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CHECK report for STATegRa on tokay1

This page was generated on 2021-05-06 12:33:48 -0400 (Thu, 06 May 2021).

To the developers/maintainers of the STATegRa package:
Please make sure to use the following settings in order to reproduce any error or warning you see on this page.
Package 1800/1974HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
STATegRa 1.26.0  (landing page)
David Gomez-Cabrero , NĂºria Planell
Snapshot Date: 2021-05-05 14:51:38 -0400 (Wed, 05 May 2021)
URL: https://git.bioconductor.org/packages/STATegRa
Branch: RELEASE_3_12
Last Commit: 064ea6c
Last Changed Date: 2020-10-27 11:00:09 -0400 (Tue, 27 Oct 2020)
malbec1Linux (Ubuntu 18.04.5 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version exists in internal repository
tokay1Windows Server 2012 R2 Standard / x64  OK    OK    OK    OK  UNNEEDED, same version exists in internal repository
merida1macOS 10.14.6 Mojave / x86_64  OK    OK    OK    OK  UNNEEDED, same version exists in internal repository

Summary

Package: STATegRa
Version: 1.26.0
Command: C:\Users\biocbuild\bbs-3.12-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=C:\Users\biocbuild\bbs-3.12-bioc\R\library --no-vignettes --timings STATegRa_1.26.0.tar.gz
StartedAt: 2021-05-06 07:18:36 -0400 (Thu, 06 May 2021)
EndedAt: 2021-05-06 07:25:26 -0400 (Thu, 06 May 2021)
EllapsedTime: 410.0 seconds
RetCode: 0
Status:   OK   
CheckDir: STATegRa.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\Users\biocbuild\bbs-3.12-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=C:\Users\biocbuild\bbs-3.12-bioc\R\library --no-vignettes --timings STATegRa_1.26.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory 'C:/Users/biocbuild/bbs-3.12-bioc/meat/STATegRa.Rcheck'
* using R version 4.0.5 (2021-03-31)
* 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 '1.26.0'
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'STATegRa' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking 'build' directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* loading checks for arch 'i386'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* loading checks for arch 'x64'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
modelSelection,list-numeric-character: no visible binding for global
  variable 'components'
modelSelection,list-numeric-character: no visible binding for global
  variable 'mylabel'
plotVAF,caClass: no visible binding for global variable 'comp'
plotVAF,caClass: no visible binding for global variable 'VAF'
plotVAF,caClass: no visible binding for global variable 'block'
selectCommonComps,list-numeric: no visible binding for global variable
  'comps'
selectCommonComps,list-numeric: no visible binding for global variable
  'block'
selectCommonComps,list-numeric: no visible binding for global variable
  'comp'
selectCommonComps,list-numeric: no visible binding for global variable
  'ratio'
Undefined global functions or variables:
  VAF block comp components comps mylabel ratio
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of 'data' directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking files in 'vignettes' ... OK
* checking examples ...
** running examples for arch 'i386' ... OK
Examples with CPU (user + system) or elapsed time > 5s
        user system elapsed
plotRes 5.19   0.07    5.25
** running examples for arch 'x64' ... OK
Examples with CPU (user + system) or elapsed time > 5s
                  user system elapsed
plotRes           6.93   0.06    6.98
omicsCompAnalysis 5.34   0.20    5.55
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
** running tests for arch 'i386' ...
  Running 'STATEgRa_Example.omicsCLUST.R'
  Running 'STATEgRa_Example.omicsPCA.R'
  Running 'STATegRa_Example.omicsNPC.R'
  Running 'runTests.R'
 OK
** running tests for arch 'x64' ...
  Running 'STATEgRa_Example.omicsCLUST.R'
  Running 'STATEgRa_Example.omicsPCA.R'
  Running 'STATegRa_Example.omicsNPC.R'
  Running 'runTests.R'
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in 'inst/doc' ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 1 NOTE
See
  'C:/Users/biocbuild/bbs-3.12-bioc/meat/STATegRa.Rcheck/00check.log'
for details.



Installation output

STATegRa.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\cygwin\bin\curl.exe -O http://172.29.0.3/BBS/3.12/bioc/src/contrib/STATegRa_1.26.0.tar.gz && rm -rf STATegRa.buildbin-libdir && mkdir STATegRa.buildbin-libdir && C:\Users\biocbuild\bbs-3.12-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=STATegRa.buildbin-libdir STATegRa_1.26.0.tar.gz && C:\Users\biocbuild\bbs-3.12-bioc\R\bin\R.exe CMD INSTALL STATegRa_1.26.0.zip && rm STATegRa_1.26.0.tar.gz STATegRa_1.26.0.zip
###
##############################################################################
##############################################################################


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

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100 3179k  100 3179k    0     0  14.6M      0 --:--:-- --:--:-- --:--:-- 14.7M

install for i386

* installing *source* package 'STATegRa' ...
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
  converting help for package 'STATegRa'
    finding HTML links ... done
    STATegRa-defunct                        html  
    STATegRa                                html  
    STATegRaUsersGuide                      html  
    STATegRa_data                           html  
    STATegRa_data_TCGA_BRCA                 html  
    bioDist                                 html  
    bioDistFeature                          html  
    bioDistFeaturePlot                      html  
    bioDistW                                html  
    bioDistWPlot                            html  
    bioDistclass                            html  
    bioMap                                  html  
    caClass-class                           html  
    combiningMappings                       html  
    createOmicsExpressionSet                html  
    getInitialData                          html  
    getLoadings                             html  
    getMethodInfo                           html  
    getPreprocessing                        html  
    getScores                               html  
    getVAF                                  html  
    holistOmics                             html  
    modelSelection                          html  
    finding level-2 HTML links ... done

    omicsCompAnalysis                       html  
    omicsNPC                                html  
    plotRes                                 html  
    plotVAF                                 html  
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path

install for x64

* installing *source* package 'STATegRa' ...
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'STATegRa' as STATegRa_1.26.0.zip
* DONE (STATegRa)
* installing to library 'C:/Users/biocbuild/bbs-3.12-bioc/R/library'
package 'STATegRa' successfully unpacked and MD5 sums checked

Tests output

STATegRa.Rcheck/tests_i386/runTests.Rout


R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> BiocGenerics:::testPackage("STATegRa")
Common components
[1] 2

Distinctive components
[[1]]
[1] 0

[[2]]
[1] 0

Common components
[1] 2

Distinctive components
[[1]]
[1] 1

[[2]]
[1] 1

Common components
[1] 2

Distinctive components
[[1]]
[1] 2

[[2]]
[1] 2



RUNIT TEST PROTOCOL -- Thu May 06 07:23:11 2021 
*********************************************** 
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: In rownames(pData) == colnames(exprs) :
  longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
> 
> proc.time()
   user  system elapsed 
   3.42    0.26    3.78 

STATegRa.Rcheck/tests_x64/runTests.Rout


R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> BiocGenerics:::testPackage("STATegRa")
Common components
[1] 2

Distinctive components
[[1]]
[1] 0

[[2]]
[1] 0

Common components
[1] 2

Distinctive components
[[1]]
[1] 1

[[2]]
[1] 1

Common components
[1] 2

Distinctive components
[[1]]
[1] 2

[[2]]
[1] 2



RUNIT TEST PROTOCOL -- Thu May 06 07:25:19 2021 
*********************************************** 
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: In rownames(pData) == colnames(exprs) :
  longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
> 
> proc.time()
   user  system elapsed 
   4.32    0.34    4.75 

STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsCLUST.Rout


R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> ###########################################
> ########### EXAMPLE OF THE OMICSCLUSTERING
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
> 
> #############################################
> ## PART 1: CREATING a bioMap CLASS
> #############################################
> ####### This part creates or reads the map between features.
> ####### In the present example the map is downloaded from a resource.
> #######   then the class is created.
> 
> #load("../data/STATegRa_S2.rda")
> data(STATegRa_S2)
> 
> MAP.SYMBOL<-bioMap(name = "Symbol-miRNA",
+                 metadata =  list(type_v1="Gene",type_v2="miRNA",
+                                  source_database="targetscan.Hs.eg.db",
+                                  data_extraction="July2014"),
+                 map=mapdata)
> 
> 
> #############################################
> ## PART 2: CREATING a bioDist CLASS
> #############################################
> ##### In the second part given a set of main features and surrogate feautres,
> #####    the profile of the main features is computed through the surrogate features.
> 
> # Load Data
> data(STATegRa_S1)
>   #load("../data/STATegRa.S1.Rdata")
> 
> ## Create ExpressionSets
> #  source("../R/STATegRa_omicsPCA_classes_and_methods.R")
> # Block1 - Expression data
> mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname"))
> 
> # Create Gene-gene distance computed through miRNA data
> bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1),     
+              reference = "Var1",
+              mapping = MAP.SYMBOL,
+              surrogateData = miRNA.ds,  ### miRNA data
+              referenceData = mRNA.ds,  ### mRNA data
+              maxitems=2,
+              selectionRule="sd",
+              expfac=NULL,
+              aggregation = "sum",
+              distance = "spearman",
+              noMappingDist = 0,
+              filtering = NULL,
+              name = "mRNAbymiRNA")
> 
> require(Biobase)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
    tapply, union, unique, unsplit, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> 
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+                  distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+                  map.name = "id",
+                  map.metadata = list(),
+                  params = list())
> 
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
> 
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
> 
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+                        bioDistList = bioDistList,
+                        weights=weights)
> length(bioDistWList)
[1] 4
> 
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
> 
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+                listDistW = bioDistWList,
+                method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
4: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
5: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
6: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
7: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
> 
> #############################################
> ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
> #############################################
> 
> ## IDH1
> 
> IDH1.F<-bioDistFeature(Feature = "IDH1" ,
+                        listDistW = bioDistWList,
+                        threshold.cor=0.7)
> bioDistFeaturePlot(data=IDH1.F)
> 
> ## PDGFRA
> 
> #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
> #                       listDistW = bioDistWList,
> #                       threshold.cor=0.7)
> #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
> 
> ## EGFR
> #EGFR.F<-bioDistFeature(Feature = "EGFR" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.7)
> #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
> 
> ## MGMT
> #MGMT.F<-bioDistFeature(Feature = "MGMT" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.5)
> #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
  38.43    0.95   39.46 

STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsCLUST.Rout


R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> ###########################################
> ########### EXAMPLE OF THE OMICSCLUSTERING
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
> 
> #############################################
> ## PART 1: CREATING a bioMap CLASS
> #############################################
> ####### This part creates or reads the map between features.
> ####### In the present example the map is downloaded from a resource.
> #######   then the class is created.
> 
> #load("../data/STATegRa_S2.rda")
> data(STATegRa_S2)
> 
> MAP.SYMBOL<-bioMap(name = "Symbol-miRNA",
+                 metadata =  list(type_v1="Gene",type_v2="miRNA",
+                                  source_database="targetscan.Hs.eg.db",
+                                  data_extraction="July2014"),
+                 map=mapdata)
> 
> 
> #############################################
> ## PART 2: CREATING a bioDist CLASS
> #############################################
> ##### In the second part given a set of main features and surrogate feautres,
> #####    the profile of the main features is computed through the surrogate features.
> 
> # Load Data
> data(STATegRa_S1)
>   #load("../data/STATegRa.S1.Rdata")
> 
> ## Create ExpressionSets
> #  source("../R/STATegRa_omicsPCA_classes_and_methods.R")
> # Block1 - Expression data
> mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname"))
> 
> # Create Gene-gene distance computed through miRNA data
> bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1),     
+              reference = "Var1",
+              mapping = MAP.SYMBOL,
+              surrogateData = miRNA.ds,  ### miRNA data
+              referenceData = mRNA.ds,  ### mRNA data
+              maxitems=2,
+              selectionRule="sd",
+              expfac=NULL,
+              aggregation = "sum",
+              distance = "spearman",
+              noMappingDist = 0,
+              filtering = NULL,
+              name = "mRNAbymiRNA")
> 
> require(Biobase)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
    tapply, union, unique, unsplit, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> 
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+                  distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+                  map.name = "id",
+                  map.metadata = list(),
+                  params = list())
> 
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
> 
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
> 
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+                        bioDistList = bioDistList,
+                        weights=weights)
> length(bioDistWList)
[1] 4
> 
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
> 
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+                listDistW = bioDistWList,
+                method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
4: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
5: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
6: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
7: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
> 
> #############################################
> ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
> #############################################
> 
> ## IDH1
> 
> IDH1.F<-bioDistFeature(Feature = "IDH1" ,
+                        listDistW = bioDistWList,
+                        threshold.cor=0.7)
> bioDistFeaturePlot(data=IDH1.F)
> 
> ## PDGFRA
> 
> #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
> #                       listDistW = bioDistWList,
> #                       threshold.cor=0.7)
> #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
> 
> ## EGFR
> #EGFR.F<-bioDistFeature(Feature = "EGFR" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.7)
> #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
> 
> ## MGMT
> #MGMT.F<-bioDistFeature(Feature = "MGMT" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.5)
> #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
  25.21    0.82   26.17 

STATegRa.Rcheck/tests_i386/STATegRa_Example.omicsNPC.Rout


R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> 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 
  73.48    0.34   73.90 

STATegRa.Rcheck/tests_x64/STATegRa_Example.omicsNPC.Rout


R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 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 
  81.42    0.29   81.79 

STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsPCA.Rout


R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> ###########################################
> ########### EXAMPLE OF THE OMICSPCA
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
> 
> # g_legend (not exported by STATegRa any more)
> ## code from https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs
> g_legend<-function(a.gplot){
+     tmp <- ggplot_gtable(ggplot_build(a.gplot))
+     leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
+     legend <- tmp$grobs[[leg]]
+     return(legend)}
> 
> #########################
> ## PART 1. Load data
> 
> ## Load data
> data(STATegRa_S3)
> 
> ls()
[1] "Block1.PCA" "Block2.PCA" "ed.PCA"     "g_legend"  
> 
> ## Create ExpressionSets
> # Block1 - Expression data
> B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname"))
> 
> #########################
> ## PART 2.  Model Selection
> 
> require(grid)
Loading required package: grid
> require(gridExtra)
Loading required package: gridExtra
> require(ggplot2)
Loading required package: ggplot2
> 
> ## Select the optimal components
> ms <- modelSelection(Input=list(B1,B2),Rmax=4,fac.sel="single%",varthreshold=0.03,center=TRUE,scale=TRUE,weight=TRUE)
Common components
[1] 2

