David Gomez-Cabrero
| Snapshot Date: 2020-04-14 16:46:13 -0400 (Tue, 14 Apr 2020) |
| URL: https://git.bioconductor.org/packages/STATegRa |
| Branch: RELEASE_3_10 |
| Last Commit: ffd403f |
| Last Changed Date: 2019-10-29 13:09:05 -0400 (Tue, 29 Oct 2019) |
| Back to Multiple platform build/check report for BioC 3.10 |
|
This page was generated on 2020-04-15 12:23:56 -0400 (Wed, 15 Apr 2020).
| Package 1673/1823 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||
| STATegRa 1.22.0 David Gomez-Cabrero
| malbec1 | Linux (Ubuntu 18.04.4 LTS) / x86_64 | OK | OK | OK | | ||||||
| tokay1 | Windows Server 2012 R2 Standard / x64 | OK | OK | [ OK ] | OK | | ||||||
| merida1 | OS X 10.11.6 El Capitan / x86_64 | OK | OK | OK | OK | |
| Package: STATegRa |
| Version: 1.22.0 |
| Command: C:\Users\biocbuild\bbs-3.10-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=C:\Users\biocbuild\bbs-3.10-bioc\R\library --no-vignettes --timings STATegRa_1.22.0.tar.gz |
| StartedAt: 2020-04-15 06:55:51 -0400 (Wed, 15 Apr 2020) |
| EndedAt: 2020-04-15 07:02:16 -0400 (Wed, 15 Apr 2020) |
| EllapsedTime: 385.2 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: STATegRa.Rcheck |
| Warnings: 0 |
##############################################################################
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###
### Running command:
###
### C:\Users\biocbuild\bbs-3.10-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=C:\Users\biocbuild\bbs-3.10-bioc\R\library --no-vignettes --timings STATegRa_1.22.0.tar.gz
###
##############################################################################
##############################################################################
* using log directory 'C:/Users/biocbuild/bbs-3.10-bioc/meat/STATegRa.Rcheck'
* using R version 3.6.3 (2020-02-29)
* 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 '1.22.0'
* 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 5.22 0.04 5.26
** running examples for arch 'x64' ... OK
Examples with CPU or elapsed time > 5s
user system elapsed
plotRes 6.03 0.04 6.08
plotVAF 5.09 0.14 5.24
* 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-3.10-bioc/meat/STATegRa.Rcheck/00check.log'
for details.
STATegRa.Rcheck/00install.out
##############################################################################
##############################################################################
###
### Running command:
###
### C:\cygwin\bin\curl.exe -O https://malbec1.bioconductor.org/BBS/3.10/bioc/src/contrib/STATegRa_1.22.0.tar.gz && rm -rf STATegRa.buildbin-libdir && mkdir STATegRa.buildbin-libdir && C:\Users\biocbuild\bbs-3.10-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=STATegRa.buildbin-libdir STATegRa_1.22.0.tar.gz && C:\Users\biocbuild\bbs-3.10-bioc\R\bin\R.exe CMD INSTALL STATegRa_1.22.0.zip && rm STATegRa_1.22.0.tar.gz STATegRa_1.22.0.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 3179k 100 3179k 0 0 36.8M 0 --:--:-- --:--:-- --:--:-- 38.8M
install for i386
* installing *source* package 'STATegRa' ...
** using staged installation
** 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 from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
install for x64
* installing *source* package 'STATegRa' ...
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'STATegRa' as STATegRa_1.22.0.zip
* DONE (STATegRa)
* installing to library 'C:/Users/biocbuild/bbs-3.10-bioc/R/library'
package 'STATegRa' successfully unpacked and MD5 sums checked
|
STATegRa.Rcheck/tests_i386/runTests.Rout
R version 3.6.3 (2020-02-29) -- "Holding the Windsock"
Copyright (C) 2020 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 Apr 15 07:00:13 2020
***********************************************
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
3.59 0.15 3.78
|
STATegRa.Rcheck/tests_x64/runTests.Rout
R version 3.6.3 (2020-02-29) -- "Holding the Windsock"
Copyright (C) 2020 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 Apr 15 07:02:11 2020
***********************************************
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
2.87 0.14 3.03
|
|
STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsCLUST.Rout
R version 3.6.3 (2020-02-29) -- "Holding the Windsock"
Copyright (C) 2020 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, colnames, dirname, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
pmin.int, rank, rbind, 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
35.29 1.04 36.32
|
STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsCLUST.Rout
R version 3.6.3 (2020-02-29) -- "Holding the Windsock"
Copyright (C) 2020 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, colnames, dirname, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
pmin.int, rank, rbind, 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
28.62 0.93 29.54
|
|
STATegRa.Rcheck/tests_i386/STATegRa_Example.omicsNPC.Rout
R version 3.6.3 (2020-02-29) -- "Holding the Windsock"
Copyright (C) 2020 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
77.65 0.39 78.01
|
STATegRa.Rcheck/tests_x64/STATegRa_Example.omicsNPC.Rout
R version 3.6.3 (2020-02-29) -- "Holding the Windsock"
Copyright (C) 2020 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
71.78 0.17 71.95
|
|
STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsPCA.Rout
R version 3.6.3 (2020-02-29) -- "Holding the Windsock"
