David Gomez-Cabrero
| Snapshot Date: 2018-04-11 16:45:18 -0400 (Wed, 11 Apr 2018) |
| URL: https://git.bioconductor.org/packages/STATegRa |
| Branch: RELEASE_3_6 |
| Last Commit: 393cfc1 |
| Last Changed Date: 2017-10-30 12:40:43 -0400 (Mon, 30 Oct 2017) |
| Back to Multiple platform build/check report for BioC 3.6 |
|
This page was generated on 2018-04-12 13:25:14 -0400 (Thu, 12 Apr 2018).
| Package 1358/1472 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||
| STATegRa 1.12.0 David Gomez-Cabrero
| malbec1 | Linux (Ubuntu 16.04.1 LTS) / x86_64 | NotNeeded | OK | OK | | ||||||
| tokay1 | Windows Server 2012 R2 Standard / x64 | NotNeeded | OK | [ OK ] | OK | | ||||||
| veracruz1 | OS X 10.11.6 El Capitan / x86_64 | NotNeeded | OK | OK | OK | |
| Package: STATegRa |
| Version: 1.12.0 |
| Command: rm -rf STATegRa.buildbin-libdir STATegRa.Rcheck && mkdir STATegRa.buildbin-libdir STATegRa.Rcheck && C:\Users\biocbuild\bbs-3.6-bioc\R\bin\R.exe CMD INSTALL --build --merge-multiarch --library=STATegRa.buildbin-libdir STATegRa_1.12.0.tar.gz >STATegRa.Rcheck\00install.out 2>&1 && cp STATegRa.Rcheck\00install.out STATegRa-install.out && C:\Users\biocbuild\bbs-3.6-bioc\R\bin\R.exe CMD check --library=STATegRa.buildbin-libdir --install="check:STATegRa-install.out" --force-multiarch --no-vignettes --timings STATegRa_1.12.0.tar.gz |
| StartedAt: 2018-04-12 03:27:36 -0400 (Thu, 12 Apr 2018) |
| EndedAt: 2018-04-12 03:34:23 -0400 (Thu, 12 Apr 2018) |
| EllapsedTime: 406.7 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: STATegRa.Rcheck |
| Warnings: 0 |
##############################################################################
##############################################################################
###
### Running command:
###
### rm -rf STATegRa.buildbin-libdir STATegRa.Rcheck && mkdir STATegRa.buildbin-libdir STATegRa.Rcheck && C:\Users\biocbuild\bbs-3.6-bioc\R\bin\R.exe CMD INSTALL --build --merge-multiarch --library=STATegRa.buildbin-libdir STATegRa_1.12.0.tar.gz >STATegRa.Rcheck\00install.out 2>&1 && cp STATegRa.Rcheck\00install.out STATegRa-install.out && C:\Users\biocbuild\bbs-3.6-bioc\R\bin\R.exe CMD check --library=STATegRa.buildbin-libdir --install="check:STATegRa-install.out" --force-multiarch --no-vignettes --timings STATegRa_1.12.0.tar.gz
###
##############################################################################
##############################################################################
* using log directory 'C:/Users/biocbuild/bbs-3.6-bioc/meat/STATegRa.Rcheck'
* using R version 3.4.4 (2018-03-15)
* 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 '1.12.0'
* 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 ... NOTE
installed size is 5.1Mb
sub-directories of 1Mb or more:
data 2.4Mb
doc 2.1Mb
* 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
** checking loading without being on the library search path ... 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 loading without being on the library search path ... 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
biplotRes,caClass-character-numeric-character: no visible binding for
global variable 'values.1'
biplotRes,caClass-character-numeric-character: no visible binding for
global variable 'values.2'
biplotRes,caClass-character-numeric-character: no visible binding for
global variable 'color'
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,matrix-matrix-numeric: no visible binding for global
variable 'comps'
selectCommonComps,matrix-matrix-numeric: no visible binding for global
variable 'block'
selectCommonComps,matrix-matrix-numeric: no visible binding for global
variable 'comp'
selectCommonComps,matrix-matrix-numeric: no visible binding for global
variable 'ratio'
Undefined global functions or variables:
VAF block color comp comps ratio values.1 values.2
* 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 installed files from 'inst/doc' ... OK
* checking files in 'vignettes' ... OK
* checking examples ...
** running examples for arch 'i386' ... OK
Examples with CPU or elapsed time > 5s
user system elapsed
biplotRes 5.86 0.12 5.99
plotRes 5.68 0.07 5.75
plotVAF 5.10 0.04 7.36
** running examples for arch 'x64' ... OK
Examples with CPU or elapsed time > 5s
user system elapsed
biplotRes 7.39 0.15 7.55
plotRes 5.63 0.09 5.73
plotVAF 5.17 0.11 5.28
* 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: 2 NOTEs
See
'C:/Users/biocbuild/bbs-3.6-bioc/meat/STATegRa.Rcheck/00check.log'
for details.
STATegRa.Rcheck/00install.out
install for i386
* installing *source* package 'STATegRa' ...
** R
** data
** inst
** preparing package for lazy loading
** help
*** installing help indices
converting help for package 'STATegRa'
finding HTML links ... done
PCA.selection html
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
biplotRes html
caClass-class html
combiningMappings html
createOmicsExpressionSet html
getInitialData html
getLoadings html
getMethodInfo html
getPreprocessing html
getScores html
getVAF html
holistOmics html
modelSelection html
omicsCompAnalysis html
omicsNPC html
plotRes html
plotVAF html
selectCommonComps html
finding level-2 HTML links ... done
** building package indices
** installing vignettes
** testing if installed package can be loaded
In R CMD INSTALL
install for x64
* installing *source* package 'STATegRa' ...
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'STATegRa' as STATegRa_1.12.0.zip
* DONE (STATegRa)
In R CMD INSTALL
In R CMD INSTALL
|
STATegRa.Rcheck/tests_i386/runTests.Rout
R version 3.4.4 (2018-03-15) -- "Someone to Lean On"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> BiocGenerics:::testPackage("STATegRa")
RUNIT TEST PROTOCOL -- Thu Apr 12 03:32:07 2018
***********************************************
Number of test functions: 9
Number of errors: 0
Number of failures: 0
1 Test Suite :
STATegRa RUnit Tests - 9 test functions, 0 errors, 0 failures
Number of test functions: 9
Number of errors: 0
Number of failures: 0
Warning messages:
1: In rownames(pData) == colnames(exprs) :
longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
>
> proc.time()
user system elapsed
4.48 0.25 4.79
|
STATegRa.Rcheck/tests_x64/runTests.Rout
R version 3.4.4 (2018-03-15) -- "Someone to Lean On"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> BiocGenerics:::testPackage("STATegRa")
RUNIT TEST PROTOCOL -- Thu Apr 12 03:34:18 2018
***********************************************
Number of test functions: 9
Number of errors: 0
Number of failures: 0
1 Test Suite :
STATegRa RUnit Tests - 9 test functions, 0 errors, 0 failures
Number of test functions: 9
Number of errors: 0
Number of failures: 0
Warning messages:
1: In rownames(pData) == colnames(exprs) :
longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
>
> proc.time()
user system elapsed
4.54 0.28 4.92
|
|
STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsCLUST.Rout
R version 3.4.4 (2018-03-15) -- "Someone to Lean On"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> ###########################################
> ########### EXAMPLE OF THE OMICSCLUSTERING
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
>
> #############################################
> ## PART 1: CREATING a bioMap CLASS
> #############################################
> ####### This part creates or reads the map between features.
> ####### In the present example the map is downloaded from a resource.
> ####### then the class is created.
>
> #load("../data/STATegRa_S2.rda")
> data(STATegRa_S2)
>
> MAP.SYMBOL<-bioMap(name = "Symbol-miRNA",
+ metadata = list(type_v1="Gene",type_v2="miRNA",
+ source_database="targetscan.Hs.eg.db",
+ data_extraction="July2014"),
+ map=mapdata)
>
>
> #############################################
> ## PART 2: CREATING a bioDist CLASS
> #############################################
> ##### In the second part given a set of main features and surrogate feautres,
> ##### the profile of the main features is computed through the surrogate features.
>
> # Load Data
> data(STATegRa_S1)
> #load("../data/STATegRa.S1.Rdata")
>
> ## Create ExpressionSets
> # source("../R/STATegRa_omicsPCA_classes_and_methods.R")
> # Block1 - Expression data
> mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname"))
>
> # Create Gene-gene distance computed through miRNA data
> bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1),
+ reference = "Var1",
+ mapping = MAP.SYMBOL,
+ surrogateData = miRNA.ds, ### miRNA data
+ referenceData = mRNA.ds, ### mRNA data
+ maxitems=2,
+ selectionRule="sd",
+ expfac=NULL,
+ aggregation = "sum",
+ distance = "spearman",
+ noMappingDist = 0,
+ filtering = NULL,
+ name = "mRNAbymiRNA")
>
> require(Biobase)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colMeans, colSums, colnames, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
setdiff, sort, table, tapply, union, unique, unsplit, which,
which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
>
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+ distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+ map.name = "id",
+ map.metadata = list(),
+ params = list())
>
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
>
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
>
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+ bioDistList = bioDistList,
+ weights=weights)
> length(bioDistWList)
[1] 4
>
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
>
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+ listDistW = bioDistWList,
+ method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
>
> #############################################
> ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
> #############################################
>
> ## IDH1
>
> IDH1.F<-bioDistFeature(Feature = "IDH1" ,
+ listDistW = bioDistWList,
+ threshold.cor=0.7)
> bioDistFeaturePlot(data=IDH1.F)
>
> ## PDGFRA
>
> #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.7)
> #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
>
> ## EGFR
> #EGFR.F<-bioDistFeature(Feature = "EGFR" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.7)
> #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
>
> ## MGMT
> #MGMT.F<-bioDistFeature(Feature = "MGMT" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.5)
> #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
>
>
>
>
>
> proc.time()
user system elapsed
28.82 0.78 29.68
|
STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsCLUST.Rout
R version 3.4.4 (2018-03-15) -- "Someone to Lean On"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> ###########################################
> ########### EXAMPLE OF THE OMICSCLUSTERING
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
>
> #############################################
> ## PART 1: CREATING a bioMap CLASS
> #############################################
> ####### This part creates or reads the map between features.
> ####### In the present example the map is downloaded from a resource.
> ####### then the class is created.
>
> #load("../data/STATegRa_S2.rda")
> data(STATegRa_S2)
>
> MAP.SYMBOL<-bioMap(name = "Symbol-miRNA",
+ metadata = list(type_v1="Gene",type_v2="miRNA",
+ source_database="targetscan.Hs.eg.db",
+ data_extraction="July2014"),
+ map=mapdata)
>
>
> #############################################
> ## PART 2: CREATING a bioDist CLASS
> #############################################
> ##### In the second part given a set of main features and surrogate feautres,
> ##### the profile of the main features is computed through the surrogate features.
>
> # Load Data
> data(STATegRa_S1)
> #load("../data/STATegRa.S1.Rdata")
>
> ## Create ExpressionSets
> # source("../R/STATegRa_omicsPCA_classes_and_methods.R")
> # Block1 - Expression data
> mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname"))
>
> # Create Gene-gene distance computed through miRNA data
> bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1),
+ reference = "Var1",
+ mapping = MAP.SYMBOL,
+ surrogateData = miRNA.ds, ### miRNA data
+ referenceData = mRNA.ds, ### mRNA data
+ maxitems=2,
+ selectionRule="sd",
+ expfac=NULL,
+ aggregation = "sum",
+ distance = "spearman",
+ noMappingDist = 0,
+ filtering = NULL,
+ name = "mRNAbymiRNA")
>
> require(Biobase)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colMeans, colSums, colnames, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
setdiff, sort, table, tapply, union, unique, unsplit, which,
which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
>
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+ distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+ map.name = "id",
+ map.metadata = list(),
+ params = list())
>
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
>
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
>
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+ bioDistList = bioDistList,
+ weights=weights)
> length(bioDistWList)
[1] 4
>
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
>
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+ listDistW = bioDistWList,
+ method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
4: In plot.window(...) :
relative range of values = 18 * EPS, is small (axis 2)
5: In plot.window(...) :
relative range of values = 18 * EPS, is small (axis 2)
6: In plot.window(...) :
relative range of values = 18 * EPS, is small (axis 2)
7: In plot.window(...) :
relative range of values = 18 * 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
26.20 1.10 27.42
|
|
STATegRa.Rcheck/tests_i386/STATegRa_Example.omicsNPC.Rout
R version 3.4.4 (2018-03-15) -- "Someone to Lean On"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> rm(list = ls())
> require("STATegRa")
Loading required package: STATegRa
> # Load the data
> data("TCGA_BRCA_Batch_93")
> # Setting dataTypes
> dataTypes <- c("count", "count", "continuous")
> # Setting methods to combine pvalues
> combMethods = c("Fisher", "Liptak", "Tippett")
> # Setting number of permutations
> numPerms = 1000
> # Setting number of cores
> numCores = 1
> # Setting holistOmics to print out the steps that it performs.