Distinctive components
[[1]]
[1] 2

[[2]]
[1] 2

> 
> 
> #########################
> ## PART 3. Component Analysis
> 
> ## 3.1 Component analysis of the three methods
> discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                               scale=TRUE,weight=TRUE)
> jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                              scale=TRUE,weight=TRUE)
> o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                               scale=TRUE,weight=TRUE)
> 
> ## 3.2 Exploring scores structures
> 
> # Exploring DISCO-SCA scores structure
> discoRes@scores$common ## Common scores
                      1             2
sample1    0.0781574276 -0.0431500955
sample2   -0.1192218337  0.0294090217
sample3   -0.0531412202 -0.0746839845
sample4    0.0292975233 -0.0005958151
sample5    0.0202091792  0.0110463906
sample6    0.1226089070  0.1053466474
sample7    0.1078928033 -0.0322477162
sample8    0.1782895399  0.1449364861
sample9    0.0468698152 -0.0455174448
sample10  -0.0036030457  0.0420111829
sample11  -0.0035566469 -0.0566292842
sample12   0.1006128880  0.0641380453
sample13  -0.1174408233  0.0907488692
sample14   0.0981203251  0.0617737530
sample15   0.0085334238 -0.0087014961
sample16   0.0783148707  0.1581293491
sample17  -0.1483609904  0.0638581999
sample18  -0.0963086300  0.0556639013
sample19  -0.0217244120 -0.0720085095
sample20  -0.0635636463 -0.0779654242
sample21  -0.0201840248  0.1566391588
sample22   0.0218268603 -0.0764105598
sample23   0.0852042090 -0.0032686898
sample24  -0.1287170371  0.1924546472
sample25  -0.0430574116 -0.0456564290
sample26  -0.1453896774  0.0541513495
sample27  -0.0197488935 -0.1185658424
sample28  -0.1025336230  0.0650686284
sample29   0.0706018390 -0.0682989529
sample30  -0.1295627640 -0.0066771583
sample31   0.1147449116  0.1232685639
sample32  -0.0374310938  0.0380176163
sample33   0.0599515871  0.0136865528
sample34  -0.0984200853  0.0375319963
sample35  -0.0543098413 -0.0378107733
sample36   0.1403625393 -0.0343759121
sample37   0.0228941750 -0.0732850246
sample38  -0.0222077365 -0.0962595509
sample39  -0.0941738475  0.0215199662
sample40   0.0643801018 -0.0687874816
sample41  -0.0327638101 -0.1232188183
sample42  -0.0500431849 -0.0292472286
sample43  -0.0184498861  0.0233010235
sample44   0.1487899011  0.1171357979
sample45  -0.1050774062  0.1123203109
sample46  -0.1151195823 -0.1094029590
sample47  -0.0962593782 -0.0288464915
sample48   0.0004837444 -0.0310275304
sample49   0.1135207884  0.1213973955
sample50  -0.0123553210 -0.1740743238
sample51   0.0550529941  0.1258885513
sample52   0.0499121310  0.0728543520
sample53   0.1119773681  0.1588012553
sample54  -0.0360055671  0.0228575340
sample55   0.0210419006  0.0006731191
sample56  -0.0434169167  0.0633125891
sample57   0.0197824728  0.1150712436
sample58   0.0030439872  0.0326097232
sample59   0.0500253034  0.0129416127
sample60   0.0184278639  0.0136081682
sample61   0.0150299439  0.0635023861
sample62  -0.0304764032 -0.0201321485
sample63   0.1102252531  0.1285977182
sample64   0.1552588118  0.0971167878
sample65  -0.0058503036  0.0207115781
sample66  -0.0025605291  0.0424320917
sample67   0.1546634738 -0.0661720254
sample68   0.0536369148 -0.0923685899
sample69   0.0640330294  0.0081982550
sample70   0.0163517629 -0.0663230132
sample71  -0.0102537674 -0.1345920168
sample72  -0.0654196153 -0.0196121832
sample73  -0.1048556215  0.0220936547
sample74   0.0123799453  0.0586113954
sample75   0.0392077891 -0.0209755859
sample76   0.0648953350 -0.0524764508
sample77   0.1172922112 -0.0201186272
sample78  -0.1463067975  0.0708474487
sample79   0.0265211254 -0.1603304648
sample80   0.0279737101 -0.0214206622
sample81   0.0079211470 -0.0738449568
sample82  -0.1544236562 -0.0361468377
sample83  -0.0494211560 -0.0050051593
sample84  -0.0259038436 -0.0346548391
sample85   0.1116484277 -0.0031500392
sample86  -0.1306483144 -0.0377216862
sample87  -0.0554778204 -0.0459749204
sample88  -0.0301623760  0.0382197281
sample89  -0.1016866726  0.0694032543
sample90   0.0086819822 -0.0201319966
sample91   0.1578625213 -0.2097829145
sample92   0.0170936923 -0.1655803192
sample93  -0.0979806873 -0.0121512585
sample94   0.0131484028 -0.0114932145
sample95   0.0315682626 -0.0758857395
sample96   0.0024125601 -0.0470134083
sample97   0.0634545391  0.0270332762
sample98  -0.0359374700 -0.0135489089
sample99  -0.1009163208  0.1124782058
sample100  0.0551753098  0.0246489214
sample101 -0.0080118988 -0.1627367376
sample102 -0.0046444120  0.0095636726
sample103 -0.0472523245 -0.0940393603
sample104  0.0198159544 -0.0591089929
sample105 -0.0400237763 -0.0160910859
sample106 -0.0923808365  0.0369018140
sample107 -0.1019373999  0.0224953776
sample108 -0.0877091661 -0.0128833725
sample109  0.0864824498 -0.0900937491
sample110 -0.1223115511 -0.0096085020
sample111  0.0257354688 -0.0936165704
sample112 -0.0765286622  0.0270346589
sample113  0.0258803340  0.0377499028
sample114  0.0021138850 -0.0882014087
sample115  0.0303460340 -0.0723581076
sample116  0.0780508544 -0.0685063401
sample117  0.0536898227 -0.0911904787
sample118  0.0666651189 -0.0236230129
sample119  0.1021871600 -0.2324934777
sample120  0.0750216552  0.0243380206
sample121 -0.0756936353  0.0942949948
sample122 -0.0259627969  0.0731989249
sample123 -0.1037846302 -0.0369197774
sample124  0.0611207998  0.0421726059
sample125 -0.0738472721  0.0066950338
sample126  0.0972916354  0.0762638192
sample127  0.0824697568 -0.0096637045
sample128 -0.1249407536  0.0929314508
sample129 -0.0734067633 -0.0434364426
sample130 -0.0003502063 -0.0309852493
sample131  0.0930182776  0.0155936107
sample132  0.0736222868  0.0733031655
sample133 -0.0498397965 -0.0462436893
sample134  0.1644873523  0.0720004304
sample135 -0.0752297264  0.0003816125
sample136  0.0227145640 -0.0495507142
sample137  0.0564717277 -0.0288917438
sample138  0.0255988156 -0.0610854717
sample139  0.0621217780  0.0235806141
sample140 -0.0604152645 -0.0435595169
sample141  0.0246743992  0.0532649293
sample142 -0.0409560220  0.0316281277
sample143 -0.0077355180 -0.0476895907
sample144  0.0173240817 -0.0156777692
sample145  0.0485474735  0.1202771577
sample146  0.0419645499 -0.0811282542
sample147 -0.0977308460 -0.0274842175
sample148  0.0368256276  0.0803979987
sample149 -0.0072865817 -0.1532985045
sample150  0.1020825286  0.0624773133
sample151  0.0305399119 -0.0289276349
sample152 -0.0533594787 -0.0638308331
sample153 -0.0891627291  0.1799582194
sample154 -0.0727557564 -0.0834161888
sample155 -0.0880668647 -0.0220821118
sample156 -0.0276561119 -0.0326626055
sample157 -0.1155032208  0.0183615473
sample158 -0.0281507544 -0.0104939484
sample159  0.0663235767  0.0443838221
sample160 -0.0302643882  0.0404264343
sample161  0.0114715649 -0.0591023673
sample162 -0.1337087004  0.1398135582
sample163  0.1330124590  0.1688781648
sample164 -0.0150336033  0.0028417462
sample165  0.0076520319 -0.0164127858
sample166  0.0367794455  0.0630663555
sample167  0.1111988834  0.0030057597
sample168 -0.0672981559  0.0446279721
sample169 -0.0413005009  0.0224392615
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
                      1             2
sample1   -0.0420516330  0.0867863000
sample2   -0.0820827968 -0.0410978281
sample3    0.0155897790 -0.0195182276
sample4   -0.1001336957 -0.0410786959
sample5   -0.0153465576 -0.0253259732
sample6    0.0340328427 -0.0408223180
sample7    0.0722579084  0.0002332461
sample8   -0.0457496385 -0.0370016459
sample9   -0.0086250235  0.0820184915
sample10  -0.0423597642 -0.0083923431
sample11   0.0022547295  0.0787766093
sample12   0.0322106709  0.1479824727
sample13  -0.0293887362 -0.0306748764
sample14   0.0337484221 -0.0367506811
sample15   0.0815538515  0.1275622724
sample16   0.0508455838  0.0540604682
sample17   0.0062597957  0.0041023689
sample18   0.0705640992 -0.0351047533
sample19  -0.0476843490 -0.0509598170
sample20   0.0522960893  0.0715522067
sample21  -0.0119122932 -0.0376093236
sample22   0.0724391134 -0.0095624859
sample23  -0.0992532238  0.0134288513
sample24  -0.1595113608  0.0728661438
sample25  -0.0920694372 -0.0749757493
sample26  -0.0595539264  0.0848965837
sample27   0.0826482877 -0.0086735107
sample28  -0.0384786785  0.0440966722
sample29   0.0777669465  0.1735308783
sample30   0.1229471177 -0.0819005169
sample31   0.0579849282 -0.0238644684
sample32   0.0970393997 -0.0111426054
sample33   0.1017588219 -0.0630442295
sample34   0.0637923495  0.0377941865
sample35   0.0789983894 -0.0229722964
sample36   0.1224939475 -0.1274954565
sample37   0.1798819939 -0.1673426860
sample38   0.0466301771  0.0888161146
sample39  -0.0168687421  0.0421533692
sample40   0.1756391286 -0.1526641803
sample41   0.0042367750  0.0004928904
sample42  -0.0447850478 -0.0651505113
sample43   0.0482308918 -0.0253529154
sample44  -0.1986711558 -0.0545778520
sample45  -0.0741833915  0.0054702990
sample46   0.0478769346 -0.0007071800
sample47   0.0608187669  0.0481622834
sample48  -0.1381490163  0.0578287405
sample49  -0.0530517026 -0.1405533076
sample50  -0.0173804557  0.1602389747
sample51   0.0462564304  0.0303473878
sample52   0.0280067219  0.0280388425
sample53   0.0667625259  0.0237702113
sample54   0.0121834230 -0.0521354304
sample55   0.0182396041  0.0221328486
sample56  -0.0001253876  0.0030907314
sample57   0.0316678671  0.0530190293
sample58   0.0393919013 -0.0297798657
sample59   0.1278291517 -0.0546527596
sample60   0.1486985815  0.1069156982
sample61   0.0793124265  0.0569796709
sample62   0.1172800354 -0.0149198138
sample63  -0.0028724024  0.1300519728
sample64   0.0237366878  0.1073287713
sample65  -0.0126534617  0.0589808386
sample66  -0.0468193574 -0.0771072860
sample67   0.1494263899 -0.0769859811
sample68   0.0977959200 -0.0577350703
sample69   0.0403087189  0.0156042223
sample70   0.0221529143  0.0315441066
sample71  -0.0546437941 -0.0272396495
sample72   0.1107487358 -0.0537319042
sample73   0.0906761413  0.0579966851
sample74   0.0586556739  0.0121421797
sample75   0.0390492657  0.0349282940
sample76  -0.0022961450 -0.1676558761
sample77  -0.0232096129 -0.2067302850
sample78  -0.0929753201 -0.0434939805
sample79  -0.1619500528 -0.0378114578
sample80   0.0680364696  0.1424663706
sample81  -0.0530785967 -0.0358350945
sample82   0.0266821028 -0.0577445002
sample83   0.1517234952 -0.0448553903
sample84  -0.0570967895 -0.0273813377
sample85   0.1086290249 -0.1228119018
sample86   0.0833858904 -0.0442914717
sample87   0.0022017657 -0.0943906812
sample88  -0.0078223256 -0.1140506579
sample89   0.0611058906 -0.0094585029
sample90   0.0022927621 -0.0936253973
sample91   0.0433584720  0.3205983068
sample92  -0.1815339324 -0.0334680681
sample93   0.0267630020  0.0614429111
sample94   0.0181877011  0.0605090465
sample95  -0.0720377709 -0.0013045803
sample96  -0.0559716157 -0.0118791542
sample97  -0.0217410721  0.0195414059
sample98   0.0379176700  0.0588357223
sample99  -0.0792424679 -0.0151274110
sample100  0.0222116938 -0.0023321390
sample101 -0.0387233239  0.1224226229
sample102 -0.2094613805 -0.0516443218
sample103  0.0138478742  0.0301052065
sample104 -0.0807988287 -0.0162719100
sample105 -0.0520493443 -0.1229665300
sample106 -0.0192612333 -0.0185238267
sample107  0.0319017255  0.0405123361
sample108 -0.0140691535  0.0163421351
sample109 -0.1831932465  0.0613007111
sample110 -0.0292790856 -0.0199849156
sample111 -0.1423254567  0.0327339997
sample112  0.0426333487 -0.0029083340
sample113 -0.0771903632  0.0268733401
sample114 -0.0241643828 -0.0184080419
sample115 -0.1959017508  0.0460130182
sample116 -0.1394477253 -0.0530806147
sample117 -0.1672363468 -0.1386536802
sample118 -0.0448344690 -0.0117622042
sample119 -0.0910392560  0.2217433302
sample120 -0.0331391819 -0.0057274610
sample121  0.0307576822  0.1392506562
sample122 -0.0839779343 -0.0291994712
sample123  0.0239649584 -0.0642163630