Copyright (C) 2020 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 0.0781574252 -0.0431500404
sample2 -0.1192218294 0.0294090945
sample3 -0.0531412243 -0.0746839868
sample4 0.0292975262 -0.0005957258
sample5 0.0202091772 0.0110464138
sample6 0.1226089084 0.1053466149
sample7 0.1078928001 -0.0322477835
sample8 0.1782895461 0.1449365184
sample9 0.0468698144 -0.0455174403
sample10 -0.0036030457 0.0420112313
sample11 -0.0035566482 -0.0566292877
sample12 0.1006128881 0.0641380232
sample13 -0.1174408152 0.0907488723
sample14 0.0981203257 0.0617737199
sample15 0.0085334199 -0.0087015611
sample16 0.0783148729 0.1581293027
sample17 -0.1483609884 0.0638581964
sample18 -0.0963086306 0.0556638403
sample19 -0.0217244149 -0.0720084531
sample20 -0.0635636485 -0.0779654762
sample21 -0.0201840224 0.1566391774
sample22 0.0218268552 -0.0764106191
sample23 0.0852042108 -0.0032685958
sample24 -0.1287170223 0.1924547712
sample25 -0.0430574105 -0.0456563424
sample26 -0.1453896719 0.0541514002
sample27 -0.0197488995 -0.1185659150
sample28 -0.1025336181 0.0650686570
sample29 0.0706018352 -0.0682990215
sample30 -0.1295627685 -0.0066772655
sample31 0.1147449122 0.1232685133
sample32 -0.0374310979 0.0380175385
sample33 0.0599515826 0.0136864695
sample34 -0.0984200873 0.0375319466
sample35 -0.0543098417 -0.0378108612
sample36 0.1403625375 -0.0343760491
sample37 0.0228941705 -0.0732852107
sample38 -0.0222077401 -0.0962595892
sample39 -0.0941738471 0.0215199887
sample40 0.0643800971 -0.0687876612
sample41 -0.0327638140 -0.1232188175
sample42 -0.0500431851 -0.0292471815
sample43 -0.0184498899 0.0233009935
sample44 0.1487899133 0.1171359585
sample45 -0.1050774013 0.1123203825
sample46 -0.1151195859 -0.1094030016
sample47 -0.0962593797 -0.0288465487
sample48 0.0004837484 -0.0310274077
sample49 0.1135207937 0.1213974355
sample50 -0.0123553256 -0.1740742990
sample51 0.0550529947 0.1258885096
sample52 0.0499121318 0.0728543209
sample53 0.1119773693 0.1588011962
sample54 -0.0360055683 0.0228575282
sample55 0.0210418983 0.0006731082
sample56 -0.0434169156 0.0633125895
sample57 0.0197824752 0.1150712061
sample58 0.0030439864 0.0326096888
sample59 0.0500252998 0.0129414928
sample60 0.0184278635 0.0136080101
sample61 0.0150299426 0.0635023119
sample62 -0.0304764074 -0.0201322514
sample63 0.1102252565 0.1285977230
sample64 0.1552588140 0.0971167637
sample65 -0.0058503045 0.0207115995
sample66 -0.0025605288 0.0424321422
sample67 0.1546634714 -0.0661721904
sample68 0.0536369095 -0.0923686822
sample69 0.0640330270 0.0081982275
sample70 0.0163517594 -0.0663230224
sample71 -0.0102537693 -0.1345919653
sample72 -0.0654196206 -0.0196122773
sample73 -0.1048556253 0.0220935843
sample74 0.0123799417 0.0586113571
sample75 0.0392077854 -0.0209756127
sample76 0.0648953353 -0.0524764611
sample77 0.1172922122 -0.0201186158
sample78 -0.1463067898 0.0708475224
sample79 0.0265211277 -0.1603303263
sample80 0.0279737056 -0.0214207144
sample81 0.0079211460 -0.0738449042
sample82 -0.1544236554 -0.0361468698
sample83 -0.0494211627 -0.0050052864
sample84 -0.0259038448 -0.0346547786
sample85 0.1116484257 -0.0031501518
sample86 -0.1306483168 -0.0377217632
sample87 -0.0554778211 -0.0459749279
sample88 -0.0301623730 0.0382197199
sample89 -0.1016866737 0.0694032049
sample90 0.0086819808 -0.0201319944
sample91 0.1578625178 -0.2097829658
sample92 0.0170936967 -0.1655801713
sample93 -0.0979806887 -0.0121512755
sample94 0.0131484006 -0.0114932203
sample95 0.0315682628 -0.0758856728
sample96 0.0024125592 -0.0470133486
sample97 0.0634545379 0.0270333105
sample98 -0.0359374726 -0.0135489344
sample99 -0.1009163148 0.1124782789
sample100 0.0551753104 0.0246488994
sample101 -0.0080119012 -0.1627366985
sample102 -0.0046444057 0.0095638637
sample103 -0.0472523273 -0.0940393712
sample104 0.0198159555 -0.0591089231
sample105 -0.0400237768 -0.0160910336
sample106 -0.0923808340 0.0369018281
sample107 -0.1019374018 0.0224953617
sample108 -0.0877091660 -0.0128833557
sample109 0.0864824548 -0.0900935898
sample110 -0.1223115494 -0.0096084779
sample111 0.0257354696 -0.0936164340
sample112 -0.0765286631 0.0270346228
sample113 0.0258803359 0.0377499787
sample114 0.0021138821 -0.0882013779
sample115 0.0303460397 -0.0723579355
sample116 0.0780508612 -0.0685062376
sample117 0.0536898267 -0.0911903385
sample118 0.0666651222 -0.0236229858
sample119 0.1021871610 -0.2324934110
sample120 0.0750216562 0.0243380557
sample121 -0.0756936333 0.0942949686
sample122 -0.0259627943 0.0731990093
sample123 -0.1037846318 -0.0369197987
sample124 0.0611208032 0.0421726924
sample125 -0.0738472734 0.0066950471
sample126 0.0972916330 0.0762637455
sample127 0.0824697557 -0.0096637009
sample128 -0.1249407484 0.0929315141
sample129 -0.0734067671 -0.0434365014
sample130 -0.0003502081 -0.0309852443
sample131 0.0930182770 0.0155935687
sample132 0.0736222889 0.0733032329
sample133 -0.0498397975 -0.0462436610
sample134 0.1644873543 0.0720003781
sample135 -0.0752297286 0.0003815510
sample136 0.0227145594 -0.0495507583
sample137 0.0564717237 -0.0288918115
sample138 0.0255988170 -0.0610853844
sample139 0.0621217783 0.0235805523
sample140 -0.0604152691 -0.0435595862
sample141 0.0246743988 0.0532649570
sample142 -0.0409560194 0.0316281875
sample143 -0.0077355177 -0.0476895707
sample144 0.0173240801 -0.0156777539
sample145 0.0485474801 0.1202772015
sample146 0.0419645458 -0.0811283051
sample147 -0.0977308491 -0.0274843003
