-02
sample82 0.1204416674 -9.907422e-03
sample83 0.0386739485 -1.171663e-03
sample84 0.0195988488 -1.033806e-02
sample85 -0.0877680171 -1.725057e-03
sample86 0.1023541048 -1.062501e-02
sample87 0.0425213089 -1.356865e-02
sample88 0.0244788514 1.180820e-02
sample89 0.0804276691 2.188588e-02
sample90 -0.0074639871 -6.140721e-03
sample91 -0.1278832404 -6.485140e-02
sample92 -0.0162199697 -5.048358e-02
sample93 0.0769344893 -3.045135e-03
sample94 -0.0104345587 -3.593172e-03
sample95 -0.0260058453 -2.330475e-02
sample96 -0.0025018700 -1.433516e-02
sample97 -0.0492358305 7.774183e-03
sample98 0.0279220220 -3.862141e-03
sample99 0.0813921923 3.487339e-02
sample100 -0.0428797405 7.112807e-03
sample101 0.0032855240 -4.940743e-02
sample102 0.0038439317 2.938008e-03
sample103 0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105 0.0314853405 -4.656633e-03
sample106 0.0726456731 1.192390e-02
sample107 0.0807342975 7.508627e-03
sample108 0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110 0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112 0.0599954082 8.820317e-03
sample113 -0.0195006577 1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131 6.937326e-03
sample121 0.0613899126 2.915307e-02
sample122 0.0218424338 2.241775e-02
sample123 0.0809008460 -1.051759e-02
sample124 -0.0472109313 1.239887e-02
sample125 0.0583180947 2.521167e-03
sample126 -0.0753941872 2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128 0.1000212216 2.908091e-02
sample129 0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877 4.091672e-03
sample132 -0.0566219024 2.179861e-02
sample133 0.0384172955 -1.372840e-02
sample134 -0.1280862736 2.077912e-02
sample135 0.0592633273 6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861 6.729304e-03
sample140 0.0468926306 -1.285498e-02
sample141 -0.0186248693 1.605439e-02
sample142 0.0328031246 9.887746e-03
sample143 0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310 3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147 0.0765025670 -7.714769e-03
sample148 -0.0276016641 2.420589e-02
sample149 0.0027545308 -4.653007e-02
sample150 -0.0792296010 1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152 0.0409796547 -1.907063e-02
sample153 0.0734301757 5.528780e-02
sample154 0.0557740684 -2.487723e-02
sample155 0.0689436560 -6.127635e-03
sample156 0.0212272938 -9.747423e-03
sample157 0.0911931194 6.355708e-03
sample158 0.0220840645 -3.016357e-03
sample159 -0.0513244242 1.304175e-02
sample160 0.0246213576 1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162 0.1078802043 4.337260e-02
sample163 -0.1017965082 5.047171e-02
sample164 0.0119430799 9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180 1.899222e-02
sample167 -0.0872832229 1.516582e-04
sample168 0.0540714512 1.397701e-02
sample169 0.0328432652 7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
[,1] [,2]
sample1 0.0133684846 2.195848e-02
sample2 0.0254157197 -1.058416e-02
sample3 -0.0049551479 -4.840017e-03
sample4 0.0310390570 -1.063929e-02
sample5 0.0046941318 -6.488426e-03
sample6 -0.0107406753 -1.026702e-02
sample7 -0.0225157631 2.624712e-04
sample8 0.0141320952 -9.505821e-03
sample9 0.0029681280 2.078210e-02
sample10 0.0131729174 -2.275042e-03
sample11 -0.0004164298 1.994019e-02
sample12 -0.0095211620 3.759883e-02
sample13 0.0091018604 -7.953956e-03
sample14 -0.0106557524 -9.181659e-03
sample15 -0.0249924121 3.262724e-02
sample16 -0.0156216400 1.375700e-02
sample17 -0.0019382446 1.073994e-03
sample18 -0.0221072481 -8.703592e-03
sample19 0.0146917619 -1.311712e-02
sample20 -0.0160353760 1.826290e-02
sample21 0.0035947899 -9.616341e-03
sample22 -0.0225060762 -2.532589e-03
sample23 0.0310000683 3.033060e-03
sample24 0.0499544372 1.809450e-02
sample25 0.0284442301 -1.932558e-02
sample26 0.0188220043 2.146985e-02
sample27 -0.0257763219 -1.999228e-03
sample28 0.0120888648 1.125834e-02
sample29 -0.0236482520 4.426726e-02
sample30 -0.0385486305 -2.055935e-02
sample31 -0.0181539336 -5.877838e-03
sample32 -0.0302630460 -2.607192e-03
sample33 -0.0319565715 -1.562628e-02
sample34 -0.0197970124 9.906813e-03
sample35 -0.0247412713 -5.434440e-03
sample36 -0.0386259060 -3.190394e-02
sample37 -0.0566199273 -4.192574e-02
sample38 -0.0142060273 2.259644e-02
sample39 0.0053589035 1.076485e-02
sample40 -0.0552546493 -3.819896e-02
sample41 -0.0013089975 9.278818e-05
sample42 0.0137252142 -1.664652e-02
sample43 -0.0151259626 -6.290953e-03
sample44 0.0617391754 -1.442883e-02
sample45 0.0231410886 1.163143e-03
sample46 -0.0148898209 -1.384176e-04
sample47 -0.0187252536 1.221690e-02
sample48 0.0432839432 1.416671e-02
sample49 0.0160818605 -3.588745e-02
sample50 0.0059333545 4.067003e-02
sample51 -0.0142914866 7.776270e-03
sample52 -0.0086339952 7.208917e-03
sample53 -0.0207386980 6.272432e-03
sample54 -0.0039856719 -1.316934e-02
sample55 -0.0056217017 5.692315e-03
sample56 0.0000123292 8.978290e-04
sample57 -0.0095805555 1.324253e-02
sample58 -0.0124160295 -7.326376e-03
sample59 -0.0400195442 -1.349736e-02
sample60 -0.0460063358 2.770091e-02
sample61 -0.0245266456 1.470710e-02
sample62 -0.0366022783 -3.437352e-03
sample63 0.0013742171 3.288796e-02
sample64 -0.0070599859 2.739588e-02
sample65 0.0041201911 1.498268e-02
sample66 0.0143173351 -1.968812e-02
sample67 -0.0467477531 -1.929938e-02
sample68 -0.0306751978 -1.436184e-02
sample69 -0.0125317217 4.130407e-03
sample70 -0.0068071487 8.080857e-03
sample71 0.0169170264 -7.027348e-03
sample72 -0.0346909749 -1.333770e-02
sample73 -0.0280506153 1.493843e-02
sample74 -0.0182611498 3.294697e-03
sample75 -0.0120563964 8.974612e-03
sample76 0.0001437236 -4.253184e-02
sample77 0.0065330299 -5.252886e-02
sample78 0.0288278141 -1.127782e-02
sample79 0.0503961481 -1.023318e-02
sample80 -0.0207693429 3.648391e-02
sample81 0.0163562768 -9.074596e-03
sample82 -0.0084317129 -1.478976e-02
sample83 -0.0474097918 -1.103126e-02
sample84 0.0177181395 -7.191197e-03
sample85 -0.0342718548 -3.082360e-02
sample86 -0.0261671791 -1.089491e-02
sample87 -0.0009486358 -2.411514e-02
sample88 0.0020528931 -2.894615e-02
sample89 -0.0189361111 -2.638639e-03
sample90 -0.0009863658 -2.390075e-02
sample91 -0.0124352695 8.153234e-02
sample92 0.0564264106 -8.909537e-03
sample93 -0.0081461774 1.570851e-02
sample94 -0.0054896581 1.547251e-02
sample95 0.0224073150 -4.374348e-04
sample96 0.0173528924 -3.050441e-03
sample97 0.0067948115 5.008237e-03
sample98 -0.0116030825 1.498764e-02
sample99 0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101 0.0124923691 3.091503e-02
sample102 0.0650835386 -1.367400e-02
sample103 -0.0042741828 7.855985e-03
sample104 0.0250591040 -4.171938e-03
sample105 0.0157516368 -3.121990e-02
sample106 0.0060593853 -5.101693e-03
sample107 -0.0098329626 1.044506e-02
sample108 0.0044269853 4.142036e-03
sample109 0.0572473486 1.517542e-02
sample110 0.0090474827 -5.119868e-03
sample111 0.0444263015 7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113 0.0241047399 6.706740e-03
sample114 0.0074558775 -4.728652e-03
sample115 0.0611851433 1.117210e-02
sample116 0.0432646951 -1.380556e-02
sample117 0.0516750066 -3.575617e-02
sample118 0.0139942100 -3.279138e-03
sample119 0.0291722987 5.587946e-02
sample120 0.0103515853 -1.690016e-03
sample121 -0.0091396331 3.552116e-02
sample122 0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124 0.0283466326 3.127845e-03
sample125 0.0016472378 -2.770692e-02
sample126 -0.0286529417 3.489336e-02
sample127 -0.0010224500 7.483214e-03
sample128 0.0209049296 2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968 4.637390e-03
sample132 0.0198526786 5.723983e-04
sample133 0.0088812957 -9.988115e-03
sample134 -0.0137484514 1.172591e-02
sample135 -0.0220314568 1.347465e-02
sample136 -0.0185173353 5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138 0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762 2.456863e-02
sample141 0.0058369763 -1.420854e-02
sample142 0.0207886071 -1.188764e-02
sample143 0.0092832598 -1.324238e-02
sample144 0.0028442140 3.627979e-03
sample145 0.0199749569 2.862202e-03
sample146 -0.0182236697 1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148 0.0065435868 -1.572917e-02
sample149 0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151 0.0245166984 -6.888241e-03
sample152 0.0107259913 3.314630e-02
sample153 0.0550963965 3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574 2.642246e-03
sample156 -0.0117803505 2.698265e-02
sample157 -0.0096167165 1.433840e-02