> verbose = TRUE
> # Run holistOmics analysis.
> output <- omicsNPC(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes, combMethods = combMethods, numPerms = numPerms, numCores = numCores, verbose = verbose)
Compute initial statistics on data
Building NULL distributions by permuting data
Compute pseudo p-values based on NULL distributions...
NPC p-values calculation...
>
> proc.time()
user system elapsed
64.17 0.07 64.39
|
STATegRa.Rcheck/tests_x64/STATegRa_Example.omicsNPC.Rout
R version 3.4.4 (2018-03-15) -- "Someone to Lean On"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> rm(list = ls())
> require("STATegRa")
Loading required package: STATegRa
> # Load the data
> data("TCGA_BRCA_Batch_93")
> # Setting dataTypes
> dataTypes <- c("count", "count", "continuous")
> # Setting methods to combine pvalues
> combMethods = c("Fisher", "Liptak", "Tippett")
> # Setting number of permutations
> numPerms = 1000
> # Setting number of cores
> numCores = 1
> # Setting holistOmics to print out the steps that it performs.
> verbose = TRUE
> # Run holistOmics analysis.
> output <- omicsNPC(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes, combMethods = combMethods, numPerms = numPerms, numCores = numCores, verbose = verbose)
Compute initial statistics on data
Building NULL distributions by permuting data
Compute pseudo p-values based on NULL distributions...
NPC p-values calculation...
>
> proc.time()
user system elapsed
76.85 0.18 77.17
|
|
STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsPCA.Rout
R version 3.4.4 (2018-03-15) -- "Someone to Lean On"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> ###########################################
> ########### EXAMPLE OF THE OMICSPCA
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
>
> # g_legend (not exported by STATegRa any more)
> ## code from https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs
> g_legend<-function(a.gplot){
+ tmp <- ggplot_gtable(ggplot_build(a.gplot))
+ leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
+ legend <- tmp$grobs[[leg]]
+ return(legend)}
>
> #########################
> ## PART 1. Load data
>
> ## Load data
> data(STATegRa_S3)
>
> ls()
[1] "Block1.PCA" "Block2.PCA" "ed.PCA" "g_legend"
>
> ## Create ExpressionSets
> # Block1 - Expression data
> B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname"))
>
> #########################
> ## PART 2. Model Selection
>
> require(grid)
Loading required package: grid
> require(gridExtra)
Loading required package: gridExtra
> require(ggplot2)
Loading required package: ggplot2
>
> ## 2.1 Select common components
> cc <- selectCommonComps(X=Block1.PCA,Y=Block2.PCA,Rmax=3)
> cc$common
[1] 2
> cc$pssq
> cc$pratios
> #png("modelSelection.png",width=822,height=416)
> grid.arrange(cc$pssq,cc$pratios,ncol=2)
> #dev.off()
>
> ## 2.2 Select distinctive components
> # Block 1
> PCA.selection(Data=Block1.PCA,fac.sel="single%",varthreshold=0.03)$numComps
[1] 4
> # Block2
> PCA.selection(Data=Block2.PCA,fac.sel="single%",varthreshold=0.03)$numComps
[1] 4
>
> ## 2.3 Optimal components analysis
> ms <- modelSelection(Input=list(B1,B2),Rmax=4,fac.sel="single%",varthreshold=0.03)
> ms
$common
[1] 2
$dist
[1] 2 2
>
> #########################
> ## PART 3. Component Analysis
>
> ## 3.1 Component analysis of the three methods
> discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=ms$common,Rspecific=ms$dist,center=TRUE,
+ scale=TRUE,weight=TRUE)
> jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=ms$common,Rspecific=ms$dist,center=TRUE,
+ scale=TRUE,weight=TRUE)
> o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=ms$common,Rspecific=ms$dist,center=TRUE,
+ scale=TRUE,weight=TRUE)
>
> ## 3.2 Exploring scores structures
>
> # Exploring DISCO-SCA scores structure
> discoRes@scores$common ## Common scores
1 2
sample1 -0.0781574364 0.0431503507
sample2 0.1192218502 -0.0294086595
sample3 0.0531412010 0.0746839750
sample4 -0.0292975057 0.0005962210
sample5 -0.0202091755 -0.0110463361
sample6 -0.1226089033 -0.1053467897
sample7 -0.1078928204 0.0322474063
sample8 -0.1782895165 -0.1449363062
sample9 -0.0468698114 0.0455174315
sample10 0.0036030559 -0.0420109951
sample11 0.0035566458 0.0566292434
sample12 -0.1006128922 -0.0641381226
sample13 0.1174408503 -0.0907488078
sample14 -0.0981203261 -0.0617738927
sample15 -0.0085334403 0.0087011822
sample16 -0.0783148604 -0.1581295638
sample17 0.1483609951 -0.0638581927
sample18 0.0963086207 -0.0556641637
sample19 0.0217244048 0.0720087481
sample20 0.0635636335 0.0779651860
sample21 0.0201840436 -0.1566391020
sample22 -0.0218268879 0.0764102944
sample23 -0.0852041942 0.0032691094
sample24 0.1287170980 -0.1924540174
sample25 0.0430574189 0.0456568286
sample26 0.1453896945 -0.0541510662
sample27 0.0197488653 0.1185654942
sample28 0.1025336409 -0.0650684689
sample29 -0.0706018603 0.0682986456
sample30 0.1295627399 0.0066766592
sample31 -0.1147449122 -0.1232687864
sample32 0.0374310788 -0.0380180034
sample33 -0.0599516128 -0.0136869279
sample34 0.0984200770 -0.0375322338
sample35 0.0543098327 0.0378103812
sample36 -0.1403625512 0.0343752694
sample37 -0.0228942037 0.0732841656
sample38 0.0222077139 0.0962593958
sample39 0.0941738510 -0.0215198638
sample40 -0.0643801315 0.0687866532
sample41 0.0327637931 0.1232188147
sample42 0.0500431832 0.0292474525
sample43 0.0184498759 -0.0233011988
sample44 -0.1487898490 -0.1171350143
sample45 0.1050774309 -0.1123199757
sample46 0.1151195605 0.1094027764
sample47 0.0962593671 0.0288462377
sample48 -0.0004837198 0.0310280863
sample49 -0.1135207665 -0.1213971953
sample50 0.0123553024 0.1740744280
sample51 -0.0550529815 -0.1258887888
sample52 -0.0499121167 -0.0728545480
sample53 -0.1119773626 -0.1588015317
sample54 0.0360055667 -0.0228575843
sample55 -0.0210419010 -0.0006732176
sample56 0.0434169285 -0.0633126148
sample57 -0.0197824516 -0.1150714633
sample58 -0.0030439915 -0.0326098842
sample59 -0.0500253222 -0.0129421753
sample60 -0.0184278693 -0.0136089043
sample61 -0.0150299414 -0.0635027737
sample62 0.0304763761 0.0201316908
sample63 -0.1102252403 -0.1285976758
sample64 -0.1552588046 -0.0971168850
sample65 0.0058503064 -0.0207115049
sample66 0.0025605393 -0.0424318902
sample67 -0.1546634920 0.0661712624
sample68 -0.0536369413 0.0923681542
sample69 -0.0640330439 -0.0081983689
sample70 -0.0163517848 0.0663229961
sample71 0.0102537587 0.1345922570
sample72 0.0654195920 0.0196117298
sample73 0.1048556054 -0.0220939924
sample74 -0.0123799525 -0.0586116174
sample75 -0.0392078000 0.0209754315
sample76 -0.0648953403 0.0524764236
sample77 -0.1172922131 0.0201187034
sample78 0.1463068237 -0.0708470554
sample79 -0.0265211130 0.1603311098
sample80 -0.0279737226 0.0214203802
sample81 -0.0079211512 0.0738452038
sample82 0.1544236477 0.0361467366
sample83 0.0494211235 0.0050045606
sample84 0.0259038487 0.0346550771
sample85 -0.1116484428 0.0031495245
sample86 0.1306482941 0.0377213579
sample87 0.0554778188 0.0459748719
sample88 0.0301623916 -0.0382197774
sample89 0.1016866697 -0.0694034961
sample90 -0.0086819919 0.0201320189
sample91 -0.1578625446 0.2097827041
sample92 -0.0170936737 0.1655810266
sample93 0.0979806779 0.0121511918
sample94 -0.0131484148 0.0114931994
sample95 -0.0315682621 0.0758860582
sample96 -0.0024125624 0.0470136837
sample97 -0.0634545425 -0.0270331182
sample98 0.0359374582 0.0135487885
sample99 0.1009163445 -0.1124778379
sample100 -0.0551753143 -0.0246489938
sample101 0.0080118827 0.1627369454
sample102 0.0046444480 -0.0095627951
sample103 0.0472523118 0.0940393062
sample104 -0.0198159440 0.0591093005
sample105 0.0400237816 0.0160912977
sample106 0.0923808464 -0.0369017393
sample107 0.1019373907 -0.0224954489
sample108 0.0877091652 0.0128834573
sample109 -0.0864824278 0.0900945104
sample110 0.1223115567 0.0096086257
sample111 -0.0257354586 0.0936171892
sample112 0.0765286586 -0.0270348328
sample113 -0.0258803169 -0.0377495758
sample114 -0.0021138977 0.0882015502
sample115 -0.0303460077 0.0723589262
sample116 -0.0780508311 0.0685068602
sample117 -0.0536898010 0.0911911246
sample118 -0.0666651107 0.0236231722
sample119 -0.1021871642 0.2324938327
sample120 -0.0750216543 -0.0243378371
sample121 0.0756936438 -0.0942951085
sample122 0.0259628172 -0.0731985568
sample123 0.1037846219 0.0369196773
sample124 -0.0611207853 -0.0421721858
sample125 0.0738472707 -0.0066949949
sample126 -0.0972916502 -0.0762641457
sample127 -0.0824697669 0.0096637435
sample128 0.1249407748 -0.0929311320
sample129 0.0734067427 0.0434361732
sample130 0.0003501964 0.0309852768
sample131 -0.0930182838 -0.0155937942
sample132 -0.0736222757 -0.0733028466
sample133 0.0498397978 0.0462437951
sample134 -0.1644873465 -0.0720006621
sample135 0.0752297148 -0.0003818987
sample136 -0.0227145873 0.0495505139
sample137 -0.0564717509 0.0288914412
sample138 -0.0255988072 0.0610858774
sample139 -0.0621217818 -0.0235808894
sample140 0.0604152445 0.0435591822
sample141 -0.0246743954 -0.0532648222
sample142 0.0409560407 -0.0316278734
sample143 0.0077355244 0.0476896620
sample144 -0.0173240838 0.0156778165
sample145 -0.0485474361 -0.1202769783
sample146 -0.0419645733 0.0811280310
sample147 0.0977308268 0.0274838405
sample148 -0.0368256126 -0.0803979369
sample149 0.0072865773 0.1532986743
sample150 -0.1020825289 -0.0624775784
sample151 -0.0305399024 0.0289279615
sample152 0.0533594815 0.0638309685
sample153 0.0891627954 -0.1799575451
sample154 0.0727557401 0.0834160142
sample155 0.0880668491 0.0220818449
sample156 0.0276560983 0.0326624797
sample157 0.1155032178 -0.0183616571
sample158 0.0281507491 0.0104938050
sample159 -0.0663235659 -0.0443836689
sample160 0.0302643876 -0.0404266249
sample161 -0.0114715505 0.0591026849
sample162 0.1337087229 -0.1398135441
sample163 -0.1330124381 -0.1688781038
sample164 0.0150336132 -0.0028415156
sample165 -0.0076520254 0.0164128881
sample166 -0.0367794298 -0.0630660930
sample167 -0.1111988887 -0.0030058072
sample168 0.0672981637 -0.0446279017
sample169 0.0413004939 -0.0224395516
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 -0.0420513529 0.0867863168
sample2 -0.0820829106 -0.0410977814
sample3 0.0155901371 -0.0195182462
sample4 -0.1001337242 -0.0410786432
sample5 -0.0153466274 -0.0253259631
sample6 0.0340323780 -0.0408223207
sample7 0.0722580301 0.0002332039
sample8 -0.0457502806 -0.0370016011
sample9 -0.0086248495 0.0820184898
sample10 -0.0423599364 -0.0083923145
sample11 0.0022549689 0.0787766002
sample12 0.0322105074 0.1479824652
sample13 -0.0293891723 -0.0306748494
sample14 0.0337481446 -0.0367506898
sample15 0.0815539621 0.1275622285
sample16 0.0508449365 0.0540604640
sample17 0.0062595711 0.0041023739
sample18 0.0705638896 -0.0351047830