sample148 0.0368256290 0.0803980195
sample149 -0.0072865823 -0.1532984691
sample150 0.1020825291 0.0624772547
sample151 0.0305399128 -0.0289275614
sample152 -0.0533594772 -0.0638308088
sample153 -0.0891627137 0.1799583545
sample154 -0.0727557591 -0.0834162283
sample155 -0.0880668689 -0.0220821642
sample156 -0.0276561163 -0.0326626259
sample157 -0.1155032207 0.0183615208
sample158 -0.0281507569 -0.0104939729
sample159 0.0663235792 0.0443838537
sample160 -0.0302643906 0.0404264040
sample161 0.0114715675 -0.0591022975
sample162 -0.1337086944 0.1398135545
sample163 0.1330124643 0.1688781729
sample164 -0.0150336032 0.0028418033
sample165 0.0076520310 -0.0164127539
sample166 0.0367794492 0.0630664093
sample167 0.1111988825 0.0030057485
sample168 -0.0672981541 0.0446279864
sample169 -0.0413005024 0.0224392006
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 0.0420516926 0.0867862963
sample2 0.0820827709 -0.0410978369
sample3 -0.0155897027 -0.0195182243
sample4 0.1001336910 -0.0410787064
sample5 0.0153465474 -0.0253259759
sample6 -0.0340329420 -0.0408223169
sample7 -0.0722578827 0.0002332543
sample8 0.0457495000 -0.0370016537
sample9 0.0086250630 0.0820184913
sample10 0.0423597308 -0.0083923490
sample11 -0.0022546771 0.0787766107
sample12 -0.0322107086 0.1479824746
sample13 0.0293886393 -0.0306748805
sample14 -0.0337484821 -0.0367506789
sample15 -0.0815538280 0.1275622808
sample16 -0.0508457221 0.0540604697
sample17 -0.0062598458 0.0041023684
sample18 -0.0705641458 -0.0351047470
sample19 0.0476844187 -0.0509598208
sample20 -0.0522960175 0.0715522141
sample21 0.0119121542 -0.0376093290
sample22 -0.0724390399 -0.0095624766
sample23 0.0992532290 0.0134288408
sample24 0.1595111859 0.0728661237
sample25 0.0920694758 -0.0749757578
sample26 0.0595538899 0.0848965770
sample27 -0.0826481822 -0.0086734995
sample28 0.0384786231 0.0440966672
sample29 -0.0777668704 0.1735308880
sample30 -0.1229471139 -0.0819005039
sample31 -0.0579850386 -0.0238644653
sample32 -0.0970394289 -0.0111425966
sample33 -0.1017588312 -0.0630442193
sample34 -0.0637923729 0.0377941921
sample35 -0.0789983699 -0.0229722868
sample36 -0.1224939532 -0.1274954427
sample37 -0.1798819631 -0.1673426652
sample38 -0.0466300780 0.0888161219
sample39 0.0168687336 0.0421533668
sample40 -0.1756390986 -0.1526641602
sample41 -0.0042366614 0.0004928936
sample42 0.0447850762 -0.0651505153
sample43 -0.0482309070 -0.0253529115
sample44 0.1986710340 -0.0545778748
sample45 0.0741832977 0.0054702888
sample46 -0.0478768334 -0.0007071722
sample47 -0.0608187371 0.0481622906
sample48 0.1381490455 0.0578287268
sample49 0.0530515769 -0.1405533158
sample50 0.0173806305 0.1602389768
sample51 -0.0462565464 0.0303473891
sample52 -0.0280067969 0.0280388431
sample53 -0.0667626666 0.0237702144
sample54 -0.0121834453 -0.0521354300
sample55 -0.0182396041 0.0221328499
sample56 0.0001253288 0.0030907296
sample57 -0.0316679794 0.0530190295
sample58 -0.0393919321 -0.0297798625
sample59 -0.1278291733 -0.0546527467
sample60 -0.1486986075 0.1069157136
sample61 -0.0793124867 0.0569796772
sample62 -0.1172800136 -0.0149198010
sample63 0.0028723009 0.1300519695
sample64 -0.0237367686 0.1073287716
sample65 0.0126534537 0.0589808363
sample66 0.0468193170 -0.0771072923
sample67 -0.1494263656 -0.0769859637
sample68 -0.0977958476 -0.0577350581
sample69 -0.0403087164 0.0156042262
sample70 -0.0221528403 0.0315441106
sample71 0.0546439144 -0.0272396520
sample72 -0.1107487189 -0.0537318925
sample73 -0.0906761467 0.0579966937
sample74 -0.0586557179 0.0121421837
sample75 -0.0390492411 0.0349282980
sample76 0.0022961694 -0.1676558748
sample77 0.0232096077 -0.2067302867
sample78 0.0929752527 -0.0434939909
sample79 0.1619501866 -0.0378114706
sample80 -0.0680364366 0.1424663776
sample81 0.0530786644 -0.0358350983
sample82 -0.0266820746 -0.0577444957
sample83 -0.1517234865 -0.0448553746
sample84 0.0570968215 -0.0273813434
sample85 -0.1086290454 -0.1228118902
sample86 -0.0833858570 -0.0442914617
sample87 -0.0022017380 -0.0943906799
sample88 0.0078222681 -0.1140506595
sample89 -0.0611059486 -0.0094584983
sample90 -0.0022927463 -0.0936253966
sample91 -0.0433582693 0.3205983166
sample92 0.1815340663 -0.0334680826
sample93 -0.0267629775 0.0614429143
sample94 -0.0181876755 0.0605090486
sample95 0.0720378410 -0.0013045859
sample96 0.0559716648 -0.0118791590
sample97 0.0217410617 0.0195414027
sample98 -0.0379176455 0.0588357264
sample99 0.0792423728 -0.0151274215
sample100 -0.0222117148 -0.0023321370
sample101 0.0387234858 0.1224226230
sample102 0.2094613694 -0.0516443439
sample103 -0.0138477864 0.0301052101
sample104 0.0807988755 -0.0162719172
sample105 0.0520493506 -0.1229665354
sample106 0.0192611984 -0.0185238293
sample107 -0.0319017296 0.0405123388
sample108 0.0140691718 0.0163421341
sample109 0.1831933287 0.0613006945
sample110 0.0292790935 -0.0199849181
sample111 0.1423255483 0.0327339868
sample112 -0.0426333710 -0.0029083302
sample113 0.0771903341 0.0268733308
sample114 0.0241644679 -0.0184080425
sample115 0.1959018175 0.0460129999
sample116 0.1394477665 -0.0530806267
sample117 0.1672364072 -0.1386536954
sample118 0.0448344808 -0.0117622077
sample119 0.0910394717 0.2217433269
sample120 0.0331391661 -0.0057274650
sample121 -0.0307577523 0.1392506573
sample122 0.0839778721 -0.0291994820
sample123 -0.0239649292 -0.0642163596
sample124 0.0909149489 0.0130419109