sample158 -0.0101754772 9.137620e-03
sample159 0.0120662931 -2.565236e-02
sample160 -0.0132238202 2.916023e-03
sample161 0.0274491966 -1.748284e-02
sample162 0.0012482909 3.152261e-02
sample163 0.0042031315 1.830701e-02
sample164 0.0174896157 -1.175915e-02
sample165 0.0097517662 -6.119019e-03
sample166 0.0190134679 -1.121582e-02
sample167 -0.0044140836 4.665585e-03
sample168 0.0049689168 -1.941822e-02
sample169 -0.0209802098 3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
[,1] [,2]
sample1 -0.0515543627 -0.0305856787
sample2 -0.0144993256 0.0236342950
sample3 -0.0371833108 -0.0140263348
sample4 0.0068945388 -0.0132539692
sample5 0.0215035333 -0.0663338101
sample6 -0.0187055152 0.0088773016
sample7 -0.0061521552 0.0064029054
sample8 -0.0210874459 0.0334652901
sample9 0.0516865043 -0.0291142799
sample10 0.0059440366 -0.0527217447
sample11 0.0393010793 -0.0200624712
sample12 -0.0420837100 0.0131331362
sample13 0.0333252565 0.0818552509
sample14 -0.0190062644 0.0160202175
sample15 -0.0030968049 -0.0189230681
sample16 -0.0004452158 0.0018880102
sample17 -0.0185848615 0.0240170131
sample18 -0.0273093598 0.0230213640
sample19 -0.0217761111 -0.0445894441
sample20 0.0245820821 0.0159812738
sample21 0.0034527644 -0.0400016054
sample22 -0.0340789054 0.0039289109
sample23 -0.0010344929 -0.0310161212
sample24 0.0289468503 0.0760962436
sample25 -0.0119098496 -0.0122798760
sample26 -0.0181001057 0.0517892852
sample27 0.0050465417 -0.0086515844
sample28 0.0057491502 0.0358830107
sample29 -0.0051104246 0.0116605117
sample30 -0.0103085904 0.0039678538
sample31 -0.0319929858 0.0090606113
sample32 -0.0036232521 -0.0328202010
sample33 -0.0534742153 0.0024751837
sample34 -0.0067495749 -0.0111000311
sample35 0.0378745721 0.0465929296
sample36 0.0647886800 0.0359987924
sample37 0.0488441236 0.0492906912
sample38 -0.0251514062 0.0197110110
sample39 -0.0085428066 -0.0105117852
sample40 0.0379324087 0.0440810741
sample41 -0.0044199152 -0.0128820644
sample42 -0.0292553573 -0.0067045265
sample43 -0.0077829155 -0.0510178219
sample44 0.0045122248 0.0479660309
sample45 -0.0074444298 -0.0051116726
sample46 -0.0088025512 0.0196186661
sample47 0.0076696301 0.0215947965
sample48 0.0290108585 -0.0175568376
sample49 -0.0141754858 0.0184717099
sample50 0.0006282201 -0.0233054373
sample51 0.0441995177 -0.0410022921
sample52 0.0715329391 -0.0399499475
sample53 -0.0095954087 -0.0029140909
sample54 0.0048933768 -0.0281884386
sample55 0.0327325487 -0.0532290012
sample56 0.0323068984 -0.0256595538
sample57 0.0806603122 -0.0286748097
sample58 -0.0064792049 -0.0006945349
sample59 0.0088958941 0.0067389649
sample60 0.0874124612 0.0431964341
sample61 0.0577604571 -0.0326112099
sample62 -0.0313318464 0.0224391756
sample63 -0.0233625220 0.0125110562
sample64 -0.0086426068 0.0148770341
sample65 0.0025256193 -0.0404466327
sample66 0.0006014071 -0.0471576264
sample67 0.0706087042 0.0516228406
sample68 0.0082301011 0.0033109509
sample69 -0.0475076743 0.0001452708
sample70 -0.0600773716 0.0089986962
sample71 -0.0096321627 -0.0050761187
sample72 -0.0031773546 -0.0166221542
sample73 -0.0113700517 -0.0191726684
sample74 -0.0014179662 -0.0608101325
sample75 0.0041911740 -0.0399981269
sample76 -0.0055326449 0.0353114263
sample77 -0.0260214459 0.0305731380
sample78 -0.0119267436 0.0632236007
sample79 0.0186017239 0.0027402910
sample80 0.0241047889 -0.0472697181
sample81 -0.0220288317 -0.0079577210
sample82 -0.0180751258 0.0639051029
sample83 -0.0256671713 -0.0125898269
sample84 0.0161392598 -0.0567222449
sample85 0.0139988188 0.0322763454
sample86 -0.0198382995 0.0389225776
sample87 0.0266270281 -0.0032979996
sample88 0.0515677078 0.0117902495
sample89 0.0014022125 -0.0140510488
sample90 -0.0375949749 0.0044004551
sample91 0.0310397965 0.0440610926
sample92 0.0270570567 0.0324380452
sample93 -0.0215009202 0.0063993941
sample94 -0.0415702912 -0.0037692077
sample95 -0.0168416047 0.0010019120
sample96 -0.0285582661 -0.0187991000
sample97 -0.0490843868 -0.0266760748
sample98 -0.0171579033 -0.0112897471
sample99 -0.0271316525 0.0232395583
sample100 -0.0301789816 0.0305498693
sample101 -0.0264371151 0.0170723968
sample102 0.0012767734 -0.0248949597
sample103 0.0055214687 -0.0030040587
sample104 0.0251346074 -0.0165212671
sample105 0.0062424215 -0.0400309901
sample106 0.0069768684 0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679 0.0023637162
sample109 -0.0014762845 0.0165583675
sample110 0.0036971063 0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112 0.0046098120 -0.0048009350
sample113 0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453 0.0135805368
sample116 0.0183575759 0.0665377581
sample117 0.0227640036 -0.0012287760
sample118 0.0015695248 0.0472617382
sample119 0.0190084932 0.0590034062
sample120 -0.0449645755 0.0072755697
sample121 0.0077307184 0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123 0.0016959300 0.0028593594
sample124 -0.0365091615 0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408 0.0019165544
sample127 -0.0494064872 0.0088209044
sample128 -0.0155454766 0.0186819802
sample129 -0.0184340400 0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422 0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133 0.0204029276 -0.0282209049
sample134 0.0175513332 0.0262883962
sample135 0.0029009925 0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323 0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139 0.0073103935 0.0308956174
sample140 0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142 0.0268670676 -0.0239229634
sample143 0.0421049126 -0.0110888235
sample144 0.0017253664 -0.0341766012
sample145 0.0681741320 -0.0073526377
sample146 -0.0239965222 0.0118396767
sample147 -0.0063453522 0.0183130585
sample148 0.0230825251 -0.0379753037
sample149 0.0223298673 0.0188909118
sample150 0.0055709108 0.0174179009
sample151 0.0039177786 -0.0233533275
sample152 0.0134325667 0.0302344591
sample153 0.0511990309 0.0730230140
sample154 0.0006698324 0.0154177486
sample155 0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599 0.0154934573
sample158 0.0201775524 -0.0332982124
sample159 -0.0086909001 0.0073496711
sample160 0.0295437331 -0.0555734536
sample161 0.0332754288 0.0033779619
sample162 0.0121954537 0.0433540412
sample163 -0.0173490933 0.0227219128
sample164 0.0143374783 -0.0453542590
sample165 0.0343612593 -0.0511194536
sample166 -0.0157536004 0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919 0.0060747155
sample169 0.0116231468 -0.0015112800
>
> ## 3.3 Plotting VAF
>
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
>
> # JIVE plotVAF
> plotVAF(jiveRes)
>
>
> #########################
> ## PART 4. Plot Results
>
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
>
> # Scores for common part. JIVE
> plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
>
> # Scores for common part. O2PLS.
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for common part. O2PLS.
> plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common",
+ combined=TRUE,block=NULL,color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
>
> # Scores for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for distinctive part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual",
+ combined=TRUE,block=NULL,color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
> # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block)
> p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for common and distinctive part. DISCO (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Loadings for common part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> # Loadings for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> # Combined plot for loadings from common and distinctive part (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
>
> ## Plot scores and loadings togheter: Common components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> ## Plot scores and loadings togheter: Common components O2PLS
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> ## Plot scores and loadings togheter: Distintive components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
>
>
> proc.time()
user system elapsed
15.46 0.64 16.31
|
STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsPCA.Rout
R version 3.5.1 Patched (2018-07-24 r75005) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-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.