sample124 -0.0909149797  0.0130419212
sample125 -0.0065350522 -0.1092631837
sample126  0.0935313126  0.1368284249
sample127  0.0035387312  0.0292755650
sample128 -0.0660293668  0.1018566070
sample129  0.0693637508 -0.0695421513
sample130  0.0008492638 -0.0669704304
sample131  0.0431024409  0.0174064976
sample132 -0.0637038544  0.0029374487
sample133 -0.0289495768 -0.0390818877
sample134  0.0446205025  0.0456334578
sample135  0.0712336772  0.0521635155
sample136  0.0596269444  0.0197299524
sample137  0.0793151179 -0.0380628065
sample138 -0.0973549752 -0.0454218489
sample139  0.0539905941 -0.1534327230
sample140  0.0850825564  0.0955814802
sample141 -0.0192680477 -0.0554450163
sample142 -0.0672260966 -0.0461321123
sample143 -0.0303731199 -0.0519260285
sample144 -0.0089365043  0.0145814901
sample145 -0.0638766436  0.0122258147
sample146  0.0585854144  0.0063083571
sample147  0.0894132866 -0.1124615432
sample148 -0.0216364602 -0.0615967245
sample149 -0.0515424131 -0.0839903521
sample150  0.0568285123 -0.0124468810
sample151 -0.0789533020 -0.0261831384
sample152 -0.0330755400  0.1306443542
sample153 -0.1751926821  0.1497731488
sample154  0.0421422198 -0.0037010015
sample155  0.0680176803  0.0095711436
sample156  0.0388909893  0.1057563092
sample157  0.0314769571  0.0561367499
sample158  0.0329620235  0.0353947424
sample159 -0.0398415131 -0.1007373917
sample160  0.0424939825  0.0108496267
sample161 -0.0888372351 -0.0679700372
sample162 -0.0027472768  0.1237843754
sample163 -0.0126101183  0.0725434184
sample164 -0.0566779438 -0.0458324340
sample165 -0.0315336562 -0.0236362423
sample166 -0.0612056484 -0.0425233254
sample167  0.0142729879  0.0179308311
sample168 -0.0169502225 -0.0769617977
sample169  0.0675080910  0.0131505502
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
                      1             2
sample1   -0.0012329622 -1.635717e-01
sample2   -0.0724350008 -6.021239e-03
sample3   -0.0188460456 -1.080036e-01
sample4    0.0390145322  3.114296e-04
sample5    0.1774811655 -2.996384e-02
sample6   -0.0451444386 -3.455857e-02
sample7   -0.0226466302 -7.020183e-03
sample8   -0.1033680156 -9.856756e-03
sample9    0.1350011692  8.979098e-02
sample10   0.1259887301 -5.097851e-02
sample11   0.0979788320  7.086533e-02
sample12  -0.0863019071 -8.620317e-02
sample13  -0.1381401094  1.828007e-01
sample14  -0.0615073845 -2.642803e-02
sample15   0.0381598919 -3.101665e-02
sample16  -0.0048776680  1.271861e-03
sample17  -0.0788480906 -1.547552e-02
sample18  -0.0884188741 -3.795487e-02
sample19   0.0703044434 -1.084004e-01
sample20  -0.0025585602  7.975872e-02
sample21   0.0941601757 -4.126737e-02
sample22  -0.0550273465 -7.806746e-02
sample23   0.0679495352 -4.102004e-02
sample24  -0.1310962663  1.649310e-01
sample25   0.0113585311 -4.426862e-02
sample26  -0.1402945859  2.016545e-02
sample27   0.0261561033  1.588409e-03
sample28  -0.0724198676  5.850594e-02
sample29  -0.0330058648  2.060796e-03
sample30  -0.0228752600 -2.015432e-02
sample31  -0.0635067898 -6.670333e-02
sample32   0.0685099643 -4.955273e-02
sample33  -0.0777765225 -1.272079e-01
sample34   0.0157842419 -3.024314e-02
sample35  -0.0529632849  1.500972e-01
sample36   0.0070900628  2.025307e-01
sample37  -0.0442420759  1.802088e-01
sample38  -0.0781511359 -3.676423e-02
sample39   0.0120331891 -3.388840e-02
sample40  -0.0473292224  1.471561e-01
sample41   0.0228189354 -2.673557e-02
sample42  -0.0245360215 -7.960866e-02
sample43   0.1036362822 -8.229577e-02
sample44  -0.1012228681  7.049456e-02
sample45   0.0013732146 -2.450907e-02
sample46  -0.0558510088  2.947355e-03
sample47  -0.0380481226  4.554172e-02
sample48   0.0784342114  4.888982e-02
sample49  -0.0605163880 -1.162352e-02
sample50   0.0530079191 -2.737935e-02
sample51   0.1514646556  5.678347e-02
sample52   0.1860935223  1.246717e-01
sample53  -0.0064177044 -2.700992e-02
sample54   0.0697038359 -2.308388e-02
sample55   0.1633577019  1.366442e-02
sample56   0.1011485125  4.682207e-02
sample57   0.1730374213  1.609603e-01
sample58  -0.0071384703 -1.666955e-02
sample59  -0.0030461745  3.005283e-02
sample60   0.0215835004  2.665877e-01
sample61   0.1510583608  1.002385e-01
sample62  -0.0925533998 -4.845844e-02
sample63  -0.0596311721 -4.137020e-02
sample64  -0.0449225771 -2.600572e-03
sample65   0.0939383788 -4.406908e-02
sample66   0.1063400812 -5.709991e-02
sample67  -0.0201590189  2.361727e-01
sample68   0.0037203102  2.418386e-02
sample69  -0.0645161194 -1.155622e-01
sample70  -0.1013440024 -1.351789e-01
sample71  -0.0016467915 -2.976843e-02
sample72   0.0328892975 -2.835859e-02
sample73   0.0275080038 -5.148186e-02
sample74   0.1341719712 -7.895279e-02
sample75   0.0951575642 -3.943185e-02
sample76  -0.0864722002  3.034990e-02
sample77  -0.1035749566 -2.545354e-02
sample78  -0.1575644058  4.939597e-02
sample79   0.0189137050  4.874679e-02
sample80   0.1384140541  4.263994e-05
sample81  -0.0118846457 -6.357932e-02
sample82  -0.1675308198  3.533910e-02
sample83  -0.0065673453 -7.812612e-02
sample84   0.1486891630 -3.109056e-02
sample85  -0.0532724498  7.417882e-02
sample86  -0.1138477383 -1.917513e-05
sample87   0.0432863947  6.080472e-02
sample88   0.0433450357  1.402491e-01
sample89   0.0331205801 -1.395400e-02
sample90  -0.0607412802 -8.610415e-02
sample91  -0.0566272823  1.303747e-01
sample92  -0.0359582544  1.061604e-01
sample93  -0.0433646361 -4.443635e-02
sample94  -0.0477291288 -1.059574e-01
sample95  -0.0249595772 -3.980526e-02
sample96   0.0035219035 -9.293928e-02
sample97  -0.0066048692 -1.527231e-01
sample98   0.0020366810 -5.579551e-02
sample99  -0.0886615976 -3.728222e-02
sample100 -0.1091259126 -3.560420e-02
sample101 -0.0739726545 -4.318002e-02
sample102  0.0574461264 -2.783910e-02
sample103  0.0142730964  9.705539e-03
sample104  0.0710395202  4.068351e-02
sample105  0.0980831378 -3.452951e-02
sample106 -0.0254259285  3.628985e-02
sample107 -0.0160653417 -9.173394e-02
sample108 -0.0200987644 -2.379692e-02
sample109 -0.0389780634  1.692360e-02
sample110 -0.0326304836  2.988110e-02
sample111  0.0676937592 -6.038212e-02
sample112  0.0167883434  5.336939e-03
sample113  0.0969217079 -2.757601e-02
sample114 -0.0026398364 -9.209158e-02
sample115 -0.0308047277  1.603824e-02
sample116 -0.1240307191  1.273000e-01
sample117  0.0334729089  5.392711e-02
sample118 -0.1037152934  6.252430e-02
sample119 -0.1064176790  1.196202e-01
sample120 -0.0771355041 -1.004932e-01
sample121 -0.0129350719  3.181977e-02
sample122  0.0847492389 -5.568323e-02
sample123 -0.0041336802  7.693172e-03
sample124 -0.0583457917 -8.396387e-02
sample125  0.0634844630 -5.232539e-02
sample126 -0.0662580928 -1.091733e-01
sample127 -0.0865024589 -1.094176e-01
sample128 -0.0627817338 -1.470960e-02
sample129 -0.0336276488 -4.007861e-02
sample130 -0.0293517741 -8.046118e-02
sample131 -0.0469197684 -2.209759e-03
sample132 -0.0241740579 -1.248598e-01
sample133  0.0907303200  1.466700e-02
sample134 -0.0350842097  7.539662e-02
sample135  0.0001333376  9.185369e-03
sample136 -0.0335876089 -9.860276e-02
sample137 -0.0640148945 -7.554472e-02
sample138  0.0060964858 -1.742762e-02
sample139 -0.0592084491  5.614968e-02
sample140  0.0427985870 -1.099553e-02
sample141  0.0618796449 -9.301036e-02
sample142  0.0898554512  3.573420e-02
sample143  0.0817389179  8.880524e-02
sample144  0.0787754786 -3.821391e-02
sample145  0.1085821629  1.569477e-01
sample146 -0.0589558005 -4.373363e-02
sample147 -0.0495330491  7.277182e-03
sample148  0.1161592843  9.079106e-03
sample149 -0.0121579539  7.788372e-02
sample150 -0.0314512550  3.520212e-02
sample151  0.0575382196 -1.945351e-02
sample152 -0.0494542138  7.025536e-02
sample153 -0.0941332613  2.153298e-01
sample154 -0.0335932075  2.078726e-02
sample155  0.0690457620 -2.780411e-02
sample156  0.1039901603 -6.292526e-02
sample157 -0.0408645781  8.065516e-03
sample158  0.1018105285  7.816872e-03
sample159 -0.0281730495 -1.207205e-02
sample160  0.1643053014  2.978112e-03
sample161  0.0374329236  8.524611e-02
sample162 -0.0804535258  8.349758e-02
sample163 -0.0743227881 -1.406222e-02
sample164  0.1208806049 -2.139458e-02
sample165  0.1608115914  2.025193e-02
sample166 -0.0425944570 -2.660712e-02
sample167 -0.0226849483 -4.464282e-02
sample168 -0.0180735542 -7.466057e-04
sample169  0.0190778982  2.645402e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
                   [,1]          [,2]
sample1   -0.0572060227 -1.729087e-02
sample2    0.0875245208  1.112588e-02
sample3    0.0403482602 -3.168994e-02
sample4   -0.0218345996  4.052760e-06
sample5   -0.0150905011  4.795041e-03
sample6   -0.0924362933  4.511003e-02
sample7   -0.0793066751 -1.243823e-02
sample8   -0.1342997187  6.215220e-02
sample9   -0.0338886944 -1.854401e-02
sample10   0.0020547173  1.749421e-02
sample11   0.0037275602 -2.364116e-02
sample12  -0.0753094533  2.772698e-02
sample13   0.0856160091  3.679963e-02
sample14  -0.0737457307  2.668452e-02
sample15  -0.0062111746 -3.554864e-03
sample16  -0.0602355268  6.675115e-02
sample17   0.1086768843  2.524534e-02
sample18   0.0702999472  2.231671e-02
sample19   0.0173785882 -3.024846e-02
sample20   0.0484173812 -3.310904e-02
sample21   0.0124657042  6.517144e-02
sample22  -0.0140989936 -3.159137e-02
sample23  -0.0627028403 -5.393710e-04
sample24   0.0919972100  7.909297e-02
sample25   0.0326998483 -1.945206e-02
sample26   0.1064741246  2.120849e-02
sample27   0.0166058995 -4.964993e-02
sample28   0.0743504770  2.614211e-02
sample29  -0.0511008491 -2.782647e-02
sample30   0.0962250842 -3.974893e-03
sample31  -0.0869563008  5.250819e-02
sample32   0.0271858919  1.552005e-02
sample33  -0.0448364581  6.243160e-03
sample34   0.0718415218  1.469396e-02
sample35   0.0403086451 -1.632629e-02
sample36  -0.1036402827 -1.304320e-02
sample37  -0.0159385744 -3.036525e-02
sample38   0.0182198369 -4.034805e-02
sample39   0.0690363619  8.058350e-03
sample40  -0.0467312750 -2.810325e-02
sample41   0.0263674438 -5.171216e-02
sample42   0.0374578960 -1.268634e-02
sample43   0.0132336869  9.536642e-03
sample44  -0.1119154428  5.028683e-02
sample45   0.0759639367  4.587903e-02
sample46   0.0871885519 -4.670385e-02
sample47   0.0721490571 -1.288540e-02
sample48   0.0005086144 -1.290565e-02
sample49  -0.0858177028  5.173760e-02
sample50   0.0118992665 -7.276215e-02
sample51  -0.0426446855  5.306205e-02
sample52  -0.0381605826  3.086785e-02
sample53  -0.0855757630  6.730043e-02
sample54   0.0261723092  9.184260e-03
sample55  -0.0156418304  4.682404e-04
sample56   0.0307831193  2.597550e-02
sample57  -0.0157242103  4.829381e-02
sample58  -0.0031174404  1.359898e-02
sample59  -0.0373001859  5.868397e-03
sample60  -0.0142609099  5.831654e-03
sample61  -0.0122255144  2.663579e-02
sample62   0.0228002942 -8.692265e-03
sample63  -0.0833127581  5.473229e-02
sample64  -0.1166548159  4.196500e-02
sample65   0.0038808902  8.568590e-03
sample66   0.0011561811  1.766612e-02
sample67  -0.1129311062 -2.608702e-02
sample68  -0.0382526429 -3.804045e-02
sample69  -0.0476502440  4.003241e-03
sample70  -0.0110329882 -2.752719e-02
sample71   0.0096850282 -5.627056e-02
sample72   0.0487124704 -8.800131e-03
sample73   0.0773058132  8.239864e-03
sample74  -0.0102488176  2.454957e-02
sample75  -0.0286613976 -8.387293e-03
sample76  -0.0472655595 -2.129315e-02
sample77  -0.0865043074 -7.296820e-03
sample78   0.1070293698  2.818346e-02
sample79  -0.0165060681 -6.659721e-02
sample80  -0.0206765949 -8.712112e-03
sample81  -0.0050943615 -3.079175e-02
sample82   0.1153622361 -1.647054e-02
sample83   0.0367979217 -2.538114e-03
sample84   0.0199463070 -1.468961e-02
sample85  -0.0827122185 -2.709824e-04
sample86   0.0969487314 -1.699897e-02
sample87   0.0421957457 -1.965953e-02
sample88   0.0215934743  1.566050e-02
sample89   0.0751559502  2.811652e-02
sample90  -0.0057328000 -8.283795e-03
sample91  -0.1134005268 -8.603522e-02
sample92  -0.0101689918 -6.894992e-02
sample93   0.0725967502 -6.003176e-03