sample125 0.0065350424 -0.1092631849
sample126 -0.0935313601 0.1368284327
sample127 -0.0035387126 0.0292755657
sample128 0.0660293000 0.1018565983
sample129 -0.0693637130 -0.0695421430
sample130 -0.0008492358 -0.0669704296
sample131 -0.0431024561 0.0174065018
sample132 0.0637037981 0.0029374402
sample133 0.0289496145 -0.0390818898
sample134 -0.0446205746 0.0456334609
sample135 -0.0712336704 0.0521635229
sample136 -0.0596268900 0.0197299596
sample137 -0.0793150912 -0.0380627976
sample138 0.0973550257 -0.0454218576
sample139 -0.0539906383 -0.1534327177
sample140 -0.0850825046 0.0955814898
sample141 0.0192680022 -0.0554450200
sample142 0.0672260619 -0.0461321203
sample143 0.0303731494 -0.0519260306
sample144 0.0089365228 0.0145814891
sample145 0.0638765218 0.0122258051
sample146 -0.0585853389 0.0063083652
sample147 -0.0894132710 -0.1124615330
sample148 0.0216363802 -0.0615967290
sample149 0.0515425343 -0.0839903536
sample150 -0.0568285761 -0.0124468765
sample151 0.0789533259 -0.0261831461
sample152 0.0330756057 0.1306443527
sample153 0.1751925204 0.1497731274
sample154 -0.0421421452 -0.0037009950
sample155 -0.0680176540 0.0095711509
sample156 -0.0388909424 0.1057563134
sample157 -0.0314769645 0.0561367529
sample158 -0.0329620101 0.0353947456
sample159 0.0398414619 -0.1007373969
sample160 -0.0424940164 0.0108496295
sample161 0.0888372727 -0.0679700449
sample162 0.0027471618 0.1237843724
sample163 0.0126099717 0.0725434133
sample164 0.0566779392 -0.0458324404
sample165 0.0315336665 -0.0236362457
sample166 0.0612055889 -0.0425233331
sample167 -0.0142729886 0.0179308324
sample168 0.0169501771 -0.0769618004
sample169 -0.0675081106 0.0131505566
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 -0.0012329625 -1.635717e-01
sample2 -0.0724350038 -6.021225e-03
sample3 -0.0188460454 -1.080036e-01
sample4 0.0390145323 3.114375e-04
sample5 0.1774811646 -2.996384e-02
sample6 -0.0451444423 -3.455857e-02
sample7 -0.0226466276 -7.020194e-03
sample8 -0.1033680199 -9.856738e-03
sample9 0.1350011738 8.979097e-02
sample10 0.1259887272 -5.097850e-02
sample11 0.0979788361 7.086533e-02
sample12 -0.0863019097 -8.620317e-02
sample13 -0.1381401110 1.828007e-01
sample14 -0.0615073865 -2.642802e-02
sample15 0.0381598923 -3.101666e-02
sample16 -0.0048776734 1.271871e-03
sample17 -0.0788480953 -1.547551e-02
sample18 -0.0884188784 -3.795486e-02
sample19 0.0703044436 -1.084004e-01
sample20 -0.0025585557 7.975871e-02
sample21 0.0941601679 -4.126736e-02
sample22 -0.0550273446 -7.806748e-02
sample23 0.0679495354 -4.102003e-02
sample24 -0.1310962724 1.649310e-01
sample25 0.0113585311 -4.426862e-02
sample26 -0.1402945892 2.016546e-02
sample27 0.0261561081 1.588389e-03
sample28 -0.0724198703 5.850596e-02
sample29 -0.0330058602 2.060781e-03
sample30 -0.0228752618 -2.015433e-02
sample31 -0.0635067949 -6.670332e-02
sample32 0.0685099613 -4.955274e-02
sample33 -0.0777765251 -1.272079e-01
sample34 0.0157842387 -3.024314e-02
sample35 -0.0529632809 1.500972e-01
sample36 0.0070900698 2.025307e-01
sample37 -0.0442420693 1.802088e-01
sample38 -0.0781511325 -3.676424e-02
sample39 0.0120331865 -3.388840e-02
sample40 -0.0473292161 1.471561e-01
sample41 0.0228189395 -2.673558e-02
sample42 -0.0245360228 -7.960866e-02
sample43 0.1036362792 -8.229577e-02
sample44 -0.1012228702 7.049459e-02
sample45 0.0013732080 -2.450905e-02
sample46 -0.0558510055 2.947342e-03
sample47 -0.0380481214 4.554171e-02
sample48 0.0784342138 4.888983e-02
sample49 -0.0605163925 -1.162351e-02
sample50 0.0530079263 -2.737937e-02
sample51 0.1514646524 5.678347e-02
sample52 0.1860935226 1.246717e-01
sample53 -0.0064177100 -2.700991e-02
sample54 0.0697038338 -2.308388e-02
sample55 0.1633577026 1.366441e-02
sample56 0.1011485103 4.682207e-02
sample57 0.1730374203 1.609603e-01
sample58 -0.0071384720 -1.666955e-02
sample59 -0.0030461738 3.005282e-02
sample60 0.0215835062 2.665877e-01
sample61 0.1510583607 1.002385e-01
sample62 -0.0925534003 -4.845845e-02
sample63 -0.0596311765 -4.137019e-02
sample64 -0.0449225788 -2.600565e-03
sample65 0.0939383772 -4.406908e-02
sample66 0.1063400779 -5.709990e-02
sample67 -0.0201590095 2.361727e-01
sample68 0.0037203151 2.418384e-02
sample69 -0.0645161212 -1.155622e-01
sample70 -0.1013440020 -1.351789e-01
sample71 -0.0016467868 -2.976844e-02
sample72 0.0328892969 -2.835861e-02
sample73 0.0275080008 -5.148187e-02
sample74 0.1341719674 -7.895280e-02
sample75 0.0951575650 -3.943186e-02
sample76 -0.0864721972 3.034990e-02
sample77 -0.1035749557 -2.545354e-02
sample78 -0.1575644098 4.939599e-02
sample79 0.0189137126 4.874678e-02
sample80 0.1384140560 4.262781e-05
sample81 -0.0118846440 -6.357932e-02
sample82 -0.1675308197 3.533910e-02
sample83 -0.0065673472 -7.812613e-02
sample84 0.1486891633 -3.109056e-02
sample85 -0.0532724471 7.417881e-02
sample86 -0.1138477384 -1.918204e-05
sample87 0.0432863968 6.080471e-02
sample88 0.0433450362 1.402491e-01
sample89 0.0331205757 -1.395400e-02
sample90 -0.0607412814 -8.610415e-02
sample91 -0.0566272676 1.303746e-01
sample92 -0.0359582454 1.061604e-01
sample93 -0.0433646375 -4.443635e-02
sample94 -0.0477291301 -1.059574e-01
sample95 -0.0249595746 -3.980526e-02
sample96 0.0035219034 -9.293928e-02
sample97 -0.0066048726 -1.527231e-01
sample98 0.0020366802 -5.579551e-02
sample99 -0.0886616045 -3.728220e-02
sample100 -0.1091259137 -3.560420e-02
sample101 -0.0739726483 -4.318003e-02
sample102 0.0574461249 -2.783908e-02