sample19 -0.0476840334 -0.0509598017
sample20 0.0522964318 0.0715521678
sample21 -0.0119129584 -0.0376092951
sample22 0.0724394625 -0.0095625345
sample23 -0.0992532083 0.0134289035
sample24 -0.1595121597 0.0728662534
sample25 -0.0920692610 -0.0749757073
sample26 -0.0595540765 0.0848966217
sample27 0.0826487795 -0.0086735708
sample28 -0.0384789263 0.0440967009
sample29 0.0777673141 0.1735308280
sample30 0.1229471359 -0.0819005828
sample31 0.0579844139 -0.0238644811
sample32 0.0970392542 -0.0111426508
sample33 0.1017587818 -0.0630442806
sample34 0.0637922401 0.0377941580
sample35 0.0789984890 -0.0229723440
sample36 0.1224939160 -0.1274955258
sample37 0.1798821395 -0.1673427914
sample38 0.0466306541 0.0888160765
sample39 -0.0168687811 0.0421533809
sample40 0.1756392697 -0.1526642826
sample41 0.0042373054 0.0004928709
sample42 -0.0447849155 -0.0651504919
sample43 0.0482308069 -0.0253529370
sample44 -0.1986717189 -0.0545777311
sample45 -0.0741838307 0.0054703535
sample46 0.0478774171 -0.0007072211
sample47 0.0608189151 0.0481622468
sample48 -0.1381488846 0.0578288087
sample49 -0.0530522932 -0.1405532624
sample50 -0.0173796350 0.1602389595
sample51 0.0462558700 0.0303473816
sample52 0.0280063516 0.0280388382
sample53 0.0667618659 0.0237701990
sample54 0.0121833082 -0.0521354335
sample55 0.0182395860 0.0221328395
sample56 -0.0001256737 0.0030907403
sample57 0.0316673249 0.0530190288
sample58 0.0393917562 -0.0297798818
sample59 0.1278290481 -0.0546528248
sample60 0.1486984654 0.1069156213
sample61 0.0793121304 0.0569796382
sample62 0.1172801470 -0.0149198784
sample63 -0.0028728682 0.1300519927
sample64 0.0237363165 0.1073287730
sample65 -0.0126535084 0.0589808484
sample66 -0.0468195631 -0.0771072550
sample67 0.1494265046 -0.0769860692
sample68 0.0977962567 -0.0577351346
sample69 0.0403087362 0.0156042027
sample70 0.0221532723 0.0315440859
sample71 -0.0546432307 -0.0272396396
sample72 0.1107488092 -0.0537319652
sample73 0.0906761157 0.0579966404
sample74 0.0586554508 0.0121421577
sample75 0.0390493711 0.0349282710
sample76 -0.0022960278 -0.1676558824
sample77 -0.0232096360 -0.2067302754
sample78 -0.0929756175 -0.0434939227
sample79 -0.1619494281 -0.0378113952
sample80 0.0680366144 0.1424663323
sample81 -0.0530782803 -0.0358350768
sample82 0.0266822541 -0.0577445202
sample83 0.1517235340 -0.0448554706
sample84 -0.0570966575 -0.0273813123
sample85 0.1086289293 -0.1228119592
sample86 0.0833860601 -0.0442915215
sample87 0.0022018886 -0.0943906891
sample88 -0.0078226026 -0.1140506489
sample89 0.0611056149 -0.0094585255
sample90 0.0022928377 -0.0936254012
sample91 0.0433594438 0.3205982545
sample92 -0.1815332995 -0.0334679964
sample93 0.0267631251 0.0614428951
sample94 0.0181878275 0.0605090356
sample95 -0.0720374403 -0.0013045529
sample96 -0.0559713877 -0.0118791311
sample97 -0.0217411224 0.0195414218
sample98 0.0379177866 0.0588357005
sample99 -0.0792429044 -0.0151273540
sample100 0.0222116062 -0.0023321472
sample101 -0.0387225511 0.1224226203
sample102 -0.2094614416 -0.0516442103
sample103 0.0138482864 0.0301051858
sample104 -0.0807986176 -0.0162718758
sample105 -0.0520493309 -0.1229665047
sample106 -0.0192613937 -0.0185238118
sample107 0.0319017098 0.0405123224
sample108 -0.0140690638 0.0163421405
sample109 -0.1831928549 0.0613007948
sample110 -0.0292790434 -0.0199849021
sample111 -0.1423250345 0.0327340617
sample112 0.0426332433 -0.0029083528
sample113 -0.0771905107 0.0268733864
sample114 -0.0241639854 -0.0184080413
sample115 -0.1959014322 0.0460131109
sample116 -0.1394475190 -0.0530805515
sample117 -0.1672360730 -0.1386536051
sample118 -0.0448344025 -0.0117621841
sample119 -0.0910382216 0.2217433451
sample120 -0.0331392502 -0.0057274398
sample121 0.0307573620 0.1392506529
sample122 -0.0839782378 -0.0291994165
sample123 0.0239650945 -0.0642163812
sample124 -0.0909151192 0.0130419752
sample125 -0.0065351101 -0.1092631793
sample126 0.0935311003 0.1368283870
sample127 0.0035388270 0.0292755622
sample128 -0.0660296679 0.1018566543
sample129 0.0693639290 -0.0695421940
sample130 0.0008493947 -0.0669704350
sample131 0.0431023746 0.0174064773
sample132 -0.0637041186 0.0029374930
sample133 -0.0289494108 -0.0390818790
sample134 0.0446201691 0.0456334447
sample135 0.0712337123 0.0521634778
sample136 0.0596272025 0.0197299144
sample137 0.0793152470 -0.0380628520
sample138 -0.0973547412 -0.0454218062
sample139 0.0539903873 -0.1534327481
sample140 0.0850827994 0.0955814293
sample141 -0.0192682725 -0.0554449982
sample142 -0.0672262705 -0.0461320726
sample143 -0.0303729915 -0.0519260193
sample144 -0.0089364268 0.0145814929
sample145 -0.0638772255 0.0122258651
sample146 0.0585857744 0.0063083151
sample147 0.0894133619 -0.1124615945
sample148 -0.0216368518 -0.0615967014
sample149 -0.0515418448 -0.0839903469
sample150 0.0568282153 -0.0124469019
sample151 -0.0789531982 -0.0261831007
sample152 -0.0330752184 0.1306443621
sample153 -0.1751934200 0.1497732650
sample154 0.0421425749 -0.0037010358
sample155 0.0680177971 0.0095711047
sample156 0.0388912011 0.1057562845
sample157 0.0314769308 0.0561367354
sample158 0.0329620767 0.0353947237
sample159 -0.0398417554 -0.1007373644
sample160 0.0424938053 0.0108496103
sample161 -0.0888370644 -0.0679699990
sample162 -0.0027477997 0.1237843956
sample163 -0.0126107966 0.0725434491
sample164 -0.0566779814 -0.0458324036
sample165 -0.0315336268 -0.0236362277
sample166 -0.0612059257 -0.0425232843
sample167 0.0142729863 0.0179308245
sample168 -0.0169504358 -0.0769617828
sample169 0.0675079982 0.0131505177
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 -0.0012329711 1.635717e-01
sample2 -0.0724350140 6.021291e-03
sample3 -0.0188460431 1.080036e-01
sample4 0.0390145219 -3.113884e-04
sample5 0.1774811613 2.996386e-02
sample6 -0.0451444460 3.455860e-02
sample7 -0.0226466185 7.020129e-03
sample8 -0.1033680336 9.856825e-03
sample9 0.1350011783 -8.979099e-02
sample10 0.1259887184 5.097855e-02
sample11 0.0979788417 -7.086536e-02
sample12 -0.0863019143 8.620317e-02
sample13 -0.1381401123 -1.828007e-01
sample14 -0.0615073874 2.642803e-02
sample15 0.0381598996 3.101662e-02
sample16 -0.0048776776 -1.271814e-03
sample17 -0.0788480987 1.547556e-02
sample18 -0.0884188751 3.795486e-02
sample19 0.0703044391 1.084004e-01
sample20 -0.0025585431 -7.975878e-02
sample21 0.0941601563 4.126746e-02
sample22 -0.0550273353 7.806739e-02
sample23 0.0679495233 4.102008e-02
sample24 -0.1310962947 -1.649308e-01
sample25 0.0113585229 4.426864e-02
sample26 -0.1402945982 -2.016540e-02
sample27 0.0261561239 -1.588496e-03
sample28 -0.0724198761 -5.850590e-02
sample29 -0.0330058492 -2.060867e-03
sample30 -0.0228752484 2.015427e-02
sample31 -0.0635067985 6.670335e-02
sample32 0.0685099678 4.955272e-02
sample33 -0.0777765195 1.272078e-01
sample34 0.0157842422 3.024314e-02
sample35 -0.0529632646 -1.500972e-01
sample36 0.0070900927 -2.025308e-01
sample37 -0.0442420383 -1.802089e-01
sample38 -0.0781511238 3.676416e-02
sample39 0.0120331822 3.388843e-02
sample40 -0.0473291872 -1.471562e-01
sample41 0.0228189462 2.673551e-02
sample42 -0.0245360283 7.960867e-02
sample43 0.1036362802 8.229577e-02
sample44 -0.1012228954 -7.049444e-02
sample45 0.0013731929 2.450916e-02
sample46 -0.0558509937 -2.947421e-03
sample47 -0.0380481117 -4.554175e-02
sample48 0.0784342024 -4.888978e-02
sample49 -0.0605164050 1.162359e-02
sample50 0.0530079326 2.737928e-02
sample51 0.1514646517 -5.678343e-02
sample52 0.1860935256 -1.246717e-01
sample53 -0.0064177135 2.700996e-02
sample54 0.0697038333 2.308389e-02
sample55 0.1633577048 -1.366442e-02
sample56 0.1011485084 -4.682203e-02
sample57 0.1730374225 -1.609603e-01
sample58 -0.0071384701 1.666955e-02
sample59 -0.0030461597 -3.005288e-02
sample60 0.0215835303 -2.665878e-01
sample61 0.1510583687 -1.002385e-01
sample62 -0.0925533883 4.845838e-02
sample63 -0.0596311868 4.137025e-02
sample64 -0.0449225830 2.600594e-03
sample65 0.0939383727 4.406910e-02
sample66 0.1063400688 5.709995e-02
sample67 -0.0201589810 -2.361728e-01
sample68 0.0037203320 -2.418394e-02
sample69 -0.0645161218 1.155622e-01
sample70 -0.1013440010 1.351789e-01
sample71 -0.0016467857 2.976839e-02
sample72 0.0328893092 2.835855e-02
sample73 0.0275080071 5.148185e-02
sample74 0.1341719673 7.895280e-02
sample75 0.0951575686 3.943183e-02
sample76 -0.0864721924 -3.034993e-02
sample77 -0.1035749569 2.545353e-02
sample78 -0.1575644214 -4.939590e-02
sample79 0.0189137067 -4.874679e-02
sample80 0.1384140635 -4.267235e-05
sample81 -0.0118846475 6.357931e-02
sample82 -0.1675308128 -3.533913e-02
sample83 -0.0065673333 7.812606e-02
sample84 0.1486891583 3.109058e-02
sample85 -0.0532724321 -7.417888e-02
sample86 -0.1138477269 1.912580e-05
sample87 0.0432864026 -6.080473e-02
sample88 0.0433450390 -1.402490e-01
sample89 0.0331205779 1.395402e-02
sample90 -0.0607412825 8.610413e-02
sample91 -0.0566272483 -1.303748e-01
sample92 -0.0359582510 -1.061604e-01
sample93 -0.0433646357 4.443634e-02
sample94 -0.0477291317 1.059574e-01
sample95 -0.0249595793 3.980525e-02
sample96 0.0035218969 9.293928e-02
sample97 -0.0066048823 1.527231e-01
sample98 0.0020366827 5.579549e-02
sample99 -0.0886616205 3.728231e-02
sample100 -0.1091259141 3.560420e-02
sample101 -0.0739726452 4.317995e-02
sample102 0.0574461014 2.783919e-02
sample103 0.0142731073 -9.705584e-03
sample104 0.0710395200 -4.068350e-02
sample105 0.0980831317 3.452954e-02
sample106 -0.0254259331 -3.628982e-02
sample107 -0.0160653469 9.173395e-02
sample108 -0.0200987669 2.379692e-02
sample109 -0.0389780724 -1.692357e-02
sample110 -0.0326304853 -2.988109e-02
sample111 0.0676937500 6.038214e-02
sample112 0.0167883448 -5.336938e-03
sample113 0.0969216944 2.757606e-02
sample114 -0.0026398354 9.209155e-02
sample115 -0.0308047404 -1.603820e-02
sample116 -0.1240307191 -1.273000e-01
sample117 0.0334729040 -5.392709e-02
sample118 -0.1037152916 -6.252431e-02
sample119 -0.1064176582 -1.196203e-01
sample120 -0.0771355150 1.004933e-01
sample121 -0.0129350773 -3.181974e-02
sample122 0.0847492191 5.568330e-02
sample123 -0.0041336744 -7.693192e-03
sample124 -0.0583458099 8.396392e-02
sample125 0.0634844581 5.232541e-02
sample126 -0.0662580959 1.091732e-01
sample127 -0.0865024630 1.094176e-01
sample128 -0.0627817526 1.470968e-02
sample129 -0.0336276399 4.007856e-02
sample130 -0.0293517754 8.046116e-02
sample131 -0.0469197645 2.209739e-03
sample132 -0.0241740785 1.248599e-01
sample133 0.0907303219 -1.466701e-02
sample134 -0.0350842061 -7.539662e-02
sample135 0.0001333449 -9.185393e-03
sample136 -0.0335876030 9.860271e-02
sample137 -0.0640148867 7.554467e-02
sample138 0.0060964808 1.742763e-02
sample139 -0.0592084415 -5.614970e-02
sample140 0.0427985990 1.099548e-02
sample141 0.0618796326 9.301040e-02