sample94  -0.0096878852 -4.693081e-03
sample95  -0.0223502239 -3.139636e-02
sample96  -0.0013232863 -1.963604e-02
sample97  -0.0476541710  1.183660e-02
sample98   0.0269546160 -5.978398e-03
sample99   0.0728179461  4.597884e-02
sample100 -0.0413398038  1.079347e-02
sample101  0.0087536994 -6.796076e-02
sample102  0.0032509529  3.932612e-03
sample103  0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105  0.0294940465 -7.140722e-03
sample106  0.0686472054  1.462895e-02
sample107  0.0748635927  8.401339e-03
sample108  0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110  0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112  0.0570870472  1.066018e-02
sample113 -0.0200110554  1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586  1.089843e-02
sample121  0.0542443861  3.861344e-02
sample122  0.0178575357  3.027138e-02
sample123  0.0775020581 -1.636852e-02
sample124 -0.0460701050  1.814758e-02
sample125  0.0543846585  2.075898e-03
sample126 -0.0729417144  3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128  0.0908136899  3.758801e-02
sample129  0.0552445878 -1.879062e-02
sample130  0.0007128089 -1.294308e-02
sample131 -0.0693311345  7.357082e-03
sample132 -0.0556565156  3.126995e-02
sample133  0.0375870104 -1.977240e-02
sample134 -0.1229130924  3.159495e-02
sample135  0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201  1.040683e-02
sample140  0.0452288969 -1.876279e-02
sample141 -0.0189142561  2.247042e-02
sample142  0.0297545566  1.280524e-02
sample143  0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491  5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147  0.0726083535 -1.239968e-02
sample148 -0.0284795794  3.389732e-02
sample149  0.0082261455 -6.399305e-02
sample150 -0.0765013197  2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152  0.0403422737 -2.714879e-02
sample153  0.0629117719  7.425085e-02
sample154  0.0551622927 -3.548984e-02
sample155  0.0654439133 -1.005306e-02
sample156  0.0209310714 -1.390213e-02
sample157  0.0851522597  6.577150e-03
sample158  0.0208354599 -4.663078e-03
sample159 -0.0498794349  1.913257e-02
sample160  0.0216074437  1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162  0.0963663017  5.705881e-02
sample163 -0.1009542191  7.174224e-02
sample164  0.0109881996  1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357  2.673084e-02
sample167 -0.0825048036  2.278863e-03
sample168  0.0486147429  1.793843e-02
sample169  0.0302506727  8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
                   [,1]          [,2]
sample1   -0.0621842115 -1.364509e-02
sample2    0.0944623785  9.720892e-03
sample3    0.0406196267 -2.236338e-02
sample4   -0.0229316496 -3.932487e-04
sample5   -0.0157330047  3.231033e-03
sample6   -0.0945794025  3.120720e-02
sample7   -0.0854427118 -1.052880e-02
sample8   -0.1376625920  4.286608e-02
sample9   -0.0377115311 -1.415134e-02
sample10   0.0035244506  1.280825e-02
sample11   0.0016639987 -1.717895e-02
sample12  -0.0781403168  1.884368e-02
sample13   0.0938400516  2.838858e-02
sample14  -0.0759839772  1.810989e-02
sample15  -0.0068340837 -2.705361e-03
sample16  -0.0590150849  4.757848e-02
sample17   0.1178805097  2.040526e-02
sample18   0.0767858320  1.756604e-02
sample19   0.0157112113 -2.172867e-02
sample20   0.0485318300 -2.327033e-02
sample21   0.0185928176  4.777095e-02
sample22  -0.0191358702 -2.329775e-02
sample23  -0.0672994194 -1.535656e-03
sample24   0.1047476642  5.935707e-02
sample25   0.0329844953 -1.358036e-02
sample26   0.1154952052  1.741529e-02
sample27   0.0133849853 -3.590922e-02
sample28   0.0821554039  2.042376e-02
sample29  -0.0567643690 -2.123848e-02
sample30   0.1016073931 -1.134728e-03
sample31  -0.0880396372  3.670548e-02
sample32   0.0300363338  1.182406e-02
sample33  -0.0467252272  3.739254e-03
sample34   0.0783666394  1.203777e-02
sample35   0.0424227097 -1.118559e-02
sample36  -0.1107646166 -1.143464e-02
sample37  -0.0191667664 -2.246060e-02
sample38   0.0155968095 -2.909621e-02
sample39   0.0746847148  7.148218e-03
sample40  -0.0517028178 -2.137267e-02
sample41   0.0234979494 -3.723018e-02
sample42   0.0388797356 -8.557228e-03
sample43   0.0149555568  7.210002e-03
sample44  -0.1150305613  3.461805e-02
sample45   0.0846146236  3.486020e-02
sample46   0.0884426404 -3.246853e-02
sample47   0.0748644971 -8.083045e-03
sample48  -0.0012033198 -9.403647e-03
sample49  -0.0872662737  3.616245e-02
sample50   0.0066941314 -5.284863e-02
sample51  -0.0411777630  3.791830e-02
sample52  -0.0379355780  2.180834e-02
sample53  -0.0851639886  4.751761e-02
sample54   0.0288006248  7.184424e-03
sample55  -0.0164920835  5.919925e-05
sample56   0.0355115616  1.951043e-02
sample57  -0.0141146068  3.492409e-02
sample58  -0.0015636132  9.862883e-03
sample59  -0.0390656483  3.590929e-03
sample60  -0.0139454780  3.963030e-03
sample61  -0.0106410274  1.919705e-02
sample62   0.0236748439 -5.922677e-03
sample63  -0.0846790877  3.839102e-02
sample64  -0.1202581015  2.846469e-02
sample65   0.0050548584  6.328644e-03
sample66   0.0028013072  1.291807e-02
sample67  -0.1231623009 -2.112565e-02
sample68  -0.0437782161 -2.845072e-02
sample69  -0.0501199692  2.053469e-03
sample70  -0.0140278645 -2.027157e-02
sample71   0.0057489505 -4.085977e-02
sample72   0.0511212704 -5.522408e-03
sample73   0.0828141409  7.431582e-03
sample74  -0.0085959456  1.772951e-02
sample75  -0.0312180394 -6.636869e-03
sample76  -0.0519051781 -1.640191e-02
sample77  -0.0925924762 -6.907800e-03
sample78   0.1163971046  2.251122e-02
sample79  -0.0240906926 -4.887766e-02
sample80  -0.0221327065 -6.730703e-03
sample81  -0.0072114968 -2.254399e-02
sample82   0.1204416674 -9.907422e-03
sample83   0.0386739485 -1.171663e-03
sample84   0.0195988488 -1.033806e-02
sample85  -0.0877680171 -1.725057e-03
sample86   0.1023541048 -1.062501e-02
sample87   0.0425213089 -1.356865e-02
sample88   0.0244788514  1.180820e-02
sample89   0.0804276691  2.188588e-02
sample90  -0.0074639871 -6.140721e-03
sample91  -0.1278832404 -6.485140e-02
sample92  -0.0162199697 -5.048358e-02
sample93   0.0769344893 -3.045135e-03
sample94  -0.0104345587 -3.593172e-03
sample95  -0.0260058453 -2.330475e-02
sample96  -0.0025018700 -1.433516e-02
sample97  -0.0492358305  7.774183e-03
sample98   0.0279220220 -3.862141e-03
sample99   0.0813921923  3.487339e-02
sample100 -0.0428797405  7.112807e-03
sample101  0.0032855240 -4.940743e-02
sample102  0.0038439317  2.938008e-03
sample103  0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105  0.0314853405 -4.656633e-03
sample106  0.0726456731  1.192390e-02
sample107  0.0807342975  7.508627e-03
sample108  0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110  0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112  0.0599954082  8.820317e-03
sample113 -0.0195006577  1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131  6.937326e-03
sample121  0.0613899126  2.915307e-02
sample122  0.0218424338  2.241775e-02
sample123  0.0809008460 -1.051759e-02
sample124 -0.0472109313  1.239887e-02
sample125  0.0583180947  2.521167e-03
sample126 -0.0753941872  2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128  0.1000212216  2.908091e-02
sample129  0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877  4.091672e-03
sample132 -0.0566219024  2.179861e-02
sample133  0.0384172955 -1.372840e-02
sample134 -0.1280862736  2.077912e-02
sample135  0.0592633273  6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861  6.729304e-03
sample140  0.0468926306 -1.285498e-02
sample141 -0.0186248693  1.605439e-02
sample142  0.0328031246  9.887746e-03
sample143  0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310  3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147  0.0765025670 -7.714769e-03
sample148 -0.0276016641  2.420589e-02
sample149  0.0027545308 -4.653007e-02
sample150 -0.0792296010  1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152  0.0409796547 -1.907063e-02
sample153  0.0734301757  5.528780e-02
sample154  0.0557740684 -2.487723e-02
sample155  0.0689436560 -6.127635e-03
sample156  0.0212272938 -9.747423e-03
sample157  0.0911931194  6.355708e-03
sample158  0.0220840645 -3.016357e-03
sample159 -0.0513244242  1.304175e-02
sample160  0.0246213576  1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162  0.1078802043  4.337260e-02
sample163 -0.1017965082  5.047171e-02
sample164  0.0119430799  9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180  1.899222e-02
sample167 -0.0872832229  1.516582e-04
sample168  0.0540714512  1.397701e-02
sample169  0.0328432652  7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
                   [,1]          [,2]
sample1    0.0133684846  2.195848e-02
sample2    0.0254157197 -1.058416e-02
sample3   -0.0049551479 -4.840017e-03
sample4    0.0310390570 -1.063929e-02
sample5    0.0046941318 -6.488426e-03
sample6   -0.0107406753 -1.026702e-02
sample7   -0.0225157631  2.624712e-04
sample8    0.0141320952 -9.505821e-03
sample9    0.0029681280  2.078210e-02
sample10   0.0131729174 -2.275042e-03
sample11  -0.0004164298  1.994019e-02
sample12  -0.0095211620  3.759883e-02
sample13   0.0091018604 -7.953956e-03
sample14  -0.0106557524 -9.181659e-03
sample15  -0.0249924121  3.262724e-02
sample16  -0.0156216400  1.375700e-02
sample17  -0.0019382446  1.073994e-03
sample18  -0.0221072481 -8.703592e-03
sample19   0.0146917619 -1.311712e-02
sample20  -0.0160353760  1.826290e-02
sample21   0.0035947899 -9.616341e-03
sample22  -0.0225060762 -2.532589e-03
sample23   0.0310000683  3.033060e-03
sample24   0.0499544372  1.809450e-02
sample25   0.0284442301 -1.932558e-02
sample26   0.0188220043  2.146985e-02
sample27  -0.0257763219 -1.999228e-03
sample28   0.0120888648  1.125834e-02
sample29  -0.0236482520  4.426726e-02
sample30  -0.0385486305 -2.055935e-02
sample31  -0.0181539336 -5.877838e-03
sample32  -0.0302630460 -2.607192e-03
sample33  -0.0319565715 -1.562628e-02
sample34  -0.0197970124  9.906813e-03
sample35  -0.0247412713 -5.434440e-03
sample36  -0.0386259060 -3.190394e-02
sample37  -0.0566199273 -4.192574e-02
sample38  -0.0142060273  2.259644e-02
sample39   0.0053589035  1.076485e-02
sample40  -0.0552546493 -3.819896e-02
sample41  -0.0013089975  9.278818e-05
sample42   0.0137252142 -1.664652e-02
sample43  -0.0151259626 -6.290953e-03
sample44   0.0617391754 -1.442883e-02
sample45   0.0231410886  1.163143e-03
sample46  -0.0148898209 -1.384176e-04
sample47  -0.0187252536  1.221690e-02
sample48   0.0432839432  1.416671e-02
sample49   0.0160818605 -3.588745e-02
sample50   0.0059333545  4.067003e-02
sample51  -0.0142914866  7.776270e-03
sample52  -0.0086339952  7.208917e-03
sample53  -0.0207386980  6.272432e-03
sample54  -0.0039856719 -1.316934e-02
sample55  -0.0056217017  5.692315e-03
sample56   0.0000123292  8.978290e-04
sample57  -0.0095805555  1.324253e-02
sample58  -0.0124160295 -7.326376e-03
sample59  -0.0400195442 -1.349736e-02
sample60  -0.0460063358  2.770091e-02
sample61  -0.0245266456  1.470710e-02
sample62  -0.0366022783 -3.437352e-03
sample63   0.0013742171  3.288796e-02
sample64  -0.0070599859  2.739588e-02
sample65   0.0041201911  1.498268e-02
sample66   0.0143173351 -1.968812e-02
sample67  -0.0467477531 -1.929938e-02
sample68  -0.0306751978 -1.436184e-02
sample69  -0.0125317217  4.130407e-03
sample70  -0.0068071487  8.080857e-03
sample71   0.0169170264 -7.027348e-03
sample72  -0.0346909749 -1.333770e-02
sample73  -0.0280506153  1.493843e-02
sample74  -0.0182611498  3.294697e-03
sample75  -0.0120563964  8.974612e-03
sample76   0.0001437236 -4.253184e-02
sample77   0.0065330299 -5.252886e-02
sample78   0.0288278141 -1.127782e-02
sample79   0.0503961481 -1.023318e-02
sample80  -0.0207693429  3.648391e-02
sample81   0.0163562768 -9.074596e-03
sample82  -0.0084317129 -1.478976e-02
sample83  -0.0474097918 -1.103126e-02
sample84   0.0177181395 -7.191197e-03
sample85  -0.0342718548 -3.082360e-02