sample103 0.0142731001 9.705528e-03
sample104 0.0710395236 4.068351e-02
sample105 0.0980831368 -3.452951e-02
sample106 -0.0254259306 3.628986e-02
sample107 -0.0160653456 -9.173394e-02
sample108 -0.0200987653 -2.379692e-02
sample109 -0.0389780582 1.692360e-02
sample110 -0.0326304842 2.988110e-02
sample111 0.0676937621 -6.038212e-02
sample112 0.0167883415 5.336939e-03
sample113 0.0969217060 -2.757600e-02
sample114 -0.0026398347 -9.209159e-02
sample115 -0.0308047240 1.603825e-02
sample116 -0.1240307131 1.273000e-01
sample117 0.0334729137 5.392712e-02
sample118 -0.1037152904 6.252431e-02
sample119 -0.1064176649 1.196202e-01
sample120 -0.0771355063 -1.004932e-01
sample121 -0.0129350755 3.181978e-02
sample122 0.0847492341 -5.568322e-02
sample123 -0.0041336800 7.693168e-03
sample124 -0.0583457945 -8.396386e-02
sample125 0.0634844603 -5.232539e-02
sample126 -0.0662580964 -1.091733e-01
sample127 -0.0865024596 -1.094176e-01
sample128 -0.0627817392 -1.470958e-02
sample129 -0.0336276489 -4.007862e-02
sample130 -0.0293517747 -8.046118e-02
sample131 -0.0469197678 -2.209762e-03
sample132 -0.0241740627 -1.248598e-01
sample133 0.0907303214 1.466700e-02
sample134 -0.0350842089 7.539662e-02
sample135 0.0001333372 9.185363e-03
sample136 -0.0335876085 -9.860277e-02
sample137 -0.0640148942 -7.554473e-02
sample138 0.0060964880 -1.742762e-02
sample139 -0.0592084487 5.614968e-02
sample140 0.0427985883 -1.099554e-02
sample141 0.0618796408 -9.301036e-02
sample142 0.0898554498 3.573421e-02
sample143 0.0817389213 8.880524e-02
sample144 0.0787754787 -3.821392e-02
sample145 0.1085821616 1.569477e-01
sample146 -0.0589557974 -4.373364e-02
sample147 -0.0495330493 7.277174e-03
sample148 0.1161592813 9.079114e-03
sample149 -0.0121579464 7.788371e-02
sample150 -0.0314512556 3.520212e-02
sample151 0.0575382206 -1.945351e-02
sample152 -0.0494542098 7.025536e-02
sample153 -0.0941332651 2.153298e-01
sample154 -0.0335932045 2.078725e-02
sample155 0.0690457614 -2.780412e-02
sample156 0.1039901605 -6.292527e-02
sample157 -0.0408645799 8.065518e-03
sample158 0.1018105290 7.816866e-03
sample159 -0.0281730513 -1.207204e-02
sample160 0.1643052995 2.978109e-03
sample161 0.0374329276 8.524611e-02
sample162 -0.0804535310 8.349760e-02
sample163 -0.0743227935 -1.406220e-02
sample164 0.1208806039 -2.139458e-02
sample165 0.1608115925 2.025192e-02
sample166 -0.0425944600 -2.660711e-02
sample167 -0.0226849479 -4.464283e-02
sample168 -0.0180735573 -7.465977e-04
sample169 0.0190778975 2.645401e-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
15.06 0.51 15.57
|
STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsPCA.Rout
R version 3.6.3 (2020-02-29) -- "Holding the Windsock"
Copyright (C) 2020 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 0.0781574277 0.0431500457
sample2 -0.1192218302 -0.0294090937
sample3 -0.0531412231 0.0746839903
sample4 0.0292975270 0.0005957332
sample5 0.0202091799 -0.0110464009
sample6 0.1226089077 -0.1053466165
sample7 0.1078927998 0.0322477794
sample8 0.1782895445 -0.1449365216
sample9 0.0468698153 0.0455174421
sample10 -0.0036030434 -0.0420112200
sample11 -0.0035566475 0.0566292880
sample12 0.1006128882 -0.0641380309
sample13 -0.1174408196 -0.0907488854
sample14 0.0981203249 -0.0617737228
sample15 0.0085334208 0.0087015574
sample16 0.0783148724 -0.1581293069
sample17 -0.1483609894 -0.0638582004
sample18 -0.0963086318 -0.0556638452
sample19 -0.0217244124 0.0720084652
sample20 -0.0635636494 0.0779654687
sample21 -0.0201840211 -0.1566391685
sample22 0.0218268554 0.0764106166
sample23 0.0852042126 0.0032686046
sample24 -0.1287170259 -0.1924547815
sample25 -0.0430574095 0.0456563509
sample26 -0.1453896738 -0.0541514092
sample27 -0.0197488992 0.1185659136
sample28 -0.1025336198 -0.0650686633
sample29 0.0706018351 0.0682990108
sample30 -0.1295627691 0.0066772632
sample31 0.1147449116 -0.1232685161
sample32 -0.0374310968 -0.0380175357
sample33 0.0599515827 -0.0136864709
sample34 -0.0984200870 -0.0375319478
sample35 -0.0543098444 0.0378108498
sample36 0.1403625345 0.0343760402
sample37 0.0228941669 0.0732851990
sample38 -0.0222077404 0.0962595816
sample39 -0.0941738464 -0.0215199870
sample40 0.0643800939 0.0687876503
sample41 -0.0327638131 0.1232188198
sample42 -0.0500431844 0.0292471872
sample43 -0.0184498878 -0.0233009851
sample44 0.1487899113 -0.1171359583
sample45 -0.1050774011 -0.1123203784
sample46 -0.1151195865 0.1094029967
sample47 -0.0962593808 0.0288465410
sample48 0.0004837494 0.0310274138
sample49 0.1135207925 -0.1213974324
sample50 -0.0123553237 0.1740742989
sample51 0.0550529955 -0.1258885060
sample52 0.0499121324 -0.0728543173
sample53 0.1119773690 -0.1588011991
sample54 -0.0360055673 -0.0228575219
sample55 0.0210419002 -0.0006731007
sample56 -0.0434169150 -0.0633125855
sample57 0.0197824752 -0.1150712055
sample58 0.0030439863 -0.0326096891
sample59 0.0500252988 -0.0129414976
sample60 0.0184278602 -0.0136080289
sample61 0.0150299430 -0.0635023121
sample62 -0.0304764084 0.0201322440
sample63 0.1102252562 -0.1285977291
sample64 0.1552588133 -0.0971167710
sample65 -0.0058503026 -0.0207115936
sample66 -0.0025605269 -0.0424321294
sample67 0.1546634677 0.0661721758
sample68 0.0536369090 0.0923686793
sample69 0.0640330275 -0.0081982287
sample70 0.0163517599 0.0663230202
sample71 -0.0102537685 0.1345919694
sample72 -0.0654196202 0.0196122780
sample73 -0.1048556245 -0.0220935857