sample142 0.0898554425 -3.573415e-02
sample143 0.0817389245 -8.880524e-02
sample144 0.0787754772 3.821391e-02
sample145 0.1085821534 -1.569476e-01
sample146 -0.0589557882 4.373356e-02
sample147 -0.0495330371 -7.277228e-03
sample148 0.1161592749 -9.079055e-03
sample149 -0.0121579396 -7.788377e-02
sample150 -0.0314512520 -3.520213e-02
sample151 0.0575382133 1.945353e-02
sample152 -0.0494542078 -7.025538e-02
sample153 -0.0941332870 -2.153296e-01
sample154 -0.0335931942 -2.078732e-02
sample155 0.0690457690 2.780408e-02
sample156 0.1039901636 6.292523e-02
sample157 -0.0408645774 -8.065515e-03
sample158 0.1018105332 -7.816883e-03
sample159 -0.0281730580 1.207208e-02
sample160 0.1643053018 -2.978099e-03
sample161 0.0374329251 -8.524610e-02
sample162 -0.0804535369 -8.349751e-02
sample163 -0.0743228059 1.406229e-02
sample164 0.1208805972 2.139462e-02
sample165 0.1608115910 -2.025191e-02
sample166 -0.0425944709 2.660717e-02
sample167 -0.0226849485 4.464281e-02
sample168 -0.0180735611 7.466373e-04
sample169 0.0190779043 -2.645403e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
[,1] [,2]
sample1 -0.0572060227 -1.729087e-02
sample2 0.0875245208 1.112588e-02
sample3 0.0403482602 -3.168994e-02
sample4 -0.0218345996 4.052760e-06
sample5 -0.0150905011 4.795041e-03
sample6 -0.0924362933 4.511003e-02
sample7 -0.0793066751 -1.243823e-02
sample8 -0.1342997187 6.215220e-02
sample9 -0.0338886944 -1.854401e-02
sample10 0.0020547173 1.749421e-02
sample11 0.0037275602 -2.364116e-02
sample12 -0.0753094533 2.772698e-02
sample13 0.0856160091 3.679963e-02
sample14 -0.0737457307 2.668452e-02
sample15 -0.0062111746 -3.554864e-03
sample16 -0.0602355268 6.675115e-02
sample17 0.1086768843 2.524534e-02
sample18 0.0702999472 2.231671e-02
sample19 0.0173785882 -3.024846e-02
sample20 0.0484173812 -3.310904e-02
sample21 0.0124657042 6.517144e-02
sample22 -0.0140989936 -3.159137e-02
sample23 -0.0627028403 -5.393710e-04
sample24 0.0919972100 7.909297e-02
sample25 0.0326998483 -1.945206e-02
sample26 0.1064741246 2.120849e-02
sample27 0.0166058995 -4.964993e-02
sample28 0.0743504770 2.614211e-02
sample29 -0.0511008491 -2.782647e-02
sample30 0.0962250842 -3.974893e-03
sample31 -0.0869563008 5.250819e-02
sample32 0.0271858919 1.552005e-02
sample33 -0.0448364581 6.243160e-03
sample34 0.0718415218 1.469396e-02
sample35 0.0403086451 -1.632629e-02
sample36 -0.1036402827 -1.304320e-02
sample37 -0.0159385744 -3.036525e-02
sample38 0.0182198369 -4.034805e-02
sample39 0.0690363619 8.058350e-03
sample40 -0.0467312750 -2.810325e-02
sample41 0.0263674438 -5.171216e-02
sample42 0.0374578960 -1.268634e-02
sample43 0.0132336869 9.536642e-03
sample44 -0.1119154428 5.028683e-02
sample45 0.0759639367 4.587903e-02
sample46 0.0871885519 -4.670385e-02
sample47 0.0721490571 -1.288540e-02
sample48 0.0005086144 -1.290565e-02
sample49 -0.0858177028 5.173760e-02
sample50 0.0118992665 -7.276215e-02
sample51 -0.0426446855 5.306205e-02
sample52 -0.0381605826 3.086785e-02
sample53 -0.0855757630 6.730043e-02
sample54 0.0261723092 9.184260e-03
sample55 -0.0156418304 4.682404e-04
sample56 0.0307831193 2.597550e-02
sample57 -0.0157242103 4.829381e-02
sample58 -0.0031174404 1.359898e-02
sample59 -0.0373001859 5.868397e-03
sample60 -0.0142609099 5.831654e-03
sample61 -0.0122255144 2.663579e-02
sample62 0.0228002942 -8.692265e-03
sample63 -0.0833127581 5.473229e-02
sample64 -0.1166548159 4.196500e-02
sample65 0.0038808902 8.568590e-03
sample66 0.0011561811 1.766612e-02
sample67 -0.1129311062 -2.608702e-02
sample68 -0.0382526429 -3.804045e-02
sample69 -0.0476502440 4.003241e-03
sample70 -0.0110329882 -2.752719e-02
sample71 0.0096850282 -5.627056e-02
sample72 0.0487124704 -8.800131e-03
sample73 0.0773058132 8.239864e-03
sample74 -0.0102488176 2.454957e-02
sample75 -0.0286613976 -8.387293e-03
sample76 -0.0472655595 -2.129315e-02
sample77 -0.0865043074 -7.296820e-03
sample78 0.1070293698 2.818346e-02
sample79 -0.0165060681 -6.659721e-02
sample80 -0.0206765949 -8.712112e-03
sample81 -0.0050943615 -3.079175e-02
sample82 0.1153622361 -1.647054e-02
sample83 0.0367979217 -2.538114e-03
sample84 0.0199463070 -1.468961e-02
sample85 -0.0827122185 -2.709824e-04
sample86 0.0969487314 -1.699897e-02
sample87 0.0421957457 -1.965953e-02
sample88 0.0215934743 1.566050e-02
sample89 0.0751559502 2.811652e-02
sample90 -0.0057328000 -8.283795e-03
sample91 -0.1134005268 -8.603522e-02
sample92 -0.0101689918 -6.894992e-02
sample93 0.0725967502 -6.003176e-03
sample94 -0.0096878852 -4.693081e-03
sample95 -0.0223502239 -3.139636e-02
sample96 -0.0013232863 -1.963604e-02
sample97 -0.0476541710 1.183660e-02
sample98 0.0269546160 -5.978398e-03
sample99 0.0728179461 4.597884e-02
sample100 -0.0413398038 1.079347e-02
sample101 0.0087536994 -6.796076e-02
sample102 0.0032509529 3.932612e-03
sample103 0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105 0.0294940465 -7.140722e-03
sample106 0.0686472054 1.462895e-02
sample107 0.0748635927 8.401339e-03
sample108 0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110 0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112 0.0570870472 1.066018e-02
sample113 -0.0200110554 1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586 1.089843e-02
sample121 0.0542443861 3.861344e-02
sample122 0.0178575357 3.027138e-02
sample123 0.0775020581 -1.636852e-02
sample124 -0.0460701050 1.814758e-02
sample125 0.0543846585 2.075898e-03
sample126 -0.0729417144 3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128 0.0908136899 3.758801e-02
sample129 0.0552445878 -1.879062e-02
sample130 0.0007128089 -1.294308e-02
sample131 -0.0693311345 7.357082e-03
sample132 -0.0556565156 3.126995e-02
sample133 0.0375870104 -1.977240e-02
sample134 -0.1229130924 3.159495e-02
sample135 0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201 1.040683e-02
sample140 0.0452288969 -1.876279e-02
sample141 -0.0189142561 2.247042e-02
sample142 0.0297545566 1.280524e-02
sample143 0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491 5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147 0.0726083535 -1.239968e-02
sample148 -0.0284795794 3.389732e-02
sample149 0.0082261455 -6.399305e-02
sample150 -0.0765013197 2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152 0.0403422737 -2.714879e-02
sample153 0.0629117719 7.425085e-02
sample154 0.0551622927 -3.548984e-02
sample155 0.0654439133 -1.005306e-02
sample156 0.0209310714 -1.390213e-02
sample157 0.0851522597 6.577150e-03
sample158 0.0208354599 -4.663078e-03
sample159 -0.0498794349 1.913257e-02
sample160 0.0216074437 1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162 0.0963663017 5.705881e-02
sample163 -0.1009542191 7.174224e-02
sample164 0.0109881996 1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357 2.673084e-02
sample167 -0.0825048036 2.278863e-03
sample168 0.0486147429 1.793843e-02
sample169 0.0302506727 8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
[,1] [,2]
sample1 -0.0621842115 -1.364509e-02
sample2 0.0944623785 9.720892e-03
sample3 0.0406196267 -2.236338e-02
sample4 -0.0229316496 -3.932487e-04
sample5 -0.0157330047 3.231033e-03
sample6 -0.0945794025 3.120720e-02
sample7 -0.0854427118 -1.052880e-02
sample8 -0.1376625920 4.286608e-02
sample9 -0.0377115311 -1.415134e-02
sample10 0.0035244506 1.280825e-02
sample11 0.0016639987 -1.717895e-02
sample12 -0.0781403168 1.884368e-02
sample13 0.0938400516 2.838858e-02
sample14 -0.0759839772 1.810989e-02
sample15 -0.0068340837 -2.705361e-03
sample16 -0.0590150849 4.757848e-02
sample17 0.1178805097 2.040526e-02
sample18 0.0767858320 1.756604e-02
sample19 0.0157112113 -2.172867e-02
sample20 0.0485318300 -2.327033e-02
sample21 0.0185928176 4.777095e-02
sample22 -0.0191358702 -2.329775e-02
sample23 -0.0672994194 -1.535656e-03
sample24 0.1047476642 5.935707e-02
sample25 0.0329844953 -1.358036e-02
sample26 0.1154952052 1.741529e-02
sample27 0.0133849853 -3.590922e-02
sample28 0.0821554039 2.042376e-02
sample29 -0.0567643690 -2.123848e-02
sample30 0.1016073931 -1.134728e-03
sample31 -0.0880396372 3.670548e-02
sample32 0.0300363338 1.182406e-02
sample33 -0.0467252272 3.739254e-03
sample34 0.0783666394 1.203777e-02
sample35 0.0424227097 -1.118559e-02
sample36 -0.1107646166 -1.143464e-02
sample37 -0.0191667664 -2.246060e-02
sample38 0.0155968095 -2.909621e-02
sample39 0.0746847148 7.148218e-03
sample40 -0.0517028178 -2.137267e-02
sample41 0.0234979494 -3.723018e-02
sample42 0.0388797356 -8.557228e-03
sample43 0.0149555568 7.210002e-03
sample44 -0.1150305613 3.461805e-02
sample45 0.0846146236 3.486020e-02
sample46 0.0884426404 -3.246853e-02
sample47 0.0748644971 -8.083045e-03
sample48 -0.0012033198 -9.403647e-03
sample49 -0.0872662737 3.616245e-02
sample50 0.0066941314 -5.284863e-02
sample51 -0.0411777630 3.791830e-02
sample52 -0.0379355780 2.180834e-02
sample53 -0.0851639886 4.751761e-02
sample54 0.0288006248 7.184424e-03
sample55 -0.0164920835 5.919925e-05
sample56 0.0355115616 1.951043e-02
sample57 -0.0141146068 3.492409e-02
sample58 -0.0015636132 9.862883e-03
sample59 -0.0390656483 3.590929e-03
sample60 -0.0139454780 3.963030e-03
sample61 -0.0106410274 1.919705e-02
sample62 0.0236748439 -5.922677e-03
sample63 -0.0846790877 3.839102e-02
sample64 -0.1202581015 2.846469e-02
sample65 0.0050548584 6.328644e-03
sample66 0.0028013072 1.291807e-02
sample67 -0.1231623009 -2.112565e-02
sample68 -0.0437782161 -2.845072e-02
sample69 -0.0501199692 2.053469e-03
sample70 -0.0140278645 -2.027157e-02
sample71 0.0057489505 -4.085977e-02
sample72 0.0511212704 -5.522408e-03
sample73 0.0828141409 7.431582e-03
sample74 -0.0085959456 1.772951e-02
sample75 -0.0312180394 -6.636869e-03
sample76 -0.0519051781 -1.640191e-02
sample77 -0.0925924762 -6.907800e-03
sample78 0.1163971046 2.251122e-02
sample79 -0.0240906926 -4.887766e-02
sample80 -0.0221327065 -6.730703e-03
sample81 -0.0072114968 -2.254399e-02
sample82 0.1204416674 -9.907422e-03
sample83 0.0386739485 -1.171663e-03
sample84 0.0195988488 -1.033806e-02
sample85 -0.0877680171 -1.725057e-03
sample86 0.1023541048 -1.062501e-02
sample87 0.0425213089 -1.356865e-02
sample88 0.0244788514 1.180820e-02
sample89 0.0804276691 2.188588e-02
sample90 -0.0074639871 -6.140721e-03
sample91 -0.1278832404 -6.485140e-02
sample92 -0.0162199697 -5.048358e-02
sample93 0.0769344893 -3.045135e-03
sample94 -0.0104345587 -3.593172e-03
sample95 -0.0260058453 -2.330475e-02
sample96 -0.0025018700 -1.433516e-02
sample97 -0.0492358305 7.774183e-03
sample98 0.0279220220 -3.862141e-03
sample99 0.0813921923 3.487339e-02
sample100 -0.0428797405 7.112807e-03
sample101 0.0032855240 -4.940743e-02
sample102 0.0038439317 2.938008e-03
sample103 0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105 0.0314853405 -4.656633e-03
sample106 0.0726456731 1.192390e-02