> ###########################################
> ########### EXAMPLE OF THE OMICSPCA
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
>
> # g_legend (not exported by STATegRa any more)
> ## code from https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs
> g_legend<-function(a.gplot){
+ tmp <- ggplot_gtable(ggplot_build(a.gplot))
+ leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
+ legend <- tmp$grobs[[leg]]
+ return(legend)}
>
> #########################
> ## PART 1. Load data
>
> ## Load data
> data(STATegRa_S3)
>
> ls()
[1] "Block1.PCA" "Block2.PCA" "ed.PCA" "g_legend"
>
> ## Create ExpressionSets
> # Block1 - Expression data
> B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname"))
>
> #########################
> ## PART 2. Model Selection
>
> require(grid)
Loading required package: grid
> require(gridExtra)
Loading required package: gridExtra
> require(ggplot2)
Loading required package: ggplot2
>
> ## Select the optimal components
> ms <- modelSelection(Input=list(B1,B2),Rmax=4,fac.sel="single%",varthreshold=0.03,center=TRUE,scale=TRUE,weight=TRUE)
Common components
[1] 2
Distinctive components
[[1]]
[1] 2
[[2]]
[1] 2
>
>
> #########################
> ## PART 3. Component Analysis
>
> ## 3.1 Component analysis of the three methods
> discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+ scale=TRUE,weight=TRUE)
> jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+ scale=TRUE,weight=TRUE)
> o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+ scale=TRUE,weight=TRUE)
>
> ## 3.2 Exploring scores structures
>
> # Exploring DISCO-SCA scores structure
> discoRes@scores$common ## Common scores
1 2
sample1 -0.0781574357 -0.0431503625
sample2 0.1192218499 0.0294086372
sample3 0.0531412001 -0.0746839743
sample4 -0.0292975037 -0.0005962361
sample5 -0.0202091717 0.0110463494
sample6 -0.1226089040 0.1053467940
sample7 -0.1078928219 -0.0322473935
sample8 -0.1782895171 0.1449362899
sample9 -0.0468698089 -0.0455174225
sample10 0.0036030592 0.0420109977
sample11 0.0035566473 -0.0566292358
sample12 -0.1006128936 0.0641381171
sample13 0.1174408481 0.0907487914
sample14 -0.0981203274 0.0617738953
sample15 -0.0085334404 -0.0087011664
sample16 -0.0783148602 0.1581295724
sample17 0.1483609939 0.0638581859
sample18 0.0963086185 0.0556641699
sample19 0.0217244065 -0.0720087510
sample20 0.0635636324 -0.0779651775
sample21 0.0201840465 0.1566391085
sample22 -0.0218268902 -0.0764102852
sample23 -0.0852041916 -0.0032691232
sample24 0.1287170983 0.1924539728
sample25 0.0430574200 -0.0456568444
sample26 0.1453896929 0.0541510390
sample27 0.0197488641 -0.1185654753
sample28 0.1025336403 0.0650684535
sample29 -0.0706018621 -0.0682986375
sample30 0.1295627378 -0.0066766355
sample31 -0.1147449134 0.1232687930
sample32 0.0374310792 0.0380180284
sample33 -0.0599516154 0.0136869421
sample34 0.0984200768 0.0375322460
sample35 0.0543098301 -0.0378103686
sample36 -0.1403625533 -0.0343752385
sample37 -0.0228942078 -0.0732841276
sample38 0.0222077114 -0.0962593964
sample39 0.0941738516 0.0215198596
sample40 -0.0643801355 -0.0687866168
sample41 0.0327637928 -0.1232188123
sample42 0.0500431832 -0.0292474627
sample43 0.0184498775 0.0233012175
sample44 -0.1487898480 0.1171349689
sample45 0.1050774325 0.1123199602
sample46 0.1151195582 -0.1094027725
sample47 0.0962593654 -0.0288462305
sample48 -0.0004837167 -0.0310281078
sample49 -0.1135207665 0.1213971838
sample50 0.0123553029 -0.1740744314
sample51 -0.0550529785 0.1258888114
sample52 -0.0499121131 0.0728545706
sample53 -0.1119773625 0.1588015441
sample54 0.0360055680 0.0228575939
sample55 -0.0210418980 0.0006732353
sample56 0.0434169308 0.0633126239
sample57 -0.0197824481 0.1150714850
sample58 -0.0030439919 0.0326098921
sample59 -0.0500253239 0.0129422024
sample60 -0.0184278710 0.0136089352
sample61 -0.0150299392 0.0635028022
sample62 0.0304763727 -0.0201316760
sample63 -0.1102252404 0.1285976667
sample64 -0.1552588051 0.0971168836
sample65 0.0058503087 0.0207115087
sample66 0.0025605422 0.0424318919
sample67 -0.1546634952 -0.0661712296
sample68 -0.0536369431 -0.0923681324
sample69 -0.0640330454 0.0081983701
sample70 -0.0163517872 -0.0663230026
sample71 0.0102537586 -0.1345922675
sample72 0.0654195911 -0.0196117040
sample73 0.1048556051 0.0220940101
sample74 -0.0123799501 0.0586116400
sample75 -0.0392077987 -0.0209754164
sample76 -0.0648953425 -0.0524764265
sample77 -0.1172922151 -0.0201187111
sample78 0.1463068221 0.0708470237
sample79 -0.0265211117 -0.1603311386
sample80 -0.0279737206 -0.0214203581
sample81 -0.0079211511 -0.0738452149
sample82 0.1544236438 -0.0361467452
sample83 0.0494211215 -0.0050045309
sample84 0.0259038521 -0.0346550748
sample85 -0.1116484455 -0.0031495030
sample86 0.1306482905 -0.0377213510
sample87 0.0554778191 -0.0459748648
sample88 0.0301623923 0.0382197839
sample89 0.1016866700 0.0694035107
sample90 -0.0086819932 -0.0201320220
sample91 -0.1578625472 -0.2097827073
sample92 -0.0170936733 -0.1655810638
sample93 0.0979806768 -0.0121511930
sample94 -0.0131484158 -0.0114932025
sample95 -0.0315682621 -0.0758860748
sample96 -0.0024125617 -0.0470136949
sample97 -0.0634545419 0.0270331118
sample98 0.0359374579 -0.0135487831
sample99 0.1009163444 0.1124778138
sample100 -0.0551753165 0.0246489886
sample101 0.0080118811 -0.1627369635
sample102 0.0046444518 0.0095627598
sample103 0.0472523114 -0.0940393032
sample104 -0.0198159420 -0.0591093093
sample105 0.0400237840 -0.0160912964
sample106 0.0923808463 0.0369017335
sample107 0.1019373904 0.0224954513
sample108 0.0877091650 -0.0128834630
sample109 -0.0864824267 -0.0900945512
sample110 0.1223115563 -0.0096086343
sample111 -0.0257354559 -0.0936172127
sample112 0.0765286585 0.0270348424
sample113 -0.0258803137 0.0377495683
sample114 -0.0021138978 -0.0882015555
sample115 -0.0303460062 -0.0723589688
sample116 -0.0780508325 -0.0685068958
sample117 -0.0536897990 -0.0911911501
sample118 -0.0666651125 -0.0236231890
sample119 -0.1021871664 -0.2324938647
sample120 -0.0750216550 0.0243378234
sample121 0.0756936438 0.0942951094
sample122 0.0259628204 0.0731985479
sample123 0.1037846212 -0.0369196717
sample124 -0.0611207849 0.0421721619
sample125 0.0738472720 0.0066950014
sample126 -0.0972916518 0.0762641542
sample127 -0.0824697685 -0.0096637518
sample128 0.1249407751 0.0929311097
sample129 0.0734067409 -0.0434361613
sample130 0.0003501957 -0.0309852781
sample131 -0.0930182851 0.0155937986
sample132 -0.0736222748 0.0733028310
sample133 0.0498397996 -0.0462437919
sample134 -0.1644873474 0.0720006681
sample135 0.0752297140 0.0003819110
sample136 -0.0227145888 -0.0495505063
sample137 -0.0564717532 -0.0288914305
sample138 -0.0255988063 -0.0610858949
sample139 -0.0621217838 0.0235808999
sample140 0.0604152441 -0.0435591646
sample141 -0.0246743936 0.0532648247
sample142 0.0409560433 0.0316278694
sample143 0.0077355258 -0.0476896586
sample144 -0.0173240822 -0.0156778119
sample145 -0.0485474328 0.1202769766
sample146 -0.0419645756 -0.0811280250
sample147 0.0977308244 -0.0274838246
sample148 -0.0368256097 0.0803979450
sample149 0.0072865766 -0.1532986826
sample150 -0.1020825300 0.0624775881
sample151 -0.0305399005 -0.0289279713
sample152 0.0533594807 -0.0638309828
sample153 0.0891627966 0.1799574992
sample154 0.0727557384 -0.0834160090
sample155 0.0880668495 -0.0220818263
sample156 0.0276560999 -0.0326624664
sample157 0.1155032168 0.0183616575
sample158 0.0281507507 -0.0104937905
sample159 -0.0663235658 0.0443836616
sample160 0.0302643905 0.0404266475
sample161 -0.0114715493 -0.0591026965
sample162 0.1337087222 0.1398135330
sample163 -0.1330124383 0.1688780933
sample164 0.0150336162 0.0028415161