sample74 0.0123799441 -0.0586113487
sample75 0.0392077872 0.0209756172
sample76 0.0648953335 0.0524764601
sample77 0.1172922108 0.0201186179
sample78 -0.1463067924 -0.0708475283
sample79 0.0265211282 0.1603303327
sample80 0.0279737076 0.0214207153
sample81 0.0079211470 0.0738449092
sample82 -0.1544236581 0.0361468598
sample83 -0.0494211624 0.0050052849
sample84 -0.0259038423 0.0346547917
sample85 0.1116484234 0.0031501451
sample86 -0.1306483186 0.0377217551
sample87 -0.0554778214 0.0459749310
sample88 -0.0301623745 -0.0382197191
sample89 -0.1016866735 -0.0694032042
sample90 0.0086819810 0.0201319972
sample91 0.1578625167 0.2097829449
sample92 0.0170936959 0.1655801728
sample93 -0.0979806886 0.0121512721
sample94 0.0131484015 0.0114932192
sample95 0.0315682635 0.0758856757
sample96 0.0024125607 0.0470133552
sample97 0.0634545398 -0.0270333046
sample98 -0.0359374719 0.0135489336
sample99 -0.1009163156 -0.1124782787
sample100 0.0551753093 -0.0246489052
sample101 -0.0080119007 0.1627366936
sample102 -0.0046444041 -0.0095638495
sample103 -0.0472523269 0.0940393703
sample104 0.0198159563 0.0591089291
sample105 -0.0400237751 0.0160910468
sample106 -0.0923808349 -0.0369018295
sample107 -0.1019374009 -0.0224953611
sample108 -0.0877091659 0.0128833558
sample109 0.0864824550 0.0900935917
sample110 -0.1223115502 0.0096084768
sample111 0.0257354720 0.0936164448
sample112 -0.0765286632 -0.0270346234
sample113 0.0258803377 -0.0377499698
sample114 0.0021138834 0.0882013830
sample115 0.0303460400 0.0723579390
sample116 0.0780508585 0.0685062320
sample117 0.0536898269 0.0911903489
sample118 0.0666651203 0.0236229791
sample119 0.1021871596 0.2324933961
sample120 0.0750216565 -0.0243380548
sample121 -0.0756936339 -0.0942949760
sample122 -0.0259627925 -0.0731989978
sample123 -0.1037846321 0.0369197996
sample124 0.0611208038 -0.0421726894
sample125 -0.0738472722 -0.0066950373
sample126 0.0972916333 -0.0762637531
sample127 0.0824697560 0.0096636990
sample128 -0.1249407489 -0.0929315175
sample129 -0.0734067673 0.0434365008
sample130 -0.0003502076 0.0309852479
sample131 0.0930182763 -0.0155935738
sample132 0.0736222902 -0.0733032270
sample133 -0.0498397964 0.0462436682
sample134 0.1644873526 -0.0720003868
sample135 -0.0752297288 -0.0003815556
sample136 0.0227145602 0.0495507574
sample137 0.0564717236 0.0288918088
sample138 0.0255988177 0.0610853906
sample139 0.0621217762 -0.0235805555
sample140 -0.0604152684 0.0435595830
sample141 0.0246744005 -0.0532649472
sample142 -0.0409560187 -0.0316281796
sample143 -0.0077355176 0.0476895747
sample144 0.0173240817 0.0156777599
sample145 0.0485474794 -0.1202771997
sample146 0.0419645457 0.0811283009
sample147 -0.0977308503 0.0274842975
sample148 0.0368256301 -0.0803980104
sample149 -0.0072865830 0.1532984699
sample150 0.1020825279 -0.0624772600
sample151 0.0305399141 0.0289275693
sample152 -0.0533594781 0.0638308003
sample153 -0.0891627172 -0.1799583665
sample154 -0.0727557597 0.0834162242
sample155 -0.0880668678 0.0220821667
sample156 -0.0276561140 0.0326626298
sample157 -0.1155032214 -0.0183615262
sample158 -0.0281507557 0.0104939762
sample159 0.0663235788 -0.0443838503
sample160 -0.0302643888 -0.0404263964
sample161 0.0114715672 0.0591023017
sample162 -0.1337086966 -0.1398135661
sample163 0.1330124632 -0.1688781788
sample164 -0.0150336013 -0.0028417917
sample165 0.0076520329 0.0164127644
sample166 0.0367794489 -0.0630664071
sample167 0.1111988827 -0.0030057494
sample168 -0.0672981546 -0.0446279841
sample169 -0.0413005028 -0.0224392034
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 0.0420516904 0.0867862972
sample2 0.0820827725 -0.0410978404
sample3 -0.0155897078 -0.0195182207
sample4 0.1001336886 -0.0410787098
sample5 0.0153465424 -0.0253259762
sample6 -0.0340329395 -0.0408223193
sample7 -0.0722578839 0.0002332575
sample8 0.0457495058 -0.0370016605
sample9 0.0086250627 0.0820184921
sample10 0.0423597284 -0.0083923512
sample11 -0.0022546773 0.0787766123
sample12 -0.0322107012 0.1479824738
sample13 0.0293886488 -0.0306748856
sample14 -0.0337484804 -0.0367506799
sample15 -0.0815538255 0.1275622841
sample16 -0.0508457143 0.0540604662
sample17 -0.0062598413 0.0041023668
sample18 -0.0705641434 -0.0351047461
sample19 0.0476844105 -0.0509598192
sample20 -0.0522960162 0.0715522180
sample21 0.0119121561 -0.0376093338
sample22 -0.0724390436 -0.0095624714
sample23 0.0992532267 0.0134288377
sample24 0.1595112023 0.0728661111
sample25 0.0920694705 -0.0749757591
sample26 0.0595538988 0.0848965731
sample27 -0.0826481876 -0.0086734929
sample28 0.0384786302 0.0440966635
sample29 -0.0777668665 0.1735308926
sample30 -0.1229471165 -0.0819004992
sample31 -0.0579850350 -0.0238644671
sample32 -0.0970394302 -0.0111425939
sample33 -0.1017588339 -0.0630442157
sample34 -0.0637923708 0.0377941935
sample35 -0.0789983678 -0.0229722839
sample36 -0.1224939555 -0.1274954391
sample37 -0.1798819671 -0.1673426581
sample38 -0.0466300777 0.0888161267
sample39 0.0168687353 0.0421533661
sample40 -0.1756391027 -0.1526641532
sample41 -0.0042366671 0.0004928980
sample42 0.0447850719 -0.0651505153
sample43 -0.0482309108 -0.0253529098
sample44 0.1986710398 -0.0545778863
sample45 0.0741833020 0.0054702831
sample46 -0.0478768360 -0.0007071670
sample47 -0.0608187345 0.0481622935
sample48 0.1381490456 0.0578287229
sample49 0.0530515775 -0.1405533218
sample50 0.0173806276 0.1602389820