sample107 0.0807342975 7.508627e-03
sample108 0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110 0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112 0.0599954082 8.820317e-03
sample113 -0.0195006577 1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131 6.937326e-03
sample121 0.0613899126 2.915307e-02
sample122 0.0218424338 2.241775e-02
sample123 0.0809008460 -1.051759e-02
sample124 -0.0472109313 1.239887e-02
sample125 0.0583180947 2.521167e-03
sample126 -0.0753941872 2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128 0.1000212216 2.908091e-02
sample129 0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877 4.091672e-03
sample132 -0.0566219024 2.179861e-02
sample133 0.0384172955 -1.372840e-02
sample134 -0.1280862736 2.077912e-02
sample135 0.0592633273 6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861 6.729304e-03
sample140 0.0468926306 -1.285498e-02
sample141 -0.0186248693 1.605439e-02
sample142 0.0328031246 9.887746e-03
sample143 0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310 3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147 0.0765025670 -7.714769e-03
sample148 -0.0276016641 2.420589e-02
sample149 0.0027545308 -4.653007e-02
sample150 -0.0792296010 1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152 0.0409796547 -1.907063e-02
sample153 0.0734301757 5.528780e-02
sample154 0.0557740684 -2.487723e-02
sample155 0.0689436560 -6.127635e-03
sample156 0.0212272938 -9.747423e-03
sample157 0.0911931194 6.355708e-03
sample158 0.0220840645 -3.016357e-03
sample159 -0.0513244242 1.304175e-02
sample160 0.0246213576 1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162 0.1078802043 4.337260e-02
sample163 -0.1017965082 5.047171e-02
sample164 0.0119430799 9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180 1.899222e-02
sample167 -0.0872832229 1.516582e-04
sample168 0.0540714512 1.397701e-02
sample169 0.0328432652 7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
[,1] [,2]
sample1 0.0133684846 2.195848e-02
sample2 0.0254157197 -1.058416e-02
sample3 -0.0049551479 -4.840017e-03
sample4 0.0310390570 -1.063929e-02
sample5 0.0046941318 -6.488426e-03
sample6 -0.0107406753 -1.026702e-02
sample7 -0.0225157631 2.624712e-04
sample8 0.0141320952 -9.505821e-03
sample9 0.0029681280 2.078210e-02
sample10 0.0131729174 -2.275042e-03
sample11 -0.0004164298 1.994019e-02
sample12 -0.0095211620 3.759883e-02
sample13 0.0091018604 -7.953956e-03
sample14 -0.0106557524 -9.181659e-03
sample15 -0.0249924121 3.262724e-02
sample16 -0.0156216400 1.375700e-02
sample17 -0.0019382446 1.073994e-03
sample18 -0.0221072481 -8.703592e-03
sample19 0.0146917619 -1.311712e-02
sample20 -0.0160353760 1.826290e-02
sample21 0.0035947899 -9.616341e-03
sample22 -0.0225060762 -2.532589e-03
sample23 0.0310000683 3.033060e-03
sample24 0.0499544372 1.809450e-02
sample25 0.0284442301 -1.932558e-02
sample26 0.0188220043 2.146985e-02
sample27 -0.0257763219 -1.999228e-03
sample28 0.0120888648 1.125834e-02
sample29 -0.0236482520 4.426726e-02
sample30 -0.0385486305 -2.055935e-02
sample31 -0.0181539336 -5.877838e-03
sample32 -0.0302630460 -2.607192e-03
sample33 -0.0319565715 -1.562628e-02
sample34 -0.0197970124 9.906813e-03
sample35 -0.0247412713 -5.434440e-03
sample36 -0.0386259060 -3.190394e-02
sample37 -0.0566199273 -4.192574e-02
sample38 -0.0142060273 2.259644e-02
sample39 0.0053589035 1.076485e-02
sample40 -0.0552546493 -3.819896e-02
sample41 -0.0013089975 9.278818e-05
sample42 0.0137252142 -1.664652e-02
sample43 -0.0151259626 -6.290953e-03
sample44 0.0617391754 -1.442883e-02
sample45 0.0231410886 1.163143e-03
sample46 -0.0148898209 -1.384176e-04
sample47 -0.0187252536 1.221690e-02
sample48 0.0432839432 1.416671e-02
sample49 0.0160818605 -3.588745e-02
sample50 0.0059333545 4.067003e-02
sample51 -0.0142914866 7.776270e-03
sample52 -0.0086339952 7.208917e-03
sample53 -0.0207386980 6.272432e-03
sample54 -0.0039856719 -1.316934e-02
sample55 -0.0056217017 5.692315e-03
sample56 0.0000123292 8.978290e-04
sample57 -0.0095805555 1.324253e-02
sample58 -0.0124160295 -7.326376e-03
sample59 -0.0400195442 -1.349736e-02
sample60 -0.0460063358 2.770091e-02
sample61 -0.0245266456 1.470710e-02
sample62 -0.0366022783 -3.437352e-03
sample63 0.0013742171 3.288796e-02
sample64 -0.0070599859 2.739588e-02
sample65 0.0041201911 1.498268e-02
sample66 0.0143173351 -1.968812e-02
sample67 -0.0467477531 -1.929938e-02
sample68 -0.0306751978 -1.436184e-02
sample69 -0.0125317217 4.130407e-03
sample70 -0.0068071487 8.080857e-03
sample71 0.0169170264 -7.027348e-03
sample72 -0.0346909749 -1.333770e-02
sample73 -0.0280506153 1.493843e-02
sample74 -0.0182611498 3.294697e-03
sample75 -0.0120563964 8.974612e-03
sample76 0.0001437236 -4.253184e-02
sample77 0.0065330299 -5.252886e-02
sample78 0.0288278141 -1.127782e-02
sample79 0.0503961481 -1.023318e-02
sample80 -0.0207693429 3.648391e-02
sample81 0.0163562768 -9.074596e-03
sample82 -0.0084317129 -1.478976e-02
sample83 -0.0474097918 -1.103126e-02
sample84 0.0177181395 -7.191197e-03
sample85 -0.0342718548 -3.082360e-02
sample86 -0.0261671791 -1.089491e-02
sample87 -0.0009486358 -2.411514e-02
sample88 0.0020528931 -2.894615e-02
sample89 -0.0189361111 -2.638639e-03
sample90 -0.0009863658 -2.390075e-02
sample91 -0.0124352695 8.153234e-02
sample92 0.0564264106 -8.909537e-03
sample93 -0.0081461774 1.570851e-02
sample94 -0.0054896581 1.547251e-02
sample95 0.0224073150 -4.374348e-04
sample96 0.0173528924 -3.050441e-03
sample97 0.0067948115 5.008237e-03
sample98 -0.0116030825 1.498764e-02
sample99 0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101 0.0124923691 3.091503e-02
sample102 0.0650835386 -1.367400e-02
sample103 -0.0042741828 7.855985e-03
sample104 0.0250591040 -4.171938e-03
sample105 0.0157516368 -3.121990e-02
sample106 0.0060593853 -5.101693e-03
sample107 -0.0098329626 1.044506e-02
sample108 0.0044269853 4.142036e-03
sample109 0.0572473486 1.517542e-02
sample110 0.0090474827 -5.119868e-03
sample111 0.0444263015 7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113 0.0241047399 6.706740e-03
sample114 0.0074558775 -4.728652e-03
sample115 0.0611851433 1.117210e-02
sample116 0.0432646951 -1.380556e-02
sample117 0.0516750066 -3.575617e-02
sample118 0.0139942100 -3.279138e-03
sample119 0.0291722987 5.587946e-02
sample120 0.0103515853 -1.690016e-03
sample121 -0.0091396331 3.552116e-02
sample122 0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124 0.0283466326 3.127845e-03
sample125 0.0016472378 -2.770692e-02
sample126 -0.0286529417 3.489336e-02
sample127 -0.0010224500 7.483214e-03
sample128 0.0209049296 2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968 4.637390e-03
sample132 0.0198526786 5.723983e-04
sample133 0.0088812957 -9.988115e-03
sample134 -0.0137484514 1.172591e-02
sample135 -0.0220314568 1.347465e-02
sample136 -0.0185173353 5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138 0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762 2.456863e-02
sample141 0.0058369763 -1.420854e-02
sample142 0.0207886071 -1.188764e-02
sample143 0.0092832598 -1.324238e-02
sample144 0.0028442140 3.627979e-03
sample145 0.0199749569 2.862202e-03
sample146 -0.0182236697 1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148 0.0065435868 -1.572917e-02
sample149 0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151 0.0245166984 -6.888241e-03
sample152 0.0107259913 3.314630e-02
sample153 0.0550963965 3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574 2.642246e-03
sample156 -0.0117803505 2.698265e-02
sample157 -0.0096167165 1.433840e-02
sample158 -0.0101754772 9.137620e-03
sample159 0.0120662931 -2.565236e-02
sample160 -0.0132238202 2.916023e-03
sample161 0.0274491966 -1.748284e-02
sample162 0.0012482909 3.152261e-02
sample163 0.0042031315 1.830701e-02
sample164 0.0174896157 -1.175915e-02
sample165 0.0097517662 -6.119019e-03
sample166 0.0190134679 -1.121582e-02
sample167 -0.0044140836 4.665585e-03
sample168 0.0049689168 -1.941822e-02
sample169 -0.0209802098 3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
[,1] [,2]
sample1 -0.0515543627 -0.0305856787
sample2 -0.0144993256 0.0236342950
sample3 -0.0371833108 -0.0140263348
sample4 0.0068945388 -0.0132539692
sample5 0.0215035333 -0.0663338101
sample6 -0.0187055152 0.0088773016
sample7 -0.0061521552 0.0064029054
sample8 -0.0210874459 0.0334652901
sample9 0.0516865043 -0.0291142799
sample10 0.0059440366 -0.0527217447
sample11 0.0393010793 -0.0200624712
sample12 -0.0420837100 0.0131331362
sample13 0.0333252565 0.0818552509
sample14 -0.0190062644 0.0160202175
sample15 -0.0030968049 -0.0189230681
sample16 -0.0004452158 0.0018880102
sample17 -0.0185848615 0.0240170131
sample18 -0.0273093598 0.0230213640
sample19 -0.0217761111 -0.0445894441
sample20 0.0245820821 0.0159812738
sample21 0.0034527644 -0.0400016054
sample22 -0.0340789054 0.0039289109
sample23 -0.0010344929 -0.0310161212
sample24 0.0289468503 0.0760962436
sample25 -0.0119098496 -0.0122798760
sample26 -0.0181001057 0.0517892852
sample27 0.0050465417 -0.0086515844
sample28 0.0057491502 0.0358830107
sample29 -0.0051104246 0.0116605117
sample30 -0.0103085904 0.0039678538
sample31 -0.0319929858 0.0090606113
sample32 -0.0036232521 -0.0328202010
sample33 -0.0534742153 0.0024751837
sample34 -0.0067495749 -0.0111000311
sample35 0.0378745721 0.0465929296
sample36 0.0647886800 0.0359987924
sample37 0.0488441236 0.0492906912
sample38 -0.0251514062 0.0197110110
sample39 -0.0085428066 -0.0105117852
sample40 0.0379324087 0.0440810741
sample41 -0.0044199152 -0.0128820644
sample42 -0.0292553573 -0.0067045265
sample43 -0.0077829155 -0.0510178219
sample44 0.0045122248 0.0479660309
sample45 -0.0074444298 -0.0051116726
sample46 -0.0088025512 0.0196186661
sample47 0.0076696301 0.0215947965
sample48 0.0290108585 -0.0175568376
sample49 -0.0141754858 0.0184717099
sample50 0.0006282201 -0.0233054373
sample51 0.0441995177 -0.0410022921
sample52 0.0715329391 -0.0399499475
sample53 -0.0095954087 -0.0029140909
sample54 0.0048933768 -0.0281884386
sample55 0.0327325487 -0.0532290012
sample56 0.0323068984 -0.0256595538
sample57 0.0806603122 -0.0286748097
sample58 -0.0064792049 -0.0006945349
sample59 0.0088958941 0.0067389649
sample60 0.0874124612 0.0431964341
sample61 0.0577604571 -0.0326112099
sample62 -0.0313318464 0.0224391756
sample63 -0.0233625220 0.0125110562
sample64 -0.0086426068 0.0148770341
sample65 0.0025256193 -0.0404466327
sample66 0.0006014071 -0.0471576264
sample67 0.0706087042 0.0516228406
sample68 0.0082301011 0.0033109509
sample69 -0.0475076743 0.0001452708
sample70 -0.0600773716 0.0089986962
sample71 -0.0096321627 -0.0050761187
sample72 -0.0031773546 -0.0166221542
sample73 -0.0113700517 -0.0191726684
sample74 -0.0014179662 -0.0608101325