sample165 -0.0076520221 -0.0164128792
sample166 -0.0367794295 0.0630660782
sample167 -0.1111988892 0.0030058077
sample168 0.0672981637 0.0446278985
sample169 0.0413004935 0.0224395663
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 0.0420513387 0.0867863173
sample2 0.0820829142 -0.0410977763
sample3 -0.0155901538 -0.0195182494
sample4 0.1001337280 -0.0410786383
sample5 0.0153466374 -0.0253259628
sample6 -0.0340323556 -0.0408223192
sample7 -0.0722580361 0.0002331996
sample8 0.0457503094 -0.0370015942
sample9 0.0086248432 0.0820184888
sample10 0.0423599492 -0.0083923119
sample11 -0.0022549795 0.0787765984
sample12 -0.0322105053 0.1479824655
sample13 0.0293891876 -0.0306748444
sample14 -0.0337481319 -0.0367506895
sample15 -0.0815539692 0.1275622239
sample16 -0.0508449070 0.0540604659
sample17 -0.0062595637 0.0041023754
sample18 -0.0705638818 -0.0351047849
sample19 0.0476840226 -0.0509598020
sample20 -0.0522964507 0.0715521632
sample21 0.0119129941 -0.0376092907
sample22 -0.0724394800 -0.0095625403
sample23 0.0992532100 0.0134289082
sample24 0.1595121898 0.0728662676
sample25 0.0920692550 -0.0749757041
sample26 0.0595540761 0.0848966267
sample27 -0.0826488018 -0.0086735784
sample28 0.0384789337 0.0440967050
sample29 -0.0777673367 0.1735308224
sample30 -0.1229471359 -0.0819005894
sample31 -0.0579843901 -0.0238644804
sample32 -0.0970392447 -0.0111426552
sample33 -0.1017587798 -0.0630442854
sample34 -0.0637922356 0.0377941555
sample35 -0.0789984960 -0.0229723484
sample36 -0.1224939116 -0.1274955321
sample37 -0.1798821445 -0.1673428017
sample38 -0.0466306814 0.0888160716
sample39 0.0168687820 0.0421533820
sample40 -0.1756392746 -0.1526642926
sample41 -0.0042373298 0.0004928671
sample42 0.0447849104 -0.0651504907
sample43 -0.0482307984 -0.0253529395
sample44 0.1986717439 -0.0545777170
sample45 0.0741838510 0.0054703601
sample46 -0.0478774422 -0.0007072264
sample47 -0.0608189253 0.0481622431
sample48 0.1381488792 0.0578288147
sample49 0.0530523233 -0.1405532560
sample50 0.0173795939 0.1602389553
sample51 -0.0462558395 0.0303473823
sample52 -0.0280063293 0.0280388385
sample53 -0.0667618348 0.0237702001
sample54 -0.0121832992 -0.0521354339
sample55 -0.0182395804 0.0221328380
sample56 0.0001256899 0.0030907417
sample57 -0.0316672959 0.0530190302
sample58 -0.0393917487 -0.0297798829
sample59 -0.1278290418 -0.0546528308
sample60 -0.1486984635 0.1069156149
sample61 -0.0793121135 0.0569796357
sample62 -0.1172801547 -0.0149198847
sample63 0.0028728857 0.1300519966
sample64 -0.0237363024 0.1073287748
sample65 0.0126535124 0.0589808491
sample66 0.0468195786 -0.0771072521
sample67 -0.1494265094 -0.0769860775
sample68 -0.0977962710 -0.0577351419
sample69 -0.0403087385 0.0156042008
sample70 -0.0221532924 0.0315440829
sample71 0.0546432048 -0.0272396407
sample72 -0.1107488103 -0.0537319717
sample73 -0.0906761152 0.0579966360
sample74 -0.0586554356 0.0121421556
sample75 -0.0390493735 0.0349282680
sample76 0.0022960238 -0.1676558831
sample77 0.0232096398 -0.2067302743
sample78 0.0929756266 -0.0434939155
sample79 0.1619493999 -0.0378113913
sample80 -0.0680366203 0.1424663277
sample81 0.0530782663 -0.0358350763
sample82 -0.0266822661 -0.0577445220
sample83 -0.1517235346 -0.0448554787
sample84 0.0570966568 -0.0273813111
sample85 -0.1086289234 -0.1228119642
sample86 -0.0833860712 -0.0442915265
sample87 -0.0022018911 -0.0943906905
sample88 0.0078226191 -0.1140506472
sample89 -0.0611056009 -0.0094585270
sample90 -0.0022928402 -0.0936254019
sample91 -0.0433595000 0.3205982474
sample92 0.1815332686 -0.0334679913
sample93 -0.0267631341 0.0614428933
sample94 -0.0181878359 0.0605090342
sample95 0.0720374243 -0.0013045514
sample96 0.0559713779 -0.0118791299
sample97 0.0217411253 0.0195414232
sample98 -0.0379177934 0.0588356980
sample99 0.0792429223 -0.0151273468
sample100 -0.0222116051 -0.0023321472
sample101 0.0387225092 0.1224226180
sample102 0.2094614478 -0.0516441998
sample103 -0.0138483065 0.0301051824
sample104 0.0807986102 -0.0162718735
sample105 0.0520493366 -0.1229665031
sample106 0.0192614003 -0.0185238097
sample107 -0.0319017105 0.0405123210
sample108 0.0140690582 0.0163421407
sample109 0.1831928338 0.0613008020
sample110 0.0292790401 -0.0199849008
sample111 0.1423250163 0.0327340659
sample112 -0.0426332381 -0.0029083544
sample113 0.0771905205 0.0268733908
sample114 0.0241639675 -0.0184080428
sample115 0.1959014150 0.0460131191
sample116 0.1394475061 -0.0530805453
sample117 0.1672360646 -0.1386535991
sample118 0.0448343962 -0.0117621818
sample119 0.0910381632 0.2217433443
sample120 0.0331392521 -0.0057274374
sample121 -0.0307573514 0.1392506540
sample122 0.0839782559 -0.0291994108
sample123 -0.0239651000 -0.0642163835
sample124 0.0909151244 0.0130419810
sample125 0.0065351177 -0.1092631793
sample126 -0.0935310949 0.1368283844
sample127 -0.0035388341 0.0292755619
sample128 0.0660296771 0.1018566602
sample129 -0.0693639368 -0.0695421988
sample130 -0.0008493996 -0.0669704361
sample131 -0.0431023730 0.0174064759
sample132 0.0637041313 0.0029374980
sample133 0.0289494066 -0.0390818792
sample134 -0.0446201554 0.0456334451
sample135 -0.0712337157 0.0521634742
sample136 -0.0596272156 0.0197299099
sample137 -0.0793152535 -0.0380628567
sample138 0.0973547317 -0.0454218030
sample139 -0.0539903755 -0.1534327497
sample140 -0.0850828122 0.0955814235
sample141 0.0192682873 -0.0554449962
sample142 0.0672262824 -0.0461320687
sample143 0.0303729889 -0.0519260192
sample144 0.0089364256 0.0145814925
sample145 0.0638772555 0.0122258717
sample146 -0.0585857931 0.0063083100
sample147 -0.0894133646 -0.1124615996
sample148 0.0216368758 -0.0615966985
sample149 0.0515418192 -0.0839903482
sample150 -0.0568282017 -0.0124469027
sample151 0.0789531960 -0.0261830977
sample152 0.0330751976 0.1306443624
sample153 0.1751934463 0.1497732798
sample154 -0.0421425931 -0.0037010401
sample155 -0.0680178009 0.0095711002
sample156 -0.0388912102 0.1057562809
sample157 -0.0314769329 0.0561367344
sample158 -0.0329620770 0.0353947213
sample159 0.0398417689 -0.1007373610
sample160 -0.0424937920 0.0108496085
sample161 0.0888370588 -0.0679699960
sample162 0.0027478176 0.1237844000
sample163 0.0126108251 0.0725434548
sample164 0.0566779883 -0.0458324012
sample165 0.0315336312 -0.0236362271
sample166 0.0612059388 -0.0425232793
sample167 -0.0142729866 0.0179308240
sample168 0.0169504470 -0.0769617808
sample169 -0.0675079939 0.0131505149
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 -0.0012329747 1.635717e-01
sample2 -0.0724350225 6.021288e-03
sample3 -0.0188460421 1.080036e-01
sample4 0.0390145186 -3.113853e-04
sample5 0.1774811587 2.996386e-02
sample6 -0.0451444535 3.455860e-02
sample7 -0.0226466112 7.020127e-03
sample8 -0.1033680453 9.856822e-03
sample9 0.1350011874 -8.979098e-02
sample10 0.1259887106 5.097856e-02
sample11 0.0979788504 -7.086535e-02
sample12 -0.0863019202 8.620317e-02
sample13 -0.1381401153 -1.828007e-01
sample14 -0.0615073911 2.642803e-02
sample15 0.0381599026 3.101662e-02
sample16 -0.0048776879 -1.271813e-03
sample17 -0.0788481077 1.547555e-02
sample18 -0.0884188812 3.795486e-02
sample19 0.0703044380 1.084004e-01
sample20 -0.0025585314 -7.975878e-02
sample21 0.0941601395 4.126746e-02
sample22 -0.0550273291 7.806739e-02
sample23 0.0679495197 4.102008e-02
sample24 -0.1310963122 -1.649308e-01
sample25 0.0113585203 4.426864e-02
sample26 -0.1402946068 -2.016540e-02
sample27 0.0261561372 -1.588497e-03
sample28 -0.0724198824 -5.850590e-02
sample29 -0.0330058380 -2.060870e-03
sample30 -0.0228752470 2.015427e-02
sample31 -0.0635068082 6.670335e-02
sample32 0.0685099649 4.955272e-02
sample33 -0.0777765219 1.272078e-01
sample34 0.0157842381 3.024314e-02