sample86  -0.0261671791 -1.089491e-02
sample87  -0.0009486358 -2.411514e-02
sample88   0.0020528931 -2.894615e-02
sample89  -0.0189361111 -2.638639e-03
sample90  -0.0009863658 -2.390075e-02
sample91  -0.0124352695  8.153234e-02
sample92   0.0564264106 -8.909537e-03
sample93  -0.0081461774  1.570851e-02
sample94  -0.0054896581  1.547251e-02
sample95   0.0224073150 -4.374348e-04
sample96   0.0173528924 -3.050441e-03
sample97   0.0067948115  5.008237e-03
sample98  -0.0116030825  1.498764e-02
sample99   0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101  0.0124923691  3.091503e-02
sample102  0.0650835386 -1.367400e-02
sample103 -0.0042741828  7.855985e-03
sample104  0.0250591040 -4.171938e-03
sample105  0.0157516368 -3.121990e-02
sample106  0.0060593853 -5.101693e-03
sample107 -0.0098329626  1.044506e-02
sample108  0.0044269853  4.142036e-03
sample109  0.0572473486  1.517542e-02
sample110  0.0090474827 -5.119868e-03
sample111  0.0444263015  7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113  0.0241047399  6.706740e-03
sample114  0.0074558775 -4.728652e-03
sample115  0.0611851433  1.117210e-02
sample116  0.0432646951 -1.380556e-02
sample117  0.0516750066 -3.575617e-02
sample118  0.0139942100 -3.279138e-03
sample119  0.0291722987  5.587946e-02
sample120  0.0103515853 -1.690016e-03
sample121 -0.0091396331  3.552116e-02
sample122  0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124  0.0283466326  3.127845e-03
sample125  0.0016472378 -2.770692e-02
sample126 -0.0286529417  3.489336e-02
sample127 -0.0010224500  7.483214e-03
sample128  0.0209049296  2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968  4.637390e-03
sample132  0.0198526786  5.723983e-04
sample133  0.0088812957 -9.988115e-03
sample134 -0.0137484514  1.172591e-02
sample135 -0.0220314568  1.347465e-02
sample136 -0.0185173353  5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138  0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762  2.456863e-02
sample141  0.0058369763 -1.420854e-02
sample142  0.0207886071 -1.188764e-02
sample143  0.0092832598 -1.324238e-02
sample144  0.0028442140  3.627979e-03
sample145  0.0199749569  2.862202e-03
sample146 -0.0182236697  1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148  0.0065435868 -1.572917e-02
sample149  0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151  0.0245166984 -6.888241e-03
sample152  0.0107259913  3.314630e-02
sample153  0.0550963965  3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574  2.642246e-03
sample156 -0.0117803505  2.698265e-02
sample157 -0.0096167165  1.433840e-02
sample158 -0.0101754772  9.137620e-03
sample159  0.0120662931 -2.565236e-02
sample160 -0.0132238202  2.916023e-03
sample161  0.0274491966 -1.748284e-02
sample162  0.0012482909  3.152261e-02
sample163  0.0042031315  1.830701e-02
sample164  0.0174896157 -1.175915e-02
sample165  0.0097517662 -6.119019e-03
sample166  0.0190134679 -1.121582e-02
sample167 -0.0044140836  4.665585e-03
sample168  0.0049689168 -1.941822e-02
sample169 -0.0209802098  3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
                   [,1]          [,2]
sample1   -0.0515543627 -0.0305856787
sample2   -0.0144993256  0.0236342950
sample3   -0.0371833108 -0.0140263348
sample4    0.0068945388 -0.0132539692
sample5    0.0215035333 -0.0663338101
sample6   -0.0187055152  0.0088773016
sample7   -0.0061521552  0.0064029054
sample8   -0.0210874459  0.0334652901
sample9    0.0516865043 -0.0291142799
sample10   0.0059440366 -0.0527217447
sample11   0.0393010793 -0.0200624712
sample12  -0.0420837100  0.0131331362
sample13   0.0333252565  0.0818552509
sample14  -0.0190062644  0.0160202175
sample15  -0.0030968049 -0.0189230681
sample16  -0.0004452158  0.0018880102
sample17  -0.0185848615  0.0240170131
sample18  -0.0273093598  0.0230213640
sample19  -0.0217761111 -0.0445894441
sample20   0.0245820821  0.0159812738
sample21   0.0034527644 -0.0400016054
sample22  -0.0340789054  0.0039289109
sample23  -0.0010344929 -0.0310161212
sample24   0.0289468503  0.0760962436
sample25  -0.0119098496 -0.0122798760
sample26  -0.0181001057  0.0517892852
sample27   0.0050465417 -0.0086515844
sample28   0.0057491502  0.0358830107
sample29  -0.0051104246  0.0116605117
sample30  -0.0103085904  0.0039678538
sample31  -0.0319929858  0.0090606113
sample32  -0.0036232521 -0.0328202010
sample33  -0.0534742153  0.0024751837
sample34  -0.0067495749 -0.0111000311
sample35   0.0378745721  0.0465929296
sample36   0.0647886800  0.0359987924
sample37   0.0488441236  0.0492906912
sample38  -0.0251514062  0.0197110110
sample39  -0.0085428066 -0.0105117852
sample40   0.0379324087  0.0440810741
sample41  -0.0044199152 -0.0128820644
sample42  -0.0292553573 -0.0067045265
sample43  -0.0077829155 -0.0510178219
sample44   0.0045122248  0.0479660309
sample45  -0.0074444298 -0.0051116726
sample46  -0.0088025512  0.0196186661
sample47   0.0076696301  0.0215947965
sample48   0.0290108585 -0.0175568376
sample49  -0.0141754858  0.0184717099
sample50   0.0006282201 -0.0233054373
sample51   0.0441995177 -0.0410022921
sample52   0.0715329391 -0.0399499475
sample53  -0.0095954087 -0.0029140909
sample54   0.0048933768 -0.0281884386
sample55   0.0327325487 -0.0532290012
sample56   0.0323068984 -0.0256595538
sample57   0.0806603122 -0.0286748097
sample58  -0.0064792049 -0.0006945349
sample59   0.0088958941  0.0067389649
sample60   0.0874124612  0.0431964341
sample61   0.0577604571 -0.0326112099
sample62  -0.0313318464  0.0224391756
sample63  -0.0233625220  0.0125110562
sample64  -0.0086426068  0.0148770341
sample65   0.0025256193 -0.0404466327
sample66   0.0006014071 -0.0471576264
sample67   0.0706087042  0.0516228406
sample68   0.0082301011  0.0033109509
sample69  -0.0475076743  0.0001452708
sample70  -0.0600773716  0.0089986962
sample71  -0.0096321627 -0.0050761187
sample72  -0.0031773546 -0.0166221542
sample73  -0.0113700517 -0.0191726684
sample74  -0.0014179662 -0.0608101325
sample75   0.0041911740 -0.0399981269
sample76  -0.0055326449  0.0353114263
sample77  -0.0260214459  0.0305731380
sample78  -0.0119267436  0.0632236007
sample79   0.0186017239  0.0027402910
sample80   0.0241047889 -0.0472697181
sample81  -0.0220288317 -0.0079577210
sample82  -0.0180751258  0.0639051029
sample83  -0.0256671713 -0.0125898269
sample84   0.0161392598 -0.0567222449
sample85   0.0139988188  0.0322763454
sample86  -0.0198382995  0.0389225776
sample87   0.0266270281 -0.0032979996
sample88   0.0515677078  0.0117902495
sample89   0.0014022125 -0.0140510488
sample90  -0.0375949749  0.0044004551
sample91   0.0310397965  0.0440610926
sample92   0.0270570567  0.0324380452
sample93  -0.0215009202  0.0063993941
sample94  -0.0415702912 -0.0037692077
sample95  -0.0168416047  0.0010019120
sample96  -0.0285582661 -0.0187991000
sample97  -0.0490843868 -0.0266760748
sample98  -0.0171579033 -0.0112897471
sample99  -0.0271316525  0.0232395583
sample100 -0.0301789816  0.0305498693
sample101 -0.0264371151  0.0170723968
sample102  0.0012767734 -0.0248949597
sample103  0.0055214687 -0.0030040587
sample104  0.0251346074 -0.0165212671
sample105  0.0062424215 -0.0400309901
sample106  0.0069768684  0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679  0.0023637162
sample109 -0.0014762845  0.0165583675
sample110  0.0036971063  0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112  0.0046098120 -0.0048009350
sample113  0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453  0.0135805368
sample116  0.0183575759  0.0665377581
sample117  0.0227640036 -0.0012287760
sample118  0.0015695248  0.0472617382
sample119  0.0190084932  0.0590034062
sample120 -0.0449645755  0.0072755697
sample121  0.0077307184  0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123  0.0016959300  0.0028593594
sample124 -0.0365091615  0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408  0.0019165544
sample127 -0.0494064872  0.0088209044
sample128 -0.0155454766  0.0186819802
sample129 -0.0184340400  0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422  0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133  0.0204029276 -0.0282209049
sample134  0.0175513332  0.0262883962
sample135  0.0029009925  0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323  0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139  0.0073103935  0.0308956174
sample140  0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142  0.0268670676 -0.0239229634
sample143  0.0421049126 -0.0110888235
sample144  0.0017253664 -0.0341766012
sample145  0.0681741320 -0.0073526377
sample146 -0.0239965222  0.0118396767
sample147 -0.0063453522  0.0183130585
sample148  0.0230825251 -0.0379753037
sample149  0.0223298673  0.0188909118
sample150  0.0055709108  0.0174179009
sample151  0.0039177786 -0.0233533275
sample152  0.0134325667  0.0302344591
sample153  0.0511990309  0.0730230140
sample154  0.0006698324  0.0154177486
sample155  0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599  0.0154934573
sample158  0.0201775524 -0.0332982124
sample159 -0.0086909001  0.0073496711
sample160  0.0295437331 -0.0555734536
sample161  0.0332754288  0.0033779619
sample162  0.0121954537  0.0433540412
sample163 -0.0173490933  0.0227219128
sample164  0.0143374783 -0.0453542590
sample165  0.0343612593 -0.0511194536
sample166 -0.0157536004  0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919  0.0060747155
sample169  0.0116231468 -0.0015112800
> 
> ## 3.3 Plotting VAF
> 
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
> 
> # JIVE plotVAF
> plotVAF(jiveRes)
> 
> 
> #########################
> ## PART 4. Plot Results
> 
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+              combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+              background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+              axisSize=NULL,titleSize=NULL)
> 
> # Scores for common part. JIVE
> plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common",
+              combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+              background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+              axisSize=NULL,titleSize=NULL)
> 
> # Scores for common part. O2PLS.
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for common part. O2PLS.
> plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common",
+              combined=TRUE,block=NULL,color="classname",shape=NULL,
+              labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+              labelSize=NULL,axisSize=NULL,titleSize=NULL)
> 
> 
> # Scores for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for distinctive part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual",
+              combined=TRUE,block=NULL,color="classname",shape=NULL,
+              labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+              labelSize=NULL,axisSize=NULL,titleSize=NULL)
> 
> # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block)
> p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for common and distinctive part. DISCO  (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Loadings for common part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> # Loadings for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> # Combined plot for loadings from common and distinctive part  (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> 
> ## Plot scores and loadings togheter: Common components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+         combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+         background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+         axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> ## Plot scores and loadings togheter:  Common components O2PLS
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> ## Plot scores and loadings togheter: Distintive components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
  10.78    0.57   11.43 

STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsPCA.Rout


R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 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.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> ###########################################
> ########### EXAMPLE OF THE OMICSPCA
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
> 
> # g_legend (not exported by STATegRa any more)
> ## code from https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs
> g_legend<-function(a.gplot){
+     tmp <- ggplot_gtable(ggplot_build(a.gplot))
+     leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
+     legend <- tmp$grobs[[leg]]
+     return(legend)}
> 
> #########################
> ## PART 1. Load data
> 
> ## Load data
> data(STATegRa_S3)
> 
> ls()
[1] "Block1.PCA" "Block2.PCA" "ed.PCA"     "g_legend"  
> 
> ## Create ExpressionSets
> # Block1 - Expression data
> B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname"))
> 
> #########################
> ## PART 2.  Model Selection
> 
> require(grid)
Loading required package: grid
> require(gridExtra)
Loading required package: gridExtra
> require(ggplot2)
Loading required package: ggplot2
> 
> ## Select the optimal components
> ms <- modelSelection(Input=list(B1,B2),Rmax=4,fac.sel="single%",varthreshold=0.03,center=TRUE,scale=TRUE,weight=TRUE)
Common components
[1] 2

Distinctive components
[[1]]
[1] 2

[[2]]
[1] 2

> 
> 
> #########################
> ## PART 3. Component Analysis
> 
> ## 3.1 Component analysis of the three methods
> discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                               scale=TRUE,weight=TRUE)
> jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                              scale=TRUE,weight=TRUE)
> o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                               scale=TRUE,weight=TRUE)
> 
> ## 3.2 Exploring scores structures
> 
> # Exploring DISCO-SCA scores structure
> discoRes@scores$common ## Common scores
                      1             2
sample1    0.0781575685 -0.0431550514
sample2   -0.1192221481  0.0294018969
sample3   -0.0531408557 -0.0746837533
sample4    0.0292971668 -0.0006037144
sample5    0.0202090717  0.0110454866
sample6    0.1226088418  0.1053494280
sample7    0.1078931452 -0.0322416667
sample8    0.1782890986  0.1449328992
sample9    0.0468697264 -0.0455171563
sample10  -0.0036032760  0.0420076335
sample11  -0.0035566356 -0.0566284758
sample12   0.1006129711  0.0641394700
sample13  -0.1174412981  0.0907474861
sample14   0.0981203562  0.0617764661
sample15   0.0085337369 -0.0086953590
sample16   0.0783146745  0.1581335245
sample17  -0.1483610696  0.0638580055
sample18  -0.0963084335  0.0556690015
sample19  -0.0217243028 -0.0720130513
sample20  -0.0635633844 -0.0779608294
sample21  -0.0201844128  0.1566381549
sample22   0.0218274027 -0.0764053670
sample23   0.0852038948 -0.0032768373
sample24  -0.1287181899  0.1924421107
sample25  -0.0430575690 -0.0456641893
sample26  -0.1453899888  0.0541456527
sample27  -0.0197483427 -0.1185590016
sample28  -0.1025339538  0.0650654044
sample29   0.0706022606 -0.0682930359
sample30  -0.1295622826 -0.0066673609
sample31   0.1147449310  0.1232729075
sample32  -0.0374308120  0.0380252767
sample33   0.0599520935  0.0136939189
sample34  -0.0984199253  0.0375366582
sample35  -0.0543096358 -0.0378032000
sample36   0.1403628125 -0.0343633739
sample37   0.0228947867 -0.0732682634
sample38  -0.0222072869 -0.0962565928
sample39  -0.0941739242  0.0215179717
sample40   0.0643807274 -0.0687713122
sample41  -0.0327634860 -0.1232187170
sample42  -0.0500431622 -0.0292515645
sample43  -0.0184497121  0.0233045860
sample44   0.1487889005  0.1171203654
sample45  -0.1050778960  0.1123137612
sample46  -0.1151191448 -0.1093994274
sample47  -0.0962591449 -0.0288415932
sample48   0.0004832454 -0.0310383970
sample49   0.1135203723  0.1213934890
sample50  -0.0123549791 -0.1740763742
sample51   0.0550527347  0.1258932792
sample52   0.0499118385  0.0728582613
sample53   0.1119772624  0.1588066606
sample54  -0.0360055719  0.0228586048
sample55   0.0210418827  0.0006751581
sample56  -0.0434171580  0.0633131494
sample57   0.0197820543  0.1150755817
sample58   0.0030440731  0.0326128891
sample59   0.0500256865  0.0129526295
sample60   0.0184280129  0.0136224232
sample61   0.0150298901  0.0635100207
sample62  -0.0304758568 -0.0201232322
sample63   0.1102250035  0.1285968068
sample64   0.1552586782  0.0971186091
sample65  -0.0058503840  0.0207102172
sample66  -0.0025607547  0.0424282895
sample67   0.1546638797 -0.0661571900
sample68   0.0536374434 -0.0923600482
sample69   0.0640333096  0.0082004827
sample70   0.0163521890 -0.0663227047
sample71  -0.0102536054 -0.1345966943
sample72  -0.0654191599 -0.0196032438
sample73  -0.1048553136  0.0221002896
sample74   0.0123800544  0.0586158778
sample75   0.0392079809 -0.0209724851
sample76   0.0648954612 -0.0524759326
sample77   0.1172922660 -0.0201201070
sample78  -0.1463072795  0.0708396217
sample79   0.0265208759 -0.1603430831
sample80   0.0279739301 -0.0214150920
sample81   0.0079212180 -0.0738497571
sample82  -0.1544234535 -0.0361449802
sample83  -0.0494205197 -0.0049933906
sample84  -0.0259039770 -0.0346593539
sample85   0.1116487547 -0.0031399934
sample86  -0.1306478912 -0.0377153274
sample87  -0.0554777854 -0.0459739265
sample88  -0.0301626637  0.0382207078
sample89  -0.1016866173  0.0694080258
sample90   0.0086821706 -0.0201324030
sample91   0.1578629954 -0.2097790347
sample92   0.0170933392 -0.1655942321
sample93  -0.0979805032 -0.0121499895
sample94   0.0131486303 -0.0114929308
sample95   0.0315682462 -0.0758919781
sample96   0.0024125855 -0.0470187459
sample97   0.0634545827  0.0270302419
sample98  -0.0359372474 -0.0135465472
sample99  -0.1009167777  0.1124709594
sample100  0.0551754128  0.0246502709
sample101 -0.0080115873 -0.1627408918
sample102 -0.0046451431  0.0095465747
sample103 -0.0472520777 -0.0940383070
sample104  0.0198157386 -0.0591149713
sample105 -0.0400239033 -0.0160950862
sample106 -0.0923810207  0.0369003232
sample107 -0.1019372307  0.0224967904
sample108 -0.0877091506 -0.0128850416
sample109  0.0864820137 -0.0901087160
sample110 -0.1223116515 -0.0096109537
sample111  0.0257352367 -0.0936286149
sample112 -0.0765285898  0.0270380763
sample113  0.0258799728  0.0377435785
sample114  0.0021141182 -0.0882041289
sample115  0.0303455120 -0.0723741835
sample116  0.0780504313 -0.0685166588
sample117  0.0536893921 -0.0912030641
sample118  0.0666649844 -0.0236262387
sample119  0.1021872622 -0.2325006589
sample120  0.0750216326  0.0243344173
sample121 -0.0756937940  0.0942971390
sample122 -0.0259632208  0.0731918247
sample123 -0.1037844630 -0.0369177962
sample124  0.0611205072  0.0421643701
sample125 -0.0738472617  0.0066943933
sample126  0.0972919234  0.0762701534
sample127  0.0824699510 -0.0096644977
sample128 -0.1249411668  0.0929251278
sample129 -0.0734063555 -0.0434311468
sample130 -0.0003500186 -0.0309857445
sample131  0.0930184092  0.0155971569
sample132  0.0736220527  0.0732969607
sample133 -0.0498398370 -0.0462456790
sample134  0.1644872589  0.0720048807
sample135 -0.0752294965  0.0003871907
sample136  0.0227150132 -0.0495467808
sample137  0.0564721866 -0.0288858199
sample138  0.0255986431 -0.0610933814
sample139  0.0621218802  0.0235859913
sample140 -0.0604148806 -0.0435529639
sample141  0.0246743011  0.0532629479
sample142 -0.0409564000  0.0316232232
sample143 -0.0077356434 -0.0476909426
sample144  0.0173241017 -0.0156786220
sample145  0.0485467449  0.1202736499
sample146  0.0419650146 -0.0811239142
sample147 -0.0977304520 -0.0274768337
sample148  0.0368253174  0.0803969017
sample149 -0.0072864839 -0.1533018399
sample150  0.1020825514  0.0624824652
sample151  0.0305397097 -0.0289339626
sample152 -0.0533595219 -0.0638336086
sample153 -0.0891639881  0.1799447834
sample154 -0.0727554283 -0.0834128043
sample155 -0.0880665717 -0.0220768234
sample156 -0.0276558756 -0.0326600756
sample157 -0.1155031540  0.0183636391
sample158 -0.0281506667 -0.0104910780
sample159  0.0663233700  0.0443808476
sample160 -0.0302644011  0.0404302877
sample161  0.0114712859 -0.0591085674
sample162 -0.1337091153  0.1398131322
sample163  0.1330120606  0.1688768857
sample164 -0.0150338218  0.0028373568
sample165  0.0076518788 -0.0164146626
sample166  0.0367791386  0.0630612124
sample167  0.1111989856  0.0030066832
sample168 -0.0672983034  0.0446266136
sample169 -0.0413003590  0.0224449375
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
                      1             2
sample1   -0.0420461462  0.0867866196
sample2   -0.0820850148 -0.0410968636
sample3    0.0155967664 -0.0195186395
sample4   -0.1001342867 -0.0410776164
sample5   -0.0153479979 -0.0253257697
sample6    0.0340237244 -0.0408223414
sample7    0.0722602896  0.0002323759
sample8   -0.0457622064 -0.0370006800
sample9   -0.0086216018  0.0820184490
sample10  -0.0423631885 -0.0083917542
sample11   0.0022594392  0.0787764088
sample12   0.0322075434  0.1479823347
sample13  -0.0293971988 -0.0306742812
sample14   0.0337429792 -0.0367508414
sample15   0.0815560598  0.1275613672
sample16   0.0508329364  0.0540604279
sample17   0.0062554313  0.0041024879
sample18   0.0705600032 -0.0351053484
sample19  -0.0476782395 -0.0509595351
sample20   0.0523028499  0.0715513932
sample21  -0.0119253690 -0.0376087020
sample22   0.0724459420 -0.0095635080
sample23  -0.0992529478  0.0134299175
sample24  -0.1595268813  0.0728684600
sample25  -0.0920660333 -0.0749749073
sample26  -0.0595567671  0.0848973811
sample27   0.0826579043 -0.0086747769
sample28  -0.0384834660  0.0440972831
sample29   0.0777742312  0.1735298314
sample30   0.1229474445 -0.0819018748
sample31   0.0579748473 -0.0238646948
sample32   0.0970365170 -0.0111435340
sample33   0.1017579988 -0.0630452805
sample34   0.0637902203  0.0377936084
sample35   0.0790003786 -0.0229732685
sample36   0.1224932995 -0.1274968655
sample37   0.1798848111 -0.1673448482
sample38   0.0466395760  0.0888153043