sample75 0.0041911740 -0.0399981269
sample76 -0.0055326449 0.0353114263
sample77 -0.0260214459 0.0305731380
sample78 -0.0119267436 0.0632236007
sample79 0.0186017239 0.0027402910
sample80 0.0241047889 -0.0472697181
sample81 -0.0220288317 -0.0079577210
sample82 -0.0180751258 0.0639051029
sample83 -0.0256671713 -0.0125898269
sample84 0.0161392598 -0.0567222449
sample85 0.0139988188 0.0322763454
sample86 -0.0198382995 0.0389225776
sample87 0.0266270281 -0.0032979996
sample88 0.0515677078 0.0117902495
sample89 0.0014022125 -0.0140510488
sample90 -0.0375949749 0.0044004551
sample91 0.0310397965 0.0440610926
sample92 0.0270570567 0.0324380452
sample93 -0.0215009202 0.0063993941
sample94 -0.0415702912 -0.0037692077
sample95 -0.0168416047 0.0010019120
sample96 -0.0285582661 -0.0187991000
sample97 -0.0490843868 -0.0266760748
sample98 -0.0171579033 -0.0112897471
sample99 -0.0271316525 0.0232395583
sample100 -0.0301789816 0.0305498693
sample101 -0.0264371151 0.0170723968
sample102 0.0012767734 -0.0248949597
sample103 0.0055214687 -0.0030040587
sample104 0.0251346074 -0.0165212671
sample105 0.0062424215 -0.0400309901
sample106 0.0069768684 0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679 0.0023637162
sample109 -0.0014762845 0.0165583675
sample110 0.0036971063 0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112 0.0046098120 -0.0048009350
sample113 0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453 0.0135805368
sample116 0.0183575759 0.0665377581
sample117 0.0227640036 -0.0012287760
sample118 0.0015695248 0.0472617382
sample119 0.0190084932 0.0590034062
sample120 -0.0449645755 0.0072755697
sample121 0.0077307184 0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123 0.0016959300 0.0028593594
sample124 -0.0365091615 0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408 0.0019165544
sample127 -0.0494064872 0.0088209044
sample128 -0.0155454766 0.0186819802
sample129 -0.0184340400 0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422 0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133 0.0204029276 -0.0282209049
sample134 0.0175513332 0.0262883962
sample135 0.0029009925 0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323 0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139 0.0073103935 0.0308956174
sample140 0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142 0.0268670676 -0.0239229634
sample143 0.0421049126 -0.0110888235
sample144 0.0017253664 -0.0341766012
sample145 0.0681741320 -0.0073526377
sample146 -0.0239965222 0.0118396767
sample147 -0.0063453522 0.0183130585
sample148 0.0230825251 -0.0379753037
sample149 0.0223298673 0.0188909118
sample150 0.0055709108 0.0174179009
sample151 0.0039177786 -0.0233533275
sample152 0.0134325667 0.0302344591
sample153 0.0511990309 0.0730230140
sample154 0.0006698324 0.0154177486
sample155 0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599 0.0154934573
sample158 0.0201775524 -0.0332982124
sample159 -0.0086909001 0.0073496711
sample160 0.0295437331 -0.0555734536
sample161 0.0332754288 0.0033779619
sample162 0.0121954537 0.0433540412
sample163 -0.0173490933 0.0227219128
sample164 0.0143374783 -0.0453542590
sample165 0.0343612593 -0.0511194536
sample166 -0.0157536004 0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919 0.0060747155
sample169 0.0116231468 -0.0015112800
>
> ## 3.3 Plotting VAF
>
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
>
> # JIVE plotVAF
> plotVAF(jiveRes)
>
>
> #########################
> ## PART 4. Plot Results
>
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block="",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="",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="",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
> # Combined plot of scores for common part. DISCO.
> plotRes(object=discoRes,comps=c(1,1),what="scores",type="common",
+ combined=TRUE,block="",color="classname")
Warning message:
In plotRes(object = discoRes, comps = c(1, 1), what = "scores", :
It is not possible to combine common components in DISCO-SCA approach.
>
>
> # 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="",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=TRUE,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=TRUE,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=TRUE,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=TRUE,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))
>
> # Combined plot for loadings for common part. DISCO-SCA.
> plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+ combined=TRUE,block="",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
> # 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 for distinctive part
> plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+ combined=TRUE,block="",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
> # Combined plot for common and distinctive part (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+ combined=TRUE,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=TRUE,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))
>
>
>
> #########################
> ## PART 5. Biplot results
>
> ## Common components DISCO-SCA
> biplotRes(object=discoRes,type="common",comps=c(1,2),block="",title=NULL,
+ colorCol="classname",sizeValues=c(2,4),shapeValues=c(17,0),
+ background=TRUE,pointSize=4,labelSize=NULL,axisSize=NULL,
+ titleSize=NULL)
>
>
> ## Common components O2PLS
> p1 <- biplotRes(object=o2plsRes,type="common",comps=c(1,2),block="expr",title=NULL,
+ colorCol="classname",sizeValues=c(2,4),shapeValues=c(17,0),
+ background=TRUE,pointSize=4,labelSize=NULL,axisSize=NULL,
+ titleSize=NULL)
Warning message:
In data.row.names(row.names, rowsi, i) :
some row.names duplicated: 170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338 --> row.names NOT used
> p2 <- biplotRes(object=o2plsRes,type="common",comps=c(1,2),block="mirna",title=NULL,
+ colorCol="classname",sizeValues=c(2,4),shapeValues=c(17,0),
+ background=TRUE,pointSize=4,labelSize=NULL,axisSize=NULL,titleSize=NULL)
Warning message:
In data.row.names(row.names, rowsi, i) :
some row.names duplicated: 170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338 --> row.names NOT used
> 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))
>
> ## Distintive components DISCO-SCA
> p1 <- biplotRes(object=discoRes,type="individual",comps=c(1,2),block="expr",title=NULL,
+ colorCol="classname",sizeValues=c(2,4),shapeValues=c(17,0),
+ background=TRUE,pointSize=4,labelSize=NULL,axisSize=NULL,
+ titleSize=NULL)
Warning message:
In data.row.names(row.names, rowsi, i) :
some row.names duplicated: 170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338 --> row.names NOT used
> p2 <- biplotRes(object=discoRes,type="individual",comps=c(1,2),block="mirna",title=NULL,
+ colorCol="classname",sizeValues=c(2,4),shapeValues=c(17,0),
+ background=TRUE,pointSize=4,labelSize=NULL,axisSize=NULL,
+ titleSize=NULL)
Warning message:
In data.row.names(row.names, rowsi, i) :
some row.names duplicated: 170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338 --> row.names NOT used
> 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))
>
>
>
> proc.time()
user system elapsed
17.76 0.39 18.26
|
STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsPCA.Rout
R version 3.4.4 (2018-03-15) -- "Someone to Lean On"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> ###########################################
> ########### EXAMPLE OF THE OMICSPCA
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
>
> # g_legend (not exported by STATegRa any more)
> ## code from https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs
> g_legend<-function(a.gplot){
+ tmp <- ggplot_gtable(ggplot_build(a.gplot))
+ leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
+ legend <- tmp$grobs[[leg]]
+ return(legend)}
>
> #########################
> ## PART 1. Load data
>
> ## Load data
> data(STATegRa_S3)
>
> ls()
[1] "Block1.PCA" "Block2.PCA" "ed.PCA" "g_legend"
>
> ## Create ExpressionSets
> # Block1 - Expression data
> B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname"))
>
> #########################
> ## PART 2. Model Selection
>
> require(grid)
Loading required package: grid
> require(gridExtra)
Loading required package: gridExtra
> require(ggplot2)
Loading required package: ggplot2
>
> ## 2.1 Select common components
> cc <- selectCommonComps(X=Block1.PCA,Y=Block2.PCA,Rmax=3)
> cc$common
[1] 2
> cc$pssq
> cc$pratios
> #png("modelSelection.png",width=822,height=416)
> grid.arrange(cc$pssq,cc$pratios,ncol=2)
> #dev.off()
>
> ## 2.2 Select distinctive components
> # Block 1
> PCA.selection(Data=Block1.PCA,fac.sel="single%",varthreshold=0.03)$numComps
[1] 4
> # Block2
> PCA.selection(Data=Block2.PCA,fac.sel="single%",varthreshold=0.03)$numComps
[1] 4
>
> ## 2.3 Optimal components analysis
> ms <- modelSelection(Input=list(B1,B2),Rmax=4,fac.sel="single%",varthreshold=0.03)
> ms
$common
[1] 2
$dist
[1] 2 2
>
> #########################
> ## PART 3. Component Analysis
>
> ## 3.1 Component analysis of the three methods
> discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=ms$common,Rspecific=ms$dist,center=TRUE,
+ scale=TRUE,weight=TRUE)
> jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=ms$common,Rspecific=ms$dist,center=TRUE,
+ scale=TRUE,weight=TRUE)
> o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=ms$common,Rspecific=ms$dist,center=TRUE,
+ scale=TRUE,weight=TRUE)
>
> ## 3.2 Exploring scores structures
>
> # Exploring DISCO-SCA scores structure
> discoRes@scores$common ## Common scores
1 2
sample1 0.0781574271 -0.0431500159
sample2 -0.1192218283 0.0294091364
sample3 -0.0531412251 -0.0746839920
sample4 0.0292975292 -0.0005956861
sample5 0.0202091807 0.0110464056
sample6 0.1226089080 0.1053465997
sample7 0.1078927977 -0.0322478156
sample8 0.1782895471 0.1449365434
sample9 0.0468698159 -0.0455174437
sample10 -0.0036030419 0.0420112407
sample11 -0.0035566475 -0.0566292926
sample12 0.1006128877 0.0641380231
sample13 -0.1174408170 0.0907488944
sample14 0.0981203246 0.0617737065
sample15 0.0085334190 -0.0087015939
sample16 0.0783148735 0.1581292822
sample17 -0.1483609890 0.0638582017
sample18 -0.0963086330 0.0556638146
sample19 -0.0217244128 -0.0720084387
sample20 -0.0635636510 -0.0779654958
sample21 -0.0201840188 0.1566391739
sample22 0.0218268522 -0.0764106478
sample23 0.0852042146 -0.0032685559
sample24 -0.1287170192 0.1924548578
sample25 -0.0430574085 -0.0456563049
sample26 -0.1453896720 0.0541514442
sample27 -0.0197489025 -0.1185659547
sample28 -0.1025336179 0.0650686831
sample29 0.0706018326 -0.0682990454
sample30 -0.1295627721 -0.0066773224
sample31 0.1147449114 0.1232684900