sample35 -0.0529632524 -1.500972e-01
sample36 0.0070901121 -2.025308e-01
sample37 -0.0442420168 -1.802089e-01
sample38 -0.0781511153 3.676416e-02
sample39 0.0120331764 3.388843e-02
sample40 -0.0473291670 -1.471562e-01
sample41 0.0228189554 2.673551e-02
sample42 -0.0245360324 7.960867e-02
sample43 0.1036362756 8.229577e-02
sample44 -0.1012229073 -7.049444e-02
sample45 0.0013731769 2.450916e-02
sample46 -0.0558509843 -2.947426e-03
sample47 -0.0380481064 -4.554176e-02
sample48 0.0784342025 -4.888977e-02
sample49 -0.0605164167 1.162359e-02
sample50 0.0530079465 2.737929e-02
sample51 0.1514646462 -5.678342e-02
sample52 0.1860935272 -1.246717e-01
sample53 -0.0064177241 2.700996e-02
sample54 0.0697038298 2.308390e-02
sample55 0.1633577065 -1.366441e-02
sample56 0.1011485041 -4.682203e-02
sample57 0.1730374216 -1.609603e-01
sample58 -0.0071384724 1.666955e-02
sample59 -0.0030461539 -3.005288e-02
sample60 0.0215835480 -2.665878e-01
sample61 0.1510583713 -1.002385e-01
sample62 -0.0925533850 4.845838e-02
sample63 -0.0596311976 4.137025e-02
sample64 -0.0449225874 2.600593e-03
sample65 0.0939383684 4.406910e-02
sample66 0.1063400603 5.709996e-02
sample67 -0.0201589557 -2.361728e-01
sample68 0.0037203459 -2.418395e-02
sample69 -0.0645161252 1.155622e-01
sample70 -0.1013440002 1.351788e-01
sample71 -0.0016467775 2.976839e-02
sample72 0.0328893123 2.835854e-02
sample73 0.0275080044 5.148185e-02
sample74 0.1341719608 7.895281e-02
sample75 0.0951575710 3.943183e-02
sample76 -0.0864721858 -3.034994e-02
sample77 -0.1035749557 2.545352e-02
sample78 -0.1575644319 -4.939590e-02
sample79 0.0189137175 -4.874679e-02
sample80 0.1384140691 -4.266642e-05
sample81 -0.0118846459 6.357931e-02
sample82 -0.1675308102 -3.533914e-02
sample83 -0.0065673320 7.812606e-02
sample84 0.1486891572 3.109059e-02
sample85 -0.0532724226 -7.417888e-02
sample86 -0.1138477231 1.911862e-05
sample87 0.0432864082 -6.080473e-02
sample88 0.0433450409 -1.402490e-01
sample89 0.0331205713 1.395402e-02
sample90 -0.0607412849 8.610413e-02
sample91 -0.0566272176 -1.303748e-01
sample92 -0.0359582379 -1.061604e-01
sample93 -0.0433646374 4.443634e-02
sample94 -0.0477291345 1.059574e-01
sample95 -0.0249595765 3.980525e-02
sample96 0.0035218946 9.293928e-02
sample97 -0.0066048914 1.527231e-01
sample98 0.0020366822 5.579549e-02
sample99 -0.0886616373 3.728230e-02
sample100 -0.1091259161 3.560419e-02
sample101 -0.0739726339 4.317995e-02
sample102 0.0574460911 2.783920e-02
sample103 0.0142731158 -9.705584e-03
sample104 0.0710395244 -4.068350e-02
sample105 0.0980831283 3.452954e-02
sample106 -0.0254259373 -3.628982e-02
sample107 -0.0160653538 9.173395e-02
sample108 -0.0200987690 2.379692e-02
sample109 -0.0389780685 -1.692357e-02
sample110 -0.0326304866 -2.988109e-02
sample111 0.0676937507 6.038214e-02
sample112 0.0167883427 -5.336938e-03
sample113 0.0969216875 2.757607e-02
sample114 -0.0026398329 9.209155e-02
sample115 -0.0308047398 -1.603820e-02
sample116 -0.1240307109 -1.273000e-01
sample117 0.0334729088 -5.392709e-02
sample118 -0.1037152870 -6.252431e-02
sample119 -0.1064176325 -1.196203e-01
sample120 -0.0771355217 1.004933e-01
sample121 -0.0129350839 -3.181974e-02
sample122 0.0847492062 5.568331e-02
sample123 -0.0041336722 -7.693193e-03
sample124 -0.0583458198 8.396392e-02
sample125 0.0634844530 5.232541e-02
sample126 -0.0662581019 1.091732e-01
sample127 -0.0865024654 1.094176e-01
sample128 -0.0627817660 1.470968e-02
sample129 -0.0336276370 4.007856e-02
sample130 -0.0293517767 8.046116e-02
sample131 -0.0469197625 2.209737e-03
sample132 -0.0241740918 1.248599e-01
sample133 0.0907303245 -1.466700e-02
sample134 -0.0350842039 -7.539662e-02
sample135 0.0001333467 -9.185394e-03
sample136 -0.0335876005 9.860271e-02
sample137 -0.0640148838 7.554466e-02
sample138 0.0060964821 1.742763e-02
sample139 -0.0592084383 -5.614970e-02
sample140 0.0427986048 1.099548e-02
sample141 0.0618796230 9.301040e-02
sample142 0.0898554379 -3.573414e-02
sample143 0.0817389314 -8.880524e-02
sample144 0.0787754768 3.821392e-02
sample145 0.1085821487 -1.569476e-01
sample146 -0.0589557800 4.373356e-02
sample147 -0.0495330333 -7.277232e-03
sample148 0.1161592679 -9.079049e-03
sample149 -0.0121579248 -7.788377e-02
sample150 -0.0314512518 -3.520213e-02
sample151 0.0575382125 1.945354e-02
sample152 -0.0494542005 -7.025538e-02
sample153 -0.0941333006 -2.153296e-01
sample154 -0.0335931857 -2.078732e-02
sample155 0.0690457706 2.780408e-02
sample156 0.1039901649 6.292524e-02
sample157 -0.0408645795 -8.065517e-03
sample158 0.1018105353 -7.816878e-03
sample159 -0.0281730631 1.207208e-02
sample160 0.1643052994 -2.978091e-03
sample161 0.0374329309 -8.524610e-02
sample162 -0.0804535476 -8.349751e-02
sample163 -0.0743228191 1.406229e-02
sample164 0.1208805933 2.139462e-02
sample165 0.1608115922 -2.025190e-02
sample166 -0.0425944795 2.660717e-02
sample167 -0.0226849481 4.464281e-02
sample168 -0.0180735673 7.466367e-04
sample169 0.0190779053 -2.645403e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
[,1] [,2]
sample1 -0.0572060227 -1.729087e-02
sample2 0.0875245208 1.112588e-02
sample3 0.0403482602 -3.168994e-02
sample4 -0.0218345996 4.052760e-06
sample5 -0.0150905011 4.795041e-03
sample6 -0.0924362933 4.511003e-02
sample7 -0.0793066751 -1.243823e-02
sample8 -0.1342997187 6.215220e-02
sample9 -0.0338886944 -1.854401e-02
sample10 0.0020547173 1.749421e-02
sample11 0.0037275602 -2.364116e-02
sample12 -0.0753094533 2.772698e-02
sample13 0.0856160091 3.679963e-02
sample14 -0.0737457307 2.668452e-02
sample15 -0.0062111746 -3.554864e-03
sample16 -0.0602355268 6.675115e-02
sample17 0.1086768843 2.524534e-02
sample18 0.0702999472 2.231671e-02
sample19 0.0173785882 -3.024846e-02
sample20 0.0484173812 -3.310904e-02
sample21 0.0124657042 6.517144e-02
sample22 -0.0140989936 -3.159137e-02
sample23 -0.0627028403 -5.393710e-04
sample24 0.0919972100 7.909297e-02
sample25 0.0326998483 -1.945206e-02
sample26 0.1064741246 2.120849e-02
sample27 0.0166058995 -4.964993e-02
sample28 0.0743504770 2.614211e-02
sample29 -0.0511008491 -2.782647e-02
sample30 0.0962250842 -3.974893e-03
sample31 -0.0869563008 5.250819e-02
sample32 0.0271858919 1.552005e-02
sample33 -0.0448364581 6.243160e-03
sample34 0.0718415218 1.469396e-02
sample35 0.0403086451 -1.632629e-02
sample36 -0.1036402827 -1.304320e-02
sample37 -0.0159385744 -3.036525e-02
sample38 0.0182198369 -4.034805e-02
sample39 0.0690363619 8.058350e-03
sample40 -0.0467312750 -2.810325e-02
sample41 0.0263674438 -5.171216e-02
sample42 0.0374578960 -1.268634e-02
sample43 0.0132336869 9.536642e-03
sample44 -0.1119154428 5.028683e-02
sample45 0.0759639367 4.587903e-02
sample46 0.0871885519 -4.670385e-02
sample47 0.0721490571 -1.288540e-02
sample48 0.0005086144 -1.290565e-02
sample49 -0.0858177028 5.173760e-02
sample50 0.0118992665 -7.276215e-02
sample51 -0.0426446855 5.306205e-02
sample52 -0.0381605826 3.086785e-02
sample53 -0.0855757630 6.730043e-02
sample54 0.0261723092 9.184260e-03
sample55 -0.0156418304 4.682404e-04
sample56 0.0307831193 2.597550e-02
sample57 -0.0157242103 4.829381e-02
sample58 -0.0031174404 1.359898e-02
sample59 -0.0373001859 5.868397e-03
sample60 -0.0142609099 5.831654e-03
sample61 -0.0122255144 2.663579e-02
sample62 0.0228002942 -8.692265e-03
sample63 -0.0833127581 5.473229e-02
sample64 -0.1166548159 4.196500e-02
sample65 0.0038808902 8.568590e-03
sample66 0.0011561811 1.766612e-02
sample67 -0.1129311062 -2.608702e-02
sample68 -0.0382526429 -3.804045e-02
sample69 -0.0476502440 4.003241e-03
sample70 -0.0110329882 -2.752719e-02
sample71 0.0096850282 -5.627056e-02
sample72 0.0487124704 -8.800131e-03
sample73 0.0773058132 8.239864e-03