sample51 -0.0462565429 0.0303473865
sample52 -0.0280067949 0.0280388413
sample53 -0.0667626604 0.0237702115
sample54 -0.0121834481 -0.0521354299
sample55 -0.0182396067 0.0221328507
sample56 0.0001253302 0.0030907276
sample57 -0.0316679739 0.0530190262
sample58 -0.0393919320 -0.0297798621
sample59 -0.1278291742 -0.0546527430
sample60 -0.1486985985 0.1069157165
sample61 -0.0793124837 0.0569796775
sample62 -0.1172800137 -0.0149197962
sample63 0.0028723104 0.1300519652
sample64 -0.0237367607 0.1073287689
sample65 0.0126534536 0.0589808357
sample66 0.0468193126 -0.0771072946
sample67 -0.1494263661 -0.0769859585
sample68 -0.0977958529 -0.0577350521
sample69 -0.0403087166 0.0156042278
sample70 -0.0221528423 0.0315441141
sample71 0.0546439078 -0.0272396494
sample72 -0.1107487226 -0.0537318877
sample73 -0.0906761452 0.0579966968
sample74 -0.0586557198 0.0121421846
sample75 -0.0390492437 0.0349283004
sample76 0.0022961639 -0.1676558737
sample77 0.0232096013 -0.2067302872
sample78 0.0929752589 -0.0434939967
sample79 0.1619501796 -0.0378114715
sample80 -0.0680364357 0.1424663808
sample81 0.0530786593 -0.0358350974
sample82 -0.0266820731 -0.0577444939
sample83 -0.1517234895 -0.0448553687
sample84 0.0570968154 -0.0273813437
sample85 -0.1086290474 -0.1228118873
sample86 -0.0833858570 -0.0442914576
sample87 -0.0022017425 -0.0943906786
sample88 0.0078222676 -0.1140506617
sample89 -0.0611059470 -0.0094584980
sample90 -0.0022927507 -0.0936253955
sample91 -0.0433582631 0.3205983233
sample92 0.1815340617 -0.0334680844
sample93 -0.0267629755 0.0614429160
sample94 -0.0181876750 0.0605090502
sample95 0.0720378377 -0.0013045858
sample96 0.0559716607 -0.0118791588
sample97 0.0217410603 0.0195414020
sample98 -0.0379176451 0.0588357286
sample99 0.0792423781 -0.0151274275
sample100 -0.0222117125 -0.0023321372
sample101 0.0387234845 0.1224226269
sample102 0.2094613662 -0.0516443510
sample103 -0.0138477890 0.0301052136
sample104 0.0807988718 -0.0162719182
sample105 0.0520493431 -0.1229665361
sample106 0.0192612007 -0.0185238312
sample107 -0.0319017285 0.0405123400
sample108 0.0140691722 0.0163421343
sample109 0.1831933282 0.0613006907
sample110 0.0292790942 -0.0199849188
sample111 0.1423255431 0.0327339854
sample112 -0.0426333700 -0.0029083294
sample113 0.0771903337 0.0268733273
sample114 0.0241644621 -0.0184080399
sample115 0.1959018172 0.0460129952
sample116 0.1394477671 -0.0530806307
sample117 0.1672363991 -0.1386536986
sample118 0.0448344828 -0.0117622093
sample119 0.0910394747 0.2217433299
sample120 0.0331391663 -0.0057274665
sample121 -0.0307577428 0.1392506553
sample122 0.0839778710 -0.0291994866
sample123 -0.0239649322 -0.0642163574
sample124 0.0909149504 0.0130419067
sample125 0.0065350367 -0.1092631847
sample126 -0.0935313535 0.1368284339
sample127 -0.0035387125 0.0292755664
sample128 0.0660293085 0.1018565934
sample129 -0.0693637169 -0.0695421389
sample130 -0.0008492402 -0.0669704282
sample131 -0.0431024542 0.0174065024
sample132 0.0637037988 0.0029374363
sample133 0.0289496099 -0.0390818891
sample134 -0.0446205685 0.0456334592
sample135 -0.0712336682 0.0521635254
sample136 -0.0596268925 0.0197299637
sample137 -0.0793150939 -0.0380627938
sample138 0.0973550212 -0.0454218589
sample139 -0.0539906404 -0.1534327172
sample140 -0.0850825040 0.0955814944
sample141 0.0192679991 -0.0554450216
sample142 0.0672260604 -0.0461321235
sample143 0.0303731458 -0.0519260305
sample144 0.0089365202 0.0145814897
sample145 0.0638765274 0.0122257982
sample146 -0.0585853415 0.0063083698
sample147 -0.0894132743 -0.1124615290
sample148 0.0216363787 -0.0615967322
sample149 0.0515425274 -0.0839903511
sample150 -0.0568285730 -0.0124468771
sample151 0.0789533222 -0.0261831477
sample152 0.0330756104 0.1306443531
sample153 0.1751925391 0.1497731143
sample154 -0.0421421472 -0.0037009910
sample155 -0.0680176563 0.0095711544
sample156 -0.0388909435 0.1057563165
sample157 -0.0314769604 0.0561367536
sample158 -0.0329620113 0.0353947473
sample159 0.0398414604 -0.1007373998
sample160 -0.0424940179 0.0108496301
sample161 0.0888372689 -0.0679700466
sample162 0.0027471752 0.1237843675
sample163 0.0126099816 0.0725434071
sample164 0.0566779347 -0.0458324420
sample165 0.0315336619 -0.0236362461
sample166 0.0612055901 -0.0425233372
sample167 -0.0142729886 0.0179308328
sample168 0.0169501768 -0.0769618023
sample169 -0.0675081091 0.0131505582
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 0.0012329599 1.635717e-01
sample2 0.0724349985 6.021232e-03
sample3 0.0188460458 1.080036e-01
sample4 -0.0390145342 -3.114369e-04
sample5 -0.1774811664 2.996383e-02
sample6 0.0451444372 3.455857e-02
sample7 0.0226466323 7.020191e-03
sample8 0.1033680123 9.856746e-03
sample9 -0.1350011676 -8.979098e-02
sample10 -0.1259887323 5.097850e-02
sample11 -0.0979788303 -7.086533e-02
sample12 0.0863019054 8.620317e-02
sample13 0.1381401096 -1.828007e-01
sample14 0.0615073839 2.642803e-02
sample15 -0.0381598907 3.101666e-02
sample16 0.0048776664 -1.271867e-03
sample17 0.0788480893 1.547552e-02
sample18 0.0884188741 3.795487e-02
sample19 -0.0703044444 1.084004e-01
sample20 0.0025585634 -7.975872e-02
sample21 -0.0941601790 4.126736e-02
sample22 0.0550273483 7.806747e-02
sample23 -0.0679495376 4.102003e-02
sample24 0.1310962617 -1.649310e-01
sample25 -0.0113585327 4.426862e-02
sample26 0.1402945837 -2.016546e-02
sample27 -0.0261560996 -1.588396e-03
sample28 0.0724198664 -5.850595e-02