sample32 -0.0374310985 0.0380174894
sample33 0.0599515796 0.0136864264
sample34 -0.0984200879 0.0375319199
sample35 -0.0543098459 -0.0378108948
sample36 0.1403625326 -0.0343761155
sample37 0.0228941629 -0.0732852998
sample38 -0.0222077430 -0.0962595988
sample39 -0.0941738459 0.0215199990
sample40 0.0643800899 -0.0687877475
sample41 -0.0327638149 -0.1232188206
sample42 -0.0500431845 -0.0292471616
sample43 -0.0184498888 0.0233009630
sample44 0.1487899172 0.1171360512
sample45 -0.1050773981 0.1123204176
sample46 -0.1151195891 -0.1094030177
sample47 -0.0962593823 -0.0288465699
sample48 0.0004837525 -0.0310273485
sample49 0.1135207949 0.1213974554
sample50 -0.0123553256 -0.1740742861
sample51 0.0550529971 0.1258884774
sample52 0.0499121341 0.0728542937
sample53 0.1119773696 0.1588011668
sample54 -0.0360055673 0.0228575149
sample55 0.0210419004 0.0006730881
sample56 -0.0434169135 0.0633125819
sample57 0.0197824776 0.1150711796
sample58 0.0030439857 0.0326096699
sample59 0.0500252964 0.0129414313
sample60 0.0184278590 0.0136079444
sample61 0.0150299433 0.0635022663
sample62 -0.0304764117 -0.0201322974
sample63 0.1102252577 0.1285977353
sample64 0.1552588141 0.0971167608
sample65 -0.0058503020 0.0207116016
sample66 -0.0025605254 0.0424321514
sample67 0.1546634649 -0.0661722645
sample68 0.0536369058 -0.0923687308
sample69 0.0640330258 0.0081982154
sample70 0.0163517575 -0.0663230219
sample71 -0.0102537694 -0.1345919416
sample72 -0.0654196230 -0.0196123321
sample73 -0.1048556264 0.0220935459
sample74 0.0123799436 0.0586113213
sample75 0.0392077861 -0.0209756360
sample76 0.0648953327 -0.0524764636
sample77 0.1172922104 -0.0201186097
sample78 -0.1463067897 0.0708475756
sample79 0.0265211298 -0.1603302572
sample80 0.0279737064 -0.0214207485
sample81 0.0079211466 -0.0738448807
sample82 -0.1544236595 -0.0361468707
sample83 -0.0494211662 -0.0050053559
sample84 -0.0259038413 -0.0346547653
sample85 0.1116484213 -0.0031502055
sample86 -0.1306483212 -0.0377217932
sample87 -0.0554778217 -0.0459749374
sample88 -0.0301623729 0.0382197129
sample89 -0.1016866739 0.0694031753
sample90 0.0086819798 -0.0201319952
sample91 0.1578625139 -0.2097829661
sample92 0.0170936980 -0.1655800891
sample93 -0.0979806897 -0.0121512795
sample94 0.0131484002 -0.0114932207
sample95 0.0315682636 -0.0758856383
sample96 0.0024125607 -0.0470133235
sample97 0.0634545397 0.0270333225
sample98 -0.0359374731 -0.0135489476
sample99 -0.1009163129 0.1124783223
sample100 0.0551753086 0.0246488974
sample101 -0.0080119025 -0.1627366679
sample102 -0.0046443996 0.0095639516
sample103 -0.0472523284 -0.0940393765
sample104 0.0198159577 -0.0591088935
sample105 -0.0400237743 -0.0160910238
sample106 -0.0923808338 0.0369018385
sample107 -0.1019374019 0.0224953526
sample108 -0.0877091660 -0.0128833457
sample109 0.0864824578 -0.0900935014
sample110 -0.1223115496 -0.0096084621
sample111 0.0257354736 -0.0936163728
sample112 -0.0765286637 0.0270346028
sample113 0.0258803400 0.0377500075
sample114 0.0021138822 -0.0882013670
sample115 0.0303460433 -0.0723578422
sample116 0.0780508610 -0.0685061696
sample117 0.0536898295 -0.0911902740
sample118 0.0666651210 -0.0236229593
sample119 0.1021871590 -0.2324933513
sample120 0.0750216567 0.0243380764
sample121 -0.0756936330 0.0942949639
sample122 -0.0259627898 0.0731990399
sample123 -0.1037846331 -0.0369198118
sample124 0.0611208056 0.0421727389
sample125 -0.0738472722 0.0066950403
sample126 0.0972916316 0.0762637157
sample127 0.0824697549 -0.0096636941
sample128 -0.1249407464 0.0929315560
sample129 -0.0734067698 -0.0434365330
sample130 -0.0003502087 -0.0309852454
sample131 0.0930182755 0.0155935528
sample132 0.0736222917 0.0733032641
sample133 -0.0498397961 -0.0462436567
sample134 0.1644873531 0.0720003607
sample135 -0.0752297302 0.0003815223
sample136 0.0227145576 -0.0495507811
sample137 0.0564717208 -0.0288918445
sample138 0.0255988188 -0.0610853434
sample139 0.0621217754 0.0235805229
sample140 -0.0604152706 -0.0435596220
sample141 0.0246744012 0.0532649584
sample142 -0.0409560164 0.0316282085
sample143 -0.0077355169 -0.0476895669
sample144 0.0173240816 -0.0156777551
sample145 0.0485474837 0.1202772208
sample146 0.0419645429 -0.0811283268
sample147 -0.0977308528 -0.0274843422
sample148 0.0368256320 0.0803980163
sample149 -0.0072865836 -0.1532984500
sample150 0.1020825276 0.0624772293
sample151 0.0305399154 -0.0289275317
sample152 -0.0533594779 -0.0638307827
sample153 -0.0891627098 0.1799584486
sample154 -0.0727557617 -0.0834162444
sample155 -0.0880668695 -0.0220821987
sample156 -0.0276561152 -0.0326626450
sample157 -0.1155032218 0.0183615140
sample158 -0.0281507562 -0.0104939936
sample159 0.0663235800 0.0443838678
sample160 -0.0302643886 0.0404263728
sample161 0.0114715688 -0.0591022647
sample162 -0.1337086942 0.1398135696
sample163 0.1330124655 0.1688781871
sample164 -0.0150335999 0.0028418174
sample165 0.0076520339 -0.0164127537
sample166 0.0367794508 0.0630664378
sample167 0.1111988821 0.0030057439
sample168 -0.0672981537 0.0446279919
sample169 -0.0413005037 0.0224391694
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 -0.0420517236 0.0867862953
sample2 -0.0820827591 -0.0410978468
sample3 0.0155896661 -0.0195182177
sample4 -0.1001336848 -0.0410787168
sample5 -0.0153465333 -0.0253259775
sample6 0.0340329944 -0.0408223195
sample7 0.0722578696 0.0002332633
sample8 -0.0457494303 -0.0370016673
sample9 -0.0086250836 0.0820184924
sample10 -0.0423597076 -0.0083923551
sample11 0.0022546486 0.0787766137
sample12 0.0322107192 0.1479824745
sample13 -0.0293885983 -0.0306748899
sample14 0.0337485133 -0.0367506791
sample15 0.0815538116 0.1275622900
sample16 0.0508457899 0.0540604660
sample17 0.0062598673 0.0041023659
sample18 0.0705641680 -0.0351047424
sample19 -0.0476844467 -0.0509598207
sample20 0.0522959751 0.0715522235
sample21 -0.0119120773 -0.0376093383
sample22 0.0724390027 -0.0095624645
sample23 -0.0992532283 0.0134288308
sample24 -0.1595111100 0.0728660954
sample25 -0.0920694905 -0.0749757643
sample26 -0.0595538825 0.0848965678
sample27 0.0826481299 -0.0086734843
sample28 -0.0384786019 0.0440966593
sample29 0.0777668219 0.1735308996
sample30 0.1229471149 -0.0819004902
sample31 0.0579850956 -0.0238644661
sample32 0.0970394476 -0.0111425880
sample33 0.1017588392 -0.0630442089
sample34 0.0637923834 0.0377941972
sample35 0.0789983557 -0.0229722776
sample36 0.1224939599 -0.1274954300
sample37 0.1798819508 -0.1673426440
sample38 0.0466300207 0.0888161323
sample39 -0.0168687310 0.0421533645
sample40 0.1756390870 -0.1526641395
sample41 0.0042366049 0.0004929012
sample42 -0.0447850869 -0.0651505176
sample43 0.0482309215 -0.0253529069
sample44 -0.1986709736 -0.0545779032
sample45 -0.0741832505 0.0054702754
sample46 0.0478767791 -0.0007071610
sample47 0.0608187164 0.0481622985
sample48 -0.1381490614 0.0578287139
sample49 -0.0530515070 -0.1405533285
sample50 -0.0173807251 0.1602389849
sample51 0.0462566089 0.0303473867
sample52 0.0280068384 0.0280388413
sample53 0.0667627379 0.0237702124
sample54 0.0121834623 -0.0521354295
sample55 0.0182396092 0.0221328519
sample56 -0.0001252965 0.0030907261
sample57 0.0316680373 0.0530190255
sample58 0.0393919493 -0.0297798601
sample59 0.1278291868 -0.0546527345
sample60 0.1486986107 0.1069157263
sample61 0.0793125182 0.0569796814
sample62 0.1172800005 -0.0149197874
sample63 -0.0028722568 0.1300519619
sample64 0.0237368034 0.1073287681
sample65 -0.0126534482 0.0589808344
sample66 -0.0468192873 -0.0771072989
sample67 0.1494263528 -0.0769859468
sample68 0.0977958138 -0.0577350432
sample69 0.0403087146 0.0156042305
sample70 0.0221528000 0.0315441173
sample71 -0.0546439737 -0.0272396500
sample72 0.1107487145 -0.0537318794
sample73 0.0906761478 0.0579967027
sample74 0.0586557466 0.0121421874
sample75 0.0390492314 0.0349283036
sample76 -0.0022961766 -0.1676558727
sample77 -0.0232095970 -0.2067302884
sample78 -0.0929752243 -0.0434940048
sample79 -0.1619502527 -0.0378114791
sample80 0.0680364180 0.1424663860
sample81 -0.0530786961 -0.0358350994
sample82 0.0266820551 -0.0577444911
sample83 0.1517234854 -0.0448553578
sample84 -0.0570968302 -0.0273813468
sample85 0.1086290594 -0.1228118798
sample86 0.0833858370 -0.0442914508
sample87 0.0022017288 -0.0943906773
sample88 -0.0078222343 -0.1140506633
sample89 0.0611059795 -0.0094584953
sample90 0.0022927426 -0.0936253948
sample91 0.0433581459 0.3205983311
sample92 -0.1815341361 -0.0334680933
sample93 0.0267629604 0.0614429183
sample94 0.0181876598 0.0605090518
sample95 -0.0720378764 -0.0013045891
sample96 -0.0559716871 -0.0118791616
sample97 -0.0217410541 0.0195413999
sample98 0.0379176310 0.0588357317
sample99 -0.0792423270 -0.0151274356
sample100 0.0222117226 -0.0023321362
sample101 -0.0387235764 0.1224226281
sample102 -0.2094613584 -0.0516443659
sample103 0.0138477403 0.0301052168
sample104 -0.0807988963 -0.0162719225
sample105 -0.0520493433 -0.1229665393
sample106 -0.0192611820 -0.0185238333
sample107 0.0319017300 0.0405123419
sample108 -0.0140691829 0.0163421338
sample109 -0.1831933748 0.0613006799
sample110 -0.0292790992 -0.0199849205
sample111 -0.1423255925 0.0327339777
sample112 0.0426333824 -0.0029083270
sample113 -0.0771903162 0.0268733210
sample114 -0.0241645084 -0.0184080395
sample115 -0.1959018551 0.0460129832
sample116 -0.1394477915 -0.0530806390
sample117 -0.1672364301 -0.1386537083
sample118 -0.0448344909 -0.0117622120
sample119 -0.0910395985 0.2217433288
sample120 -0.0331391582 -0.0057274694
sample121 0.0307577790 0.1392506553
sample122 -0.0839778347 -0.0291994941
sample123 0.0239649166 -0.0642163547
sample124 -0.0909149340 0.0130418994
sample125 -0.0065350288 -0.1092631852
sample126 0.0935313774 0.1368284386
sample127 0.0035387010 0.0292755669
sample128 -0.0660292743 0.1018565867
sample129 0.0693636965 -0.0695421329
sample130 0.0008492255 -0.0669704273
sample131 0.0431024618 0.0174065050
sample132 -0.0637037676 0.0029374301
sample133 -0.0289496288 -0.0390818900
sample134 0.0446206071 0.0456334604
sample135 0.0712336636 0.0521635304
sample136 0.0596268622 0.0197299691
sample137 0.0793150790 -0.0380627875
sample138 -0.0973550482 -0.0454218643
sample139 0.0539906657 -0.1534327141
sample140 0.0850824747 0.0955815015
sample141 -0.0192679718 -0.0554450242
sample142 -0.0672260394 -0.0461321289
sample143 -0.0303731604 -0.0519260316
sample144 -0.0089365290 0.0145814895
sample145 -0.0638764592 0.0122257907
sample146 0.0585852991 0.0063083758
sample147 0.0894132662 -0.1124615221
sample148 -0.0216363318 -0.0615967357
sample149 -0.0515425936 -0.0839903511
sample150 0.0568286079 -0.0124468747
sample151 -0.0789533339 -0.0261831525
sample152 -0.0330756496 0.1306443523
sample153 -0.1751924545 0.1497730977
sample154 0.0421421053 -0.0037009860