sample74 -0.0102488176 2.454957e-02
sample75 -0.0286613976 -8.387293e-03
sample76 -0.0472655595 -2.129315e-02
sample77 -0.0865043074 -7.296820e-03
sample78 0.1070293698 2.818346e-02
sample79 -0.0165060681 -6.659721e-02
sample80 -0.0206765949 -8.712112e-03
sample81 -0.0050943615 -3.079175e-02
sample82 0.1153622361 -1.647054e-02
sample83 0.0367979217 -2.538114e-03
sample84 0.0199463070 -1.468961e-02
sample85 -0.0827122185 -2.709824e-04
sample86 0.0969487314 -1.699897e-02
sample87 0.0421957457 -1.965953e-02
sample88 0.0215934743 1.566050e-02
sample89 0.0751559502 2.811652e-02
sample90 -0.0057328000 -8.283795e-03
sample91 -0.1134005268 -8.603522e-02
sample92 -0.0101689918 -6.894992e-02
sample93 0.0725967502 -6.003176e-03
sample94 -0.0096878852 -4.693081e-03
sample95 -0.0223502239 -3.139636e-02
sample96 -0.0013232863 -1.963604e-02
sample97 -0.0476541710 1.183660e-02
sample98 0.0269546160 -5.978398e-03
sample99 0.0728179461 4.597884e-02
sample100 -0.0413398038 1.079347e-02
sample101 0.0087536994 -6.796076e-02
sample102 0.0032509529 3.932612e-03
sample103 0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105 0.0294940465 -7.140722e-03
sample106 0.0686472054 1.462895e-02
sample107 0.0748635927 8.401339e-03
sample108 0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110 0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112 0.0570870472 1.066018e-02
sample113 -0.0200110554 1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586 1.089843e-02
sample121 0.0542443861 3.861344e-02
sample122 0.0178575357 3.027138e-02
sample123 0.0775020581 -1.636852e-02
sample124 -0.0460701050 1.814758e-02
sample125 0.0543846585 2.075898e-03
sample126 -0.0729417144 3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128 0.0908136899 3.758801e-02
sample129 0.0552445878 -1.879062e-02
sample130 0.0007128089 -1.294308e-02
sample131 -0.0693311345 7.357082e-03
sample132 -0.0556565156 3.126995e-02
sample133 0.0375870104 -1.977240e-02
sample134 -0.1229130924 3.159495e-02
sample135 0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201 1.040683e-02
sample140 0.0452288969 -1.876279e-02
sample141 -0.0189142561 2.247042e-02
sample142 0.0297545566 1.280524e-02
sample143 0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491 5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147 0.0726083535 -1.239968e-02
sample148 -0.0284795794 3.389732e-02
sample149 0.0082261455 -6.399305e-02
sample150 -0.0765013197 2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152 0.0403422737 -2.714879e-02
sample153 0.0629117719 7.425085e-02
sample154 0.0551622927 -3.548984e-02
sample155 0.0654439133 -1.005306e-02
sample156 0.0209310714 -1.390213e-02
sample157 0.0851522597 6.577150e-03
sample158 0.0208354599 -4.663078e-03
sample159 -0.0498794349 1.913257e-02
sample160 0.0216074437 1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162 0.0963663017 5.705881e-02
sample163 -0.1009542191 7.174224e-02
sample164 0.0109881996 1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357 2.673084e-02
sample167 -0.0825048036 2.278863e-03
sample168 0.0486147429 1.793843e-02
sample169 0.0302506727 8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
[,1] [,2]
sample1 -0.0621842115 -1.364509e-02
sample2 0.0944623785 9.720892e-03
sample3 0.0406196267 -2.236338e-02
sample4 -0.0229316496 -3.932487e-04
sample5 -0.0157330047 3.231033e-03
sample6 -0.0945794025 3.120720e-02
sample7 -0.0854427118 -1.052880e-02
sample8 -0.1376625920 4.286608e-02
sample9 -0.0377115311 -1.415134e-02
sample10 0.0035244506 1.280825e-02
sample11 0.0016639987 -1.717895e-02
sample12 -0.0781403168 1.884368e-02
sample13 0.0938400516 2.838858e-02
sample14 -0.0759839772 1.810989e-02
sample15 -0.0068340837 -2.705361e-03
sample16 -0.0590150849 4.757848e-02
sample17 0.1178805097 2.040526e-02
sample18 0.0767858320 1.756604e-02
sample19 0.0157112113 -2.172867e-02
sample20 0.0485318300 -2.327033e-02
sample21 0.0185928176 4.777095e-02
sample22 -0.0191358702 -2.329775e-02
sample23 -0.0672994194 -1.535656e-03
sample24 0.1047476642 5.935707e-02
sample25 0.0329844953 -1.358036e-02
sample26 0.1154952052 1.741529e-02
sample27 0.0133849853 -3.590922e-02
sample28 0.0821554039 2.042376e-02
sample29 -0.0567643690 -2.123848e-02
sample30 0.1016073931 -1.134728e-03
sample31 -0.0880396372 3.670548e-02
sample32 0.0300363338 1.182406e-02
sample33 -0.0467252272 3.739254e-03
sample34 0.0783666394 1.203777e-02
sample35 0.0424227097 -1.118559e-02
sample36 -0.1107646166 -1.143464e-02
sample37 -0.0191667664 -2.246060e-02
sample38 0.0155968095 -2.909621e-02
sample39 0.0746847148 7.148218e-03
sample40 -0.0517028178 -2.137267e-02
sample41 0.0234979494 -3.723018e-02
sample42 0.0388797356 -8.557228e-03
sample43 0.0149555568 7.210002e-03
sample44 -0.1150305613 3.461805e-02
sample45 0.0846146236 3.486020e-02
sample46 0.0884426404 -3.246853e-02
sample47 0.0748644971 -8.083045e-03
sample48 -0.0012033198 -9.403647e-03
sample49 -0.0872662737 3.616245e-02
sample50 0.0066941314 -5.284863e-02
sample51 -0.0411777630 3.791830e-02
sample52 -0.0379355780 2.180834e-02
sample53 -0.0851639886 4.751761e-02
sample54 0.0288006248 7.184424e-03
sample55 -0.0164920835 5.919925e-05
sample56 0.0355115616 1.951043e-02
sample57 -0.0141146068 3.492409e-02
sample58 -0.0015636132 9.862883e-03
sample59 -0.0390656483 3.590929e-03
sample60 -0.0139454780 3.963030e-03
sample61 -0.0106410274 1.919705e-02
sample62 0.0236748439 -5.922677e-03
sample63 -0.0846790877 3.839102e-02
sample64 -0.1202581015 2.846469e-02
sample65 0.0050548584 6.328644e-03
sample66 0.0028013072 1.291807e-02
sample67 -0.1231623009 -2.112565e-02
sample68 -0.0437782161 -2.845072e-02
sample69 -0.0501199692 2.053469e-03
sample70 -0.0140278645 -2.027157e-02
sample71 0.0057489505 -4.085977e-02
sample72 0.0511212704 -5.522408e-03
sample73 0.0828141409 7.431582e-03
sample74 -0.0085959456 1.772951e-02
sample75 -0.0312180394 -6.636869e-03
sample76 -0.0519051781 -1.640191e-02
sample77 -0.0925924762 -6.907800e-03
sample78 0.1163971046 2.251122e-02
sample79 -0.0240906926 -4.887766e-02
sample80 -0.0221327065 -6.730703e-03
sample81 -0.0072114968 -2.254399e-02
sample82 0.1204416674 -9.907422e-03
sample83 0.0386739485 -1.171663e-03
sample84 0.0195988488 -1.033806e-02
sample85 -0.0877680171 -1.725057e-03
sample86 0.1023541048 -1.062501e-02
sample87 0.0425213089 -1.356865e-02
sample88 0.0244788514 1.180820e-02
sample89 0.0804276691 2.188588e-02
sample90 -0.0074639871 -6.140721e-03
sample91 -0.1278832404 -6.485140e-02
sample92 -0.0162199697 -5.048358e-02
sample93 0.0769344893 -3.045135e-03
sample94 -0.0104345587 -3.593172e-03
sample95 -0.0260058453 -2.330475e-02
sample96 -0.0025018700 -1.433516e-02
sample97 -0.0492358305 7.774183e-03
sample98 0.0279220220 -3.862141e-03
sample99 0.0813921923 3.487339e-02
sample100 -0.0428797405 7.112807e-03
sample101 0.0032855240 -4.940743e-02
sample102 0.0038439317 2.938008e-03
sample103 0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105 0.0314853405 -4.656633e-03
sample106 0.0726456731 1.192390e-02
sample107 0.0807342975 7.508627e-03
sample108 0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110 0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112 0.0599954082 8.820317e-03
sample113 -0.0195006577 1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131 6.937326e-03
sample121 0.0613899126 2.915307e-02
sample122 0.0218424338 2.241775e-02
sample123 0.0809008460 -1.051759e-02
sample124 -0.0472109313 1.239887e-02
sample125 0.0583180947 2.521167e-03
sample126 -0.0753941872 2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128 0.1000212216 2.908091e-02
sample129 0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877 4.091672e-03
sample132 -0.0566219024 2.179861e-02