sample29 0.0330058673 -2.060786e-03
sample30 0.0228752624 2.015433e-02
sample31 0.0635067882 6.670333e-02
sample32 -0.0685099636 4.955274e-02
sample33 0.0777765229 1.272079e-01
sample34 -0.0157842417 3.024314e-02
sample35 0.0529632891 -1.500972e-01
sample36 -0.0070900568 -2.025307e-01
sample37 0.0442420834 -1.802088e-01
sample38 0.0781511378 3.676424e-02
sample39 -0.0120331904 3.388840e-02
sample40 0.0473292293 -1.471561e-01
sample41 -0.0228189336 2.673558e-02
sample42 0.0245360201 7.960866e-02
sample43 -0.1036362826 8.229577e-02
sample44 0.1012228631 -7.049458e-02
sample45 -0.0013732184 2.450906e-02
sample46 0.0558510115 -2.947344e-03
sample47 0.0380481248 -4.554171e-02
sample48 -0.0784342133 -4.888983e-02
sample49 0.0605163850 1.162351e-02
sample50 -0.0530079172 2.737936e-02
sample51 -0.1514646560 -5.678348e-02
sample52 -0.1860935214 -1.246717e-01
sample53 0.0064177028 2.700991e-02
sample54 -0.0697038362 2.308388e-02
sample55 -0.1633577014 -1.366442e-02
sample56 -0.1011485130 -4.682207e-02
sample57 -0.1730374206 -1.609603e-01
sample58 0.0071384704 1.666955e-02
sample59 0.0030461774 -3.005282e-02
sample60 -0.0215834944 -2.665877e-01
sample61 -0.1510583590 -1.002385e-01
sample62 0.0925534020 4.845845e-02
sample63 0.0596311692 4.137020e-02
sample64 0.0449225758 2.600568e-03
sample65 -0.0939383801 4.406908e-02
sample66 -0.1063400835 5.709990e-02
sample67 0.0201590263 -2.361727e-01
sample68 -0.0037203062 -2.418384e-02
sample69 0.0645161186 1.155622e-01
sample70 0.1013440022 1.351789e-01
sample71 0.0016467923 2.976844e-02
sample72 -0.0328892953 2.835860e-02
sample73 -0.0275080031 5.148187e-02
sample74 -0.1341719720 7.895279e-02
sample75 -0.0951575636 3.943185e-02
sample76 0.0864722017 -3.034990e-02
sample77 0.1035749566 2.545354e-02
sample78 0.1575644033 -4.939598e-02
sample79 -0.0189137050 -4.874679e-02
sample80 -0.1384140525 -4.263596e-05
sample81 0.0118846451 6.357932e-02
sample82 0.1675308214 -3.533910e-02
sample83 0.0065673475 7.812613e-02
sample84 -0.1486891639 3.109056e-02
sample85 0.0532724533 -7.417881e-02
sample86 0.1138477407 1.918455e-05
sample87 -0.0432863930 -6.080472e-02
sample88 -0.0433450346 -1.402491e-01
sample89 -0.0331205802 1.395400e-02
sample90 0.0607412796 8.610415e-02
sample91 0.0566272879 -1.303746e-01
sample92 0.0359582548 -1.061604e-01
sample93 0.0433646362 4.443636e-02
sample94 0.0477291279 1.059574e-01
sample95 0.0249595765 3.980526e-02
sample96 -0.0035219050 9.293928e-02
sample97 0.0066048664 1.527231e-01
sample98 -0.0020366808 5.579551e-02
sample99 0.0886615936 3.728221e-02
sample100 0.1091259123 3.560420e-02
sample101 0.0739726556 4.318002e-02
sample102 -0.0574461311 2.783908e-02
sample103 -0.0142730946 -9.705532e-03
sample104 -0.0710395203 -4.068351e-02
sample105 -0.0980831389 3.452951e-02
sample106 0.0254259279 -3.628985e-02
sample107 0.0160653407 9.173394e-02
sample108 0.0200987639 2.379692e-02
sample109 0.0389780613 -1.692360e-02
sample110 0.0326304835 -2.988110e-02
sample111 -0.0676937614 6.038211e-02
sample112 -0.0167883429 -5.336938e-03
sample113 -0.0969217105 2.757600e-02
sample114 0.0026398363 9.209158e-02
sample115 0.0308047250 -1.603825e-02
sample116 0.1240307191 -1.273000e-01
sample117 -0.0334729099 -5.392712e-02
sample118 0.1037152938 -6.252430e-02
sample119 0.1064176822 -1.196202e-01
sample120 0.0771355018 1.004932e-01
sample121 0.0129350711 -3.181978e-02
sample122 -0.0847492425 5.568322e-02
sample123 0.0041336814 -7.693168e-03
sample124 0.0583457881 8.396386e-02
sample125 -0.0634844638 5.232539e-02
sample126 0.0662580919 1.091733e-01
sample127 0.0865024577 1.094176e-01
sample128 0.0627817305 1.470959e-02
sample129 0.0336276505 4.007861e-02
sample130 0.0293517737 8.046118e-02
sample131 0.0469197691 2.209762e-03
sample132 0.0241740539 1.248598e-01
sample133 -0.0907303196 -1.466700e-02
sample134 0.0350842104 -7.539662e-02
sample135 -0.0001333362 -9.185364e-03
sample136 0.0335876097 9.860277e-02
sample137 0.0640148958 7.554473e-02
sample138 -0.0060964869 1.742762e-02
sample139 0.0592084509 -5.614968e-02
sample140 -0.0427985848 1.099554e-02
sample141 -0.0618796472 9.301036e-02
sample142 -0.0898554526 -3.573421e-02
sample143 -0.0817389165 -8.880524e-02
sample144 -0.0787754790 3.821391e-02
sample145 -0.1085821642 -1.569477e-01
sample146 0.0589558025 4.373364e-02
sample147 0.0495330516 -7.277174e-03
sample148 -0.1161592858 -9.079114e-03
sample149 0.0121579565 -7.788371e-02
sample150 0.0314512557 -3.520212e-02
sample151 -0.0575382209 1.945351e-02
sample152 0.0494542148 -7.025536e-02
sample153 0.0941332571 -2.153298e-01
sample154 0.0335932100 -2.078725e-02
sample155 -0.0690457607 2.780411e-02
sample156 -0.1039901599 6.292526e-02
sample157 0.0408645784 -8.065515e-03
sample158 -0.1018105277 -7.816870e-03
sample159 0.0281730480 1.207204e-02
sample160 -0.1643053012 -2.978113e-03
sample161 -0.0374329232 -8.524611e-02
sample162 0.0804535241 -8.349759e-02
sample163 0.0743227848 1.406221e-02
sample164 -0.1208806063 2.139458e-02
sample165 -0.1608115915 -2.025193e-02
sample166 0.0425944544 2.660711e-02
sample167 0.0226849480 4.464282e-02
sample168 0.0180735532 7.466018e-04
sample169 -0.0190778969 -2.645401e-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
12.42 0.35 12.76
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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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