sample155 0.0680176427 0.0095711598
sample156 0.0388909182 0.1057563201
sample157 0.0314769628 0.0561367554
sample158 0.0329620050 0.0353947499
sample159 -0.0398414311 -0.1007374036
sample160 0.0424940391 0.0108496321
sample161 -0.0888372885 -0.0679700515
sample162 -0.0027471154 0.1237843640
sample163 -0.0126099026 0.0725434022
sample164 -0.0566779295 -0.0458324460
sample165 -0.0315336647 -0.0236362479
sample166 -0.0612055572 -0.0425233430
sample167 0.0142729888 0.0179308337
sample168 -0.0169501512 -0.0769618045
sample169 0.0675081198 0.0131505624
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 -0.0012329584 -1.635717e-01
sample2 -0.0724349962 -6.021227e-03
sample3 -0.0188460461 -1.080036e-01
sample4 0.0390145356 3.114417e-04
sample5 0.1774811671 -2.996383e-02
sample6 -0.0451444357 -3.455857e-02
sample7 -0.0226466343 -7.020197e-03
sample8 -0.1033680091 -9.856739e-03
sample9 0.1350011657 8.979098e-02
sample10 0.1259887344 -5.097849e-02
sample11 0.0979788283 7.086533e-02
sample12 -0.0863019040 -8.620317e-02
sample13 -0.1381401090 1.828007e-01
sample14 -0.0615073832 -2.642803e-02
sample15 0.0381598895 -3.101666e-02
sample16 -0.0048776644 1.271872e-03
sample17 -0.0788480876 -1.547552e-02
sample18 -0.0884188734 -3.795487e-02
sample19 0.0703044451 -1.084004e-01
sample20 -0.0025585665 7.975871e-02
sample21 0.0941601828 -4.126735e-02
sample22 -0.0550273502 -7.806748e-02
sample23 0.0679495393 -4.102003e-02
sample24 -0.1310962569 1.649310e-01
sample25 0.0113585339 -4.426861e-02
sample26 -0.1402945815 2.016546e-02
sample27 0.0261560960 1.588387e-03
sample28 -0.0724198648 5.850596e-02
sample29 -0.0330058701 2.060779e-03
sample30 -0.0228752639 -2.015433e-02
sample31 -0.0635067863 -6.670332e-02
sample32 0.0685099635 -4.955274e-02
sample33 -0.0777765230 -1.272079e-01
sample34 0.0157842421 -3.024314e-02
sample35 -0.0529632926 1.500972e-01
sample36 0.0070900515 2.025307e-01
sample37 -0.0442420898 1.802088e-01
sample38 -0.0781511400 -3.676425e-02
sample39 0.0120331918 -3.388840e-02
sample40 -0.0473292353 1.471561e-01
sample41 0.0228189315 -2.673558e-02
sample42 -0.0245360190 -7.960866e-02
sample43 0.1036362832 -8.229577e-02
sample44 -0.1012228589 7.049459e-02
sample45 0.0013732223 -2.450905e-02
sample46 -0.0558510142 2.947337e-03
sample47 -0.0380481265 4.554171e-02
sample48 0.0784342143 4.888984e-02
sample49 -0.0605163820 -1.162351e-02
sample50 0.0530079144 -2.737937e-02
sample51 0.1514646570 5.678348e-02
sample52 0.1860935208 1.246717e-01
sample53 -0.0064177008 -2.700991e-02
sample54 0.0697038369 -2.308388e-02
sample55 0.1633577009 1.366442e-02
sample56 0.1011485139 4.682208e-02
sample57 0.1730374205 1.609604e-01
sample58 -0.0071384702 -1.666955e-02
sample59 -0.0030461797 3.005282e-02
sample60 0.0215834893 2.665877e-01
sample61 0.1510583578 1.002385e-01
sample62 -0.0925534036 -4.845846e-02
sample63 -0.0596311665 -4.137019e-02
sample64 -0.0449225747 -2.600566e-03
sample65 0.0939383812 -4.406907e-02
sample66 0.1063400857 -5.709989e-02
sample67 -0.0201590331 2.361727e-01
sample68 0.0037203024 2.418383e-02
sample69 -0.0645161180 -1.155622e-01
sample70 -0.1013440023 -1.351789e-01
sample71 -0.0016467937 -2.976844e-02
sample72 0.0328892936 -2.835861e-02
sample73 0.0275080030 -5.148187e-02
sample74 0.1341719731 -7.895279e-02
sample75 0.0951575629 -3.943185e-02
sample76 -0.0864722033 3.034989e-02
sample77 -0.1035749567 -2.545354e-02
sample78 -0.1575644006 4.939599e-02
sample79 0.0189137037 4.874679e-02
sample80 0.1384140510 4.263328e-05
sample81 -0.0118846450 -6.357932e-02
sample82 -0.1675308225 3.533909e-02
sample83 -0.0065673490 -7.812614e-02
sample84 0.1486891646 -3.109055e-02
sample85 -0.0532724562 7.417880e-02
sample86 -0.1138477423 -1.919076e-05
sample87 0.0432863915 6.080471e-02
sample88 0.0433450340 1.402491e-01
sample89 0.0331205811 -1.395400e-02
sample90 -0.0607412791 -8.610415e-02
sample91 -0.0566272946 1.303746e-01
sample92 -0.0359582564 1.061604e-01
sample93 -0.0433646360 -4.443636e-02
sample94 -0.0477291273 -1.059574e-01
sample95 -0.0249595765 -3.980526e-02
sample96 0.0035219060 -9.293928e-02
sample97 -0.0066048640 -1.527231e-01
sample98 0.0020366806 -5.579551e-02
sample99 -0.0886615894 -3.728220e-02
sample100 -0.1091259119 -3.560421e-02
sample101 -0.0739726577 -4.318003e-02
sample102 0.0574461349 -2.783907e-02
sample103 0.0142730925 9.705527e-03
sample104 0.0710395199 4.068352e-02
sample105 0.0980831399 -3.452951e-02
sample106 -0.0254259271 3.628986e-02
sample107 -0.0160653394 -9.173394e-02
sample108 -0.0200987634 -2.379692e-02
sample109 -0.0389780606 1.692361e-02
sample110 -0.0326304832 2.988110e-02
sample111 0.0676937624 -6.038211e-02
sample112 0.0167883429 5.336939e-03
sample113 0.0969217126 -2.757600e-02
sample114 -0.0026398366 -9.209159e-02
sample115 -0.0308047236 1.603826e-02
sample116 -0.1240307200 1.273000e-01
sample117 0.0334729100 5.392712e-02
sample118 -0.1037152944 6.252430e-02
sample119 -0.1064176870 1.196202e-01
sample120 -0.0771354999 -1.004932e-01
sample121 -0.0129350698 3.181978e-02
sample122 0.0847492459 -5.568321e-02
sample123 -0.0041336823 7.693165e-03
sample124 -0.0583457851 -8.396386e-02
sample125 0.0634844648 -5.232539e-02
sample126 -0.0662580910 -1.091733e-01
sample127 -0.0865024570 -1.094176e-01
sample128 -0.0627817271 -1.470958e-02
sample129 -0.0336276518 -4.007862e-02
sample130 -0.0293517735 -8.046118e-02
sample131 -0.0469197697 -2.209764e-03
sample132 -0.0241740502 -1.248598e-01
sample133 0.0907303191 1.466701e-02
sample134 -0.0350842110 7.539662e-02
sample135 0.0001333352 9.185361e-03
sample136 -0.0335876106 -9.860277e-02
sample137 -0.0640148969 -7.554474e-02
sample138 0.0060964874 -1.742761e-02
sample139 -0.0592084521 5.614967e-02
sample140 0.0427985830 -1.099554e-02
sample141 0.0618796496 -9.301035e-02
sample142 0.0898554540 3.573421e-02
sample143 0.0817389150 8.880524e-02
sample144 0.0787754793 -3.821391e-02
sample145 0.1085821656 1.569477e-01
sample146 -0.0589558046 -4.373365e-02
sample147 -0.0495330534 7.277168e-03
sample148 0.1161592875 9.079120e-03
sample149 -0.0121579596 7.788370e-02
sample150 -0.0314512560 3.520212e-02
sample151 0.0575382218 -1.945351e-02
sample152 -0.0494542161 7.025536e-02
sample153 -0.0941332530 2.153298e-01
sample154 -0.0335932123 2.078725e-02
sample155 0.0690457598 -2.780412e-02
sample156 0.1039901595 -6.292526e-02
sample157 -0.0408645783 8.065515e-03
sample158 0.1018105269 7.816870e-03
sample159 -0.0281730466 -1.207204e-02
sample160 0.1643053014 2.978116e-03
sample161 0.0374329225 8.524611e-02
sample162 -0.0804535219 8.349759e-02
sample163 -0.0743227815 -1.406220e-02
sample164 0.1208806075 -2.139457e-02
sample165 0.1608115914 2.025193e-02
sample166 -0.0425944520 -2.660711e-02
sample167 -0.0226849480 -4.464283e-02
sample168 -0.0180735519 -7.465987e-04
sample169 0.0190778961 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="",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="",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="",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
> # Combined plot of scores for common part. DISCO.
> plotRes(object=discoRes,comps=c(1,1),what="scores",type="common",
+ combined=TRUE,block="",color="classname")
Warning message:
In plotRes(object = discoRes, comps = c(1, 1), what = "scores", :
It is not possible to combine common components in DISCO-SCA approach.
>
>
> # 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="",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=TRUE,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=TRUE,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=TRUE,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=TRUE,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))
>
> # Combined plot for loadings for common part. DISCO-SCA.
> plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+ combined=TRUE,block="",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
> # 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 for distinctive part
> plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+ combined=TRUE,block="",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
> # Combined plot for common and distinctive part (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+ combined=TRUE,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=TRUE,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))
>
>
>
> #########################
> ## PART 5. Biplot results
>
> ## Common components DISCO-SCA
> biplotRes(object=discoRes,type="common",comps=c(1,2),block="",title=NULL,
+ colorCol="classname",sizeValues=c(2,4),shapeValues=c(17,0),
+ background=TRUE,pointSize=4,labelSize=NULL,axisSize=NULL,
+ titleSize=NULL)
>
>
> ## Common components O2PLS
> p1 <- biplotRes(object=o2plsRes,type="common",comps=c(1,2),block="expr",title=NULL,
+ colorCol="classname",sizeValues=c(2,4),shapeValues=c(17,0),
+ background=TRUE,pointSize=4,labelSize=NULL,axisSize=NULL,
+ titleSize=NULL)
Warning message:
In data.row.names(row.names, rowsi, i) :
some row.names duplicated: 170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338 --> row.names NOT used
> p2 <- biplotRes(object=o2plsRes,type="common",comps=c(1,2),block="mirna",title=NULL,
+ colorCol="classname",sizeValues=c(2,4),shapeValues=c(17,0),
+ background=TRUE,pointSize=4,labelSize=NULL,axisSize=NULL,titleSize=NULL)
Warning message:
In data.row.names(row.names, rowsi, i) :
some row.names duplicated: 170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338 --> row.names NOT used
> 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))
>
> ## Distintive components DISCO-SCA
> p1 <- biplotRes(object=discoRes,type="individual",comps=c(1,2),block="expr",title=NULL,
+ colorCol="classname",sizeValues=c(2,4),shapeValues=c(17,0),
+ background=TRUE,pointSize=4,labelSize=NULL,axisSize=NULL,
+ titleSize=NULL)
Warning message:
In data.row.names(row.names, rowsi, i) :
some row.names duplicated: 170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338 --> row.names NOT used
> p2 <- biplotRes(object=discoRes,type="individual",comps=c(1,2),block="mirna",title=NULL,
+ colorCol="classname",sizeValues=c(2,4),shapeValues=c(17,0),
+ background=TRUE,pointSize=4,labelSize=NULL,axisSize=NULL,
+ titleSize=NULL)
Warning message:
In data.row.names(row.names, rowsi, i) :
some row.names duplicated: 170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338 --> row.names NOT used
> 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))
>
>
>
> proc.time()
user system elapsed
19.78 0.39 20.29
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STATegRa.Rcheck/examples_i386/STATegRa-Ex.timings
|
STATegRa.Rcheck/examples_x64/STATegRa-Ex.timings
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