sample133 0.0384172955 -1.372840e-02
sample134 -0.1280862736 2.077912e-02
sample135 0.0592633273 6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861 6.729304e-03
sample140 0.0468926306 -1.285498e-02
sample141 -0.0186248693 1.605439e-02
sample142 0.0328031246 9.887746e-03
sample143 0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310 3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147 0.0765025670 -7.714769e-03
sample148 -0.0276016641 2.420589e-02
sample149 0.0027545308 -4.653007e-02
sample150 -0.0792296010 1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152 0.0409796547 -1.907063e-02
sample153 0.0734301757 5.528780e-02
sample154 0.0557740684 -2.487723e-02
sample155 0.0689436560 -6.127635e-03
sample156 0.0212272938 -9.747423e-03
sample157 0.0911931194 6.355708e-03
sample158 0.0220840645 -3.016357e-03
sample159 -0.0513244242 1.304175e-02
sample160 0.0246213576 1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162 0.1078802043 4.337260e-02
sample163 -0.1017965082 5.047171e-02
sample164 0.0119430799 9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180 1.899222e-02
sample167 -0.0872832229 1.516582e-04
sample168 0.0540714512 1.397701e-02
sample169 0.0328432652 7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
[,1] [,2]
sample1 0.0133684846 2.195848e-02
sample2 0.0254157197 -1.058416e-02
sample3 -0.0049551479 -4.840017e-03
sample4 0.0310390570 -1.063929e-02
sample5 0.0046941318 -6.488426e-03
sample6 -0.0107406753 -1.026702e-02
sample7 -0.0225157631 2.624712e-04
sample8 0.0141320952 -9.505821e-03
sample9 0.0029681280 2.078210e-02
sample10 0.0131729174 -2.275042e-03
sample11 -0.0004164298 1.994019e-02
sample12 -0.0095211620 3.759883e-02
sample13 0.0091018604 -7.953956e-03
sample14 -0.0106557524 -9.181659e-03
sample15 -0.0249924121 3.262724e-02
sample16 -0.0156216400 1.375700e-02
sample17 -0.0019382446 1.073994e-03
sample18 -0.0221072481 -8.703592e-03
sample19 0.0146917619 -1.311712e-02
sample20 -0.0160353760 1.826290e-02
sample21 0.0035947899 -9.616341e-03
sample22 -0.0225060762 -2.532589e-03
sample23 0.0310000683 3.033060e-03
sample24 0.0499544372 1.809450e-02
sample25 0.0284442301 -1.932558e-02
sample26 0.0188220043 2.146985e-02
sample27 -0.0257763219 -1.999228e-03
sample28 0.0120888648 1.125834e-02
sample29 -0.0236482520 4.426726e-02
sample30 -0.0385486305 -2.055935e-02
sample31 -0.0181539336 -5.877838e-03
sample32 -0.0302630460 -2.607192e-03
sample33 -0.0319565715 -1.562628e-02
sample34 -0.0197970124 9.906813e-03
sample35 -0.0247412713 -5.434440e-03
sample36 -0.0386259060 -3.190394e-02
sample37 -0.0566199273 -4.192574e-02
sample38 -0.0142060273 2.259644e-02
sample39 0.0053589035 1.076485e-02
sample40 -0.0552546493 -3.819896e-02
sample41 -0.0013089975 9.278818e-05
sample42 0.0137252142 -1.664652e-02
sample43 -0.0151259626 -6.290953e-03
sample44 0.0617391754 -1.442883e-02
sample45 0.0231410886 1.163143e-03
sample46 -0.0148898209 -1.384176e-04
sample47 -0.0187252536 1.221690e-02
sample48 0.0432839432 1.416671e-02
sample49 0.0160818605 -3.588745e-02
sample50 0.0059333545 4.067003e-02
sample51 -0.0142914866 7.776270e-03
sample52 -0.0086339952 7.208917e-03
sample53 -0.0207386980 6.272432e-03
sample54 -0.0039856719 -1.316934e-02
sample55 -0.0056217017 5.692315e-03
sample56 0.0000123292 8.978290e-04
sample57 -0.0095805555 1.324253e-02
sample58 -0.0124160295 -7.326376e-03
sample59 -0.0400195442 -1.349736e-02
sample60 -0.0460063358 2.770091e-02
sample61 -0.0245266456 1.470710e-02
sample62 -0.0366022783 -3.437352e-03
sample63 0.0013742171 3.288796e-02
sample64 -0.0070599859 2.739588e-02
sample65 0.0041201911 1.498268e-02
sample66 0.0143173351 -1.968812e-02
sample67 -0.0467477531 -1.929938e-02
sample68 -0.0306751978 -1.436184e-02
sample69 -0.0125317217 4.130407e-03
sample70 -0.0068071487 8.080857e-03
sample71 0.0169170264 -7.027348e-03
sample72 -0.0346909749 -1.333770e-02
sample73 -0.0280506153 1.493843e-02
sample74 -0.0182611498 3.294697e-03
sample75 -0.0120563964 8.974612e-03
sample76 0.0001437236 -4.253184e-02
sample77 0.0065330299 -5.252886e-02
sample78 0.0288278141 -1.127782e-02
sample79 0.0503961481 -1.023318e-02
sample80 -0.0207693429 3.648391e-02
sample81 0.0163562768 -9.074596e-03
sample82 -0.0084317129 -1.478976e-02
sample83 -0.0474097918 -1.103126e-02
sample84 0.0177181395 -7.191197e-03
sample85 -0.0342718548 -3.082360e-02
sample86 -0.0261671791 -1.089491e-02
sample87 -0.0009486358 -2.411514e-02
sample88 0.0020528931 -2.894615e-02
sample89 -0.0189361111 -2.638639e-03
sample90 -0.0009863658 -2.390075e-02
sample91 -0.0124352695 8.153234e-02
sample92 0.0564264106 -8.909537e-03
sample93 -0.0081461774 1.570851e-02
sample94 -0.0054896581 1.547251e-02
sample95 0.0224073150 -4.374348e-04
sample96 0.0173528924 -3.050441e-03
sample97 0.0067948115 5.008237e-03
sample98 -0.0116030825 1.498764e-02
sample99 0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101 0.0124923691 3.091503e-02
sample102 0.0650835386 -1.367400e-02
sample103 -0.0042741828 7.855985e-03
sample104 0.0250591040 -4.171938e-03
sample105 0.0157516368 -3.121990e-02
sample106 0.0060593853 -5.101693e-03
sample107 -0.0098329626 1.044506e-02
sample108 0.0044269853 4.142036e-03
sample109 0.0572473486 1.517542e-02
sample110 0.0090474827 -5.119868e-03
sample111 0.0444263015 7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113 0.0241047399 6.706740e-03
sample114 0.0074558775 -4.728652e-03
sample115 0.0611851433 1.117210e-02
sample116 0.0432646951 -1.380556e-02
sample117 0.0516750066 -3.575617e-02
sample118 0.0139942100 -3.279138e-03
sample119 0.0291722987 5.587946e-02
sample120 0.0103515853 -1.690016e-03
sample121 -0.0091396331 3.552116e-02
sample122 0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124 0.0283466326 3.127845e-03
sample125 0.0016472378 -2.770692e-02
sample126 -0.0286529417 3.489336e-02
sample127 -0.0010224500 7.483214e-03
sample128 0.0209049296 2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968 4.637390e-03
sample132 0.0198526786 5.723983e-04
sample133 0.0088812957 -9.988115e-03
sample134 -0.0137484514 1.172591e-02
sample135 -0.0220314568 1.347465e-02
sample136 -0.0185173353 5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138 0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762 2.456863e-02
sample141 0.0058369763 -1.420854e-02
sample142 0.0207886071 -1.188764e-02
sample143 0.0092832598 -1.324238e-02
sample144 0.0028442140 3.627979e-03
sample145 0.0199749569 2.862202e-03
sample146 -0.0182236697 1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148 0.0065435868 -1.572917e-02
sample149 0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151 0.0245166984 -6.888241e-03
sample152 0.0107259913 3.314630e-02
sample153 0.0550963965 3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574 2.642246e-03
sample156 -0.0117803505 2.698265e-02
sample157 -0.0096167165 1.433840e-02
sample158 -0.0101754772 9.137620e-03
sample159 0.0120662931 -2.565236e-02
sample160 -0.0132238202 2.916023e-03
sample161 0.0274491966 -1.748284e-02
sample162 0.0012482909 3.152261e-02
sample163 0.0042031315 1.830701e-02
sample164 0.0174896157 -1.175915e-02
sample165 0.0097517662 -6.119019e-03
sample166 0.0190134679 -1.121582e-02
sample167 -0.0044140836 4.665585e-03
sample168 0.0049689168 -1.941822e-02
sample169 -0.0209802098 3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
[,1] [,2]
sample1 -0.0515543627 -0.0305856787
sample2 -0.0144993256 0.0236342950
sample3 -0.0371833108 -0.0140263348
sample4 0.0068945388 -0.0132539692
sample5 0.0215035333 -0.0663338101
sample6 -0.0187055152 0.0088773016
sample7 -0.0061521552 0.0064029054
sample8 -0.0210874459 0.0334652901
sample9 0.0516865043 -0.0291142799
sample10 0.0059440366 -0.0527217447
sample11 0.0393010793 -0.0200624712
sample12 -0.0420837100 0.0131331362
sample13 0.0333252565 0.0818552509
sample14 -0.0190062644 0.0160202175
sample15 -0.0030968049 -0.0189230681
sample16 -0.0004452158 0.0018880102
sample17 -0.0185848615 0.0240170131
sample18 -0.0273093598 0.0230213640
sample19 -0.0217761111 -0.0445894441
sample20 0.0245820821 0.0159812738
sample21 0.0034527644 -0.0400016054
sample22 -0.0340789054 0.0039289109
sample23 -0.0010344929 -0.0310161212
sample24 0.0289468503 0.0760962436
sample25 -0.0119098496 -0.0122798760
sample26 -0.0181001057 0.0517892852
sample27 0.0050465417 -0.0086515844
sample28 0.0057491502 0.0358830107
sample29 -0.0051104246 0.0116605117
sample30 -0.0103085904 0.0039678538
sample31 -0.0319929858 0.0090606113
sample32 -0.0036232521 -0.0328202010
sample33 -0.0534742153 0.0024751837
sample34 -0.0067495749 -0.0111000311
sample35 0.0378745721 0.0465929296
sample36 0.0647886800 0.0359987924
sample37 0.0488441236 0.0492906912
sample38 -0.0251514062 0.0197110110
sample39 -0.0085428066 -0.0105117852
sample40 0.0379324087 0.0440810741
sample41 -0.0044199152 -0.0128820644
sample42 -0.0292553573 -0.0067045265
sample43 -0.0077829155 -0.0510178219
sample44 0.0045122248 0.0479660309
sample45 -0.0074444298 -0.0051116726
sample46 -0.0088025512 0.0196186661
sample47 0.0076696301 0.0215947965
sample48 0.0290108585 -0.0175568376
sample49 -0.0141754858 0.0184717099
sample50 0.0006282201 -0.0233054373
sample51 0.0441995177 -0.0410022921
sample52 0.0715329391 -0.0399499475
sample53 -0.0095954087 -0.0029140909
sample54 0.0048933768 -0.0281884386
sample55 0.0327325487 -0.0532290012
sample56 0.0323068984 -0.0256595538
sample57 0.0806603122 -0.0286748097
sample58 -0.0064792049 -0.0006945349
sample59 0.0088958941 0.0067389649
sample60 0.0874124612 0.0431964341
sample61 0.0577604571 -0.0326112099
sample62 -0.0313318464 0.0224391756
sample63 -0.0233625220 0.0125110562
sample64 -0.0086426068 0.0148770341
sample65 0.0025256193 -0.0404466327
sample66 0.0006014071 -0.0471576264
sample67 0.0706087042 0.0516228406
sample68 0.0082301011 0.0033109509
sample69 -0.0475076743 0.0001452708
sample70 -0.0600773716 0.0089986962
sample71 -0.0096321627 -0.0050761187
sample72 -0.0031773546 -0.0166221542
sample73 -0.0113700517 -0.0191726684
sample74 -0.0014179662 -0.0608101325
sample75 0.0041911740 -0.0399981269
sample76 -0.0055326449 0.0353114263
sample77 -0.0260214459 0.0305731380
sample78 -0.0119267436 0.0632236007
sample79 0.0186017239 0.0027402910
sample80 0.0241047889 -0.0472697181
sample81 -0.0220288317 -0.0079577210
sample82 -0.0180751258 0.0639051029
sample83 -0.0256671713 -0.0125898269
sample84 0.0161392598 -0.0567222449
sample85 0.0139988188 0.0322763454
sample86 -0.0198382995 0.0389225776
sample87 0.0266270281 -0.0032979996
sample88 0.0515677078 0.0117902495
sample89 0.0014022125 -0.0140510488
sample90 -0.0375949749 0.0044004551
sample91 0.0310397965 0.0440610926
sample92 0.0270570567 0.0324380452
sample93 -0.0215009202 0.0063993941
sample94 -0.0415702912 -0.0037692077
sample95 -0.0168416047 0.0010019120
sample96 -0.0285582661 -0.0187991000
sample97 -0.0490843868 -0.0266760748
sample98 -0.0171579033 -0.0112897471
sample99 -0.0271316525 0.0232395583
sample100 -0.0301789816 0.0305498693
sample101 -0.0264371151 0.0170723968
sample102 0.0012767734 -0.0248949597
sample103 0.0055214687 -0.0030040587
sample104 0.0251346074 -0.0165212671
sample105 0.0062424215 -0.0400309901
sample106 0.0069768684 0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679 0.0023637162
sample109 -0.0014762845 0.0165583675
sample110 0.0036971063 0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112 0.0046098120 -0.0048009350
sample113 0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453 0.0135805368
sample116 0.0183575759 0.0665377581
sample117 0.0227640036 -0.0012287760
sample118 0.0015695248 0.0472617382
sample119 0.0190084932 0.0590034062
sample120 -0.0449645755 0.0072755697
sample121 0.0077307184 0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123 0.0016959300 0.0028593594
sample124 -0.0365091615 0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408 0.0019165544
sample127 -0.0494064872 0.0088209044
sample128 -0.0155454766 0.0186819802
sample129 -0.0184340400 0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422 0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133 0.0204029276 -0.0282209049
sample134 0.0175513332 0.0262883962
sample135 0.0029009925 0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323 0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139 0.0073103935 0.0308956174
sample140 0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142 0.0268670676 -0.0239229634
sample143 0.0421049126 -0.0110888235
sample144 0.0017253664 -0.0341766012
sample145 0.0681741320 -0.0073526377
sample146 -0.0239965222 0.0118396767
sample147 -0.0063453522 0.0183130585
sample148 0.0230825251 -0.0379753037
sample149 0.0223298673 0.0188909118
sample150 0.0055709108 0.0174179009
sample151 0.0039177786 -0.0233533275
sample152 0.0134325667 0.0302344591
sample153 0.0511990309 0.0730230140
sample154 0.0006698324 0.0154177486
sample155 0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599 0.0154934573
sample158 0.0201775524 -0.0332982124
sample159 -0.0086909001 0.0073496711
sample160 0.0295437331 -0.0555734536
sample161 0.0332754288 0.0033779619
sample162 0.0121954537 0.0433540412
sample163 -0.0173490933 0.0227219128
sample164 0.0143374783 -0.0453542590
sample165 0.0343612593 -0.0511194536
sample166 -0.0157536004 0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919 0.0060747155
sample169 0.0116231468 -0.0015112800
>
> ## 3.3 Plotting VAF
>
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
>
> # JIVE plotVAF
> plotVAF(jiveRes)
>
>
> #########################
> ## PART 4. Plot Results
>
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
>
> # Scores for common part. JIVE
> plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
>
> # Scores for common part. O2PLS.
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for common part. O2PLS.
> plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common",
+ combined=TRUE,block=NULL,color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
>
> # Scores for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for distinctive part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual",
+ combined=TRUE,block=NULL,color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
> # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block)
> p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for common and distinctive part. DISCO (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Loadings for common part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> # Loadings for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> # Combined plot for loadings from common and distinctive part (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
>
> ## Plot scores and loadings togheter: Common components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> ## Plot scores and loadings togheter: Common components O2PLS
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> ## Plot scores and loadings togheter: Distintive components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
>
>
> proc.time()
user system elapsed
13.84 0.34 14.39
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STATegRa.Rcheck/examples_i386/STATegRa-Ex.timings
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STATegRa.Rcheck/examples_x64/STATegRa-Ex.timings
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