sample39  -0.0168694889  0.0421536092
sample40   0.1756418583 -0.1526662796
sample41   0.0042471472  0.0004924519
sample42  -0.0447824952 -0.0651501286
sample43   0.0482291689 -0.0253533636
sample44  -0.1986821674 -0.0545753281
sample45  -0.0741919849  0.0054714423
sample46   0.0478864032 -0.0007080528
sample47   0.0608217171  0.0481615290
sample48  -0.1381464244  0.0578301322
sample49  -0.0530633214 -0.1405523431
sample50  -0.0173643337  0.1602386105
sample51   0.0462454386  0.0303472962
sample52   0.0279994536  0.0280387815
sample53   0.0667496064  0.0237700041
sample54   0.0121811262 -0.0521354924
sample55   0.0182392136  0.0221326588
sample56  -0.0001310039  0.0030909312
sample57   0.0316572589  0.0530190602
sample58   0.0393890454 -0.0297801871
sample59   0.1278271004 -0.0546540896
sample60   0.1486964095  0.1069141490
sample61   0.0793066329  0.0569790235
sample62   0.1172822329 -0.0149211455
sample63  -0.0028814550  0.1300524187
sample64   0.0237294749  0.1073288367
sample65  -0.0126543843  0.0589810398
sample66  -0.0468234644 -0.0771066481
sample67   0.1494286437 -0.0769877788
sample68   0.0978024838 -0.0577364095
sample69   0.0403090552  0.0156038164
sample70   0.0221599376  0.0315436552
sample71  -0.0546327834 -0.0272394882
sample72   0.1107501340 -0.0537331652
sample73   0.0906756555  0.0579957677
sample74   0.0586512542  0.0121417343
sample75   0.0390513052  0.0349278111
sample76  -0.0022939039 -0.1676560115
sample77  -0.0232101469 -0.2067300897
sample78  -0.0929810960 -0.0434927700
sample79  -0.1619378390 -0.0378102167
sample80   0.0680393284  0.1424655747
sample81  -0.0530724302 -0.0358347574
sample82   0.0266851044 -0.0577449175
sample83   0.1517242218 -0.0448570465
sample84  -0.0570942684 -0.0273808338
sample85   0.1086271200 -0.1228130696
sample86   0.0833892316 -0.0442925026
sample87   0.0022041281 -0.0943908533
sample88  -0.0078277863 -0.1140504516
sample89   0.0611004822 -0.0094589510
sample90   0.0022941957 -0.0936254878
sample91   0.0433776840  0.3205971944
sample92  -0.1815215271 -0.0334666330
sample93   0.0267654518  0.0614425736
sample94   0.0181901953  0.0605088123
sample95  -0.0720313022 -0.0013040428
sample96  -0.0559671780 -0.0118787022
sample97  -0.0217420827  0.0195417274
sample98   0.0379199711  0.0588352664
sample99  -0.0792509934 -0.0151262139
sample100  0.0222099996 -0.0023322974
sample101 -0.0387081235  0.1224225227
sample102 -0.2094626236 -0.0516420367
sample103  0.0138559586  0.0301047561
sample104 -0.0807947181 -0.0162712231
sample105 -0.0520491761 -0.1229660238
sample106 -0.0192643607 -0.0185235107
sample107  0.0319014294  0.0405120515
sample108 -0.0140673828  0.0163422368
sample109 -0.1831855404  0.0613024069
sample110 -0.0292782455 -0.0199846418
sample111 -0.1423172096  0.0327352380
sample112  0.0426312843 -0.0029087144
sample113 -0.0771932742  0.0268742935
sample114 -0.0241566306 -0.0184080627
sample115 -0.1958954811  0.0460149017
sample116 -0.1394436521 -0.0530793207
sample117 -0.1672310551 -0.1386521609
sample118 -0.0448331310 -0.0117617895
sample119 -0.0910188496  0.2217435870
sample120 -0.0331405237 -0.0057270238
sample121  0.0307514970  0.1392506166
sample122 -0.0839839249 -0.0291983384
sample123  0.0239676017 -0.0642167473
sample124 -0.0909177077  0.0130430362
sample125 -0.0065362641 -0.1092630997
sample126  0.0935272145  0.1368276644
sample127  0.0035406197  0.0292755002
sample128 -0.0660351870  0.1018575988
sample129  0.0693672113 -0.0695430438
sample130  0.0008517854 -0.0669705400
sample131  0.0431011584  0.0174060909
sample132 -0.0637090474  0.0029383697
sample133 -0.0289463685 -0.0390817253
sample134  0.0446140161  0.0456332219
sample135  0.0712343955  0.0521627428
sample136  0.0596319935  0.0197291529
sample137  0.0793176270 -0.0380637493
sample138 -0.0973504241 -0.0454209917
sample139  0.0539864962 -0.1534332232
sample140  0.0850873477  0.0955804210
sample141 -0.0192725148 -0.0554446397
sample142 -0.0672295402 -0.0461312903
sample143 -0.0303706373 -0.0519258496
sample144 -0.0089350123  0.0145815384
sample145 -0.0638880304  0.0122268926
sample146  0.0585924710  0.0063074720
sample147  0.0894147285 -0.1124626024
sample148 -0.0216441911 -0.0615962322
sample149 -0.0515313099 -0.0839902805
sample150  0.0568227037 -0.0124472853
sample151 -0.0789513046 -0.0261823766
sample152 -0.0330691490  0.1306445048
sample153 -0.1752069717  0.1497755999
sample154  0.0421491862 -0.0037017257
sample155  0.0680199497  0.0095703333
sample156  0.0388951438  0.1057557843
sample157  0.0314764916  0.0561364578
sample158  0.0329630555  0.0353943532
sample159 -0.0398463023 -0.1007368169
sample160  0.0424904716  0.0108492962
sample161 -0.0888339215 -0.0679692637
sample162 -0.0027574011  0.1237848354
sample163 -0.0126233492  0.0725440991
sample164 -0.0566787413 -0.0458318152
sample165 -0.0315331381 -0.0236359513
sample166 -0.0612110908 -0.0425224649
sample167  0.0142729545  0.0179306960
sample168 -0.0169544263 -0.0769614808
sample169  0.0675062828  0.0131498913
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
                      1             2
sample1   -0.0012331732  1.635716e-01
sample2   -0.0724353325  6.022164e-03
sample3   -0.0188459923  1.080029e-01
sample4    0.0390143047 -3.106240e-04
sample5    0.1774810593  2.996430e-02
sample6   -0.0451446526  3.455900e-02
sample7   -0.0226463385  7.019174e-03
sample8   -0.1033684706  9.857994e-03
sample9    0.1350014301 -8.979115e-02
sample10   0.1259884321  5.097940e-02
sample11   0.0979791010 -7.086568e-02
sample12  -0.0863021042  8.620322e-02
sample13  -0.1381401861 -1.827998e-01
sample14  -0.0615074764  2.642808e-02
sample15   0.0381600651  3.101599e-02
sample16  -0.0048779477 -1.270992e-03
sample17  -0.0788483301  1.547608e-02
sample18  -0.0884189515  3.795477e-02
sample19   0.0703043485  1.084003e-01
sample20  -0.0025581208 -7.975973e-02
sample21   0.0941596466  4.126899e-02
sample22  -0.0550270768  7.806612e-02
sample23   0.0679492726  4.102080e-02
sample24  -0.1310969682 -1.649281e-01
sample25   0.0113583499  4.426901e-02
sample26  -0.1402949015 -2.016456e-02
sample27   0.0261566220 -1.590018e-03
sample28  -0.0724200858 -5.850507e-02
sample29  -0.0330054631 -2.062128e-03
sample30  -0.0228750246  2.015341e-02
sample31  -0.0635070486  6.670370e-02
sample32   0.0685100025  4.955245e-02
sample33  -0.0777764907  1.272070e-01
sample34   0.0157842081  3.024311e-02
sample35  -0.0529627763 -1.500981e-01
sample36   0.0070908231 -2.025321e-01
sample37  -0.0442411494 -1.802110e-01
sample38  -0.0781508271  3.676294e-02
sample39   0.0120330007  3.388885e-02
sample40  -0.0473283547 -1.471582e-01
sample41   0.0228192287  2.673454e-02
sample42  -0.0245361918  7.960878e-02
sample43   0.1036361992  8.229577e-02
sample44  -0.1012235003 -7.049233e-02
sample45   0.0013726507  2.451075e-02
sample46  -0.0558506326 -2.948627e-03
sample47  -0.0380478626 -4.554239e-02
sample48   0.0784340396 -4.888888e-02
sample49  -0.0605168218  1.162476e-02
sample50   0.0530083042  2.737809e-02
sample51   0.1514645315 -5.678257e-02
sample52   0.1860936038 -1.246711e-01
sample53  -0.0064179789  2.701062e-02
sample54   0.0697037537  2.308413e-02
sample55   0.1633577730 -1.366433e-02
sample56   0.1011483959 -4.682131e-02
sample57   0.1730374413 -1.609593e-01
sample58  -0.0071384892  1.666951e-02
sample59  -0.0030458356 -3.005379e-02
sample60   0.0215842441 -2.665888e-01
sample61   0.1510585398 -1.002384e-01
sample62  -0.0925531493  4.845724e-02
sample63  -0.0596315561  4.137112e-02
sample64  -0.0449227330  2.600973e-03
sample65   0.0939382198  4.406951e-02
sample66   0.1063397630  5.710081e-02
sample67  -0.0201580494 -2.361747e-01
sample68   0.0037208573 -2.418548e-02
sample69  -0.0645162018  1.155618e-01
sample70  -0.1013439733  1.351780e-01
sample71  -0.0016466048  2.976771e-02
sample72   0.0328895497  2.835768e-02
sample73   0.0275080411  5.148151e-02
sample74   0.1341718330  7.895304e-02
sample75   0.0951576682  3.943146e-02
sample76  -0.0864719881 -3.035055e-02
sample77  -0.1035749507  2.545324e-02
sample78  -0.1575648006 -4.939470e-02
sample79   0.0189138405 -4.874690e-02
sample80   0.1384142853 -4.317173e-05
sample81  -0.0118846675  6.357907e-02
sample82  -0.1675306585 -3.533970e-02
sample83  -0.0065671064  7.812493e-02
sample84   0.1486890623  3.109097e-02
sample85  -0.0532720200 -7.417992e-02
sample86  -0.1138474827  1.817052e-05
sample87   0.0432866014 -6.080500e-02
sample88   0.0433451219 -1.402486e-01
sample89   0.0331204775  1.395429e-02
sample90  -0.0607413499  8.610385e-02
sample91  -0.0566263446 -1.303771e-01
sample92  -0.0359580654 -1.061605e-01
sample93  -0.0433646455  4.443609e-02
sample94  -0.0477292152  1.059570e-01
sample95  -0.0249595934  3.980509e-02
sample96   0.0035217530  9.293931e-02
sample97  -0.0066052110  1.527234e-01
sample98   0.0020367078  5.579514e-02
sample99  -0.0886621923  3.728379e-02
sample100 -0.1091259616  3.560401e-02
sample101 -0.0739723745  4.317881e-02
sample102  0.0574455511  2.784092e-02
sample103  0.0142733843 -9.706381e-03
sample104  0.0710395587 -4.068330e-02
sample105  0.0980829891  3.452998e-02
sample106 -0.0254260535 -3.628931e-02
sample107 -0.0160655087  9.173398e-02
sample108 -0.0200988333  2.379699e-02
sample109 -0.0389781999 -1.692310e-02
sample110 -0.0326305266 -2.988086e-02
sample111  0.0676935874  6.038250e-02
sample112  0.0167883518 -5.336923e-03
sample113  0.0969213848  2.757706e-02
sample114 -0.0026397963  9.209100e-02
sample115 -0.0308049655 -1.603741e-02
sample116 -0.1240306383 -1.272998e-01
sample117  0.0334728621 -5.392660e-02
sample118 -0.1037152138 -6.252440e-02
sample119 -0.1064170393 -1.196218e-01
sample120 -0.0771357796  1.004935e-01
sample121 -0.0129352362 -3.181912e-02
sample122  0.0847487398  5.568468e-02
sample123 -0.0041335475 -7.693564e-03
sample124 -0.0583462348  8.396479e-02
sample125  0.0634843208  5.232569e-02
sample126 -0.0662582125  1.091730e-01
sample127 -0.0865025646  1.094172e-01
sample128 -0.0627822184  1.471098e-02
sample129 -0.0336274522  4.007772e-02
sample130 -0.0293518125  8.046086e-02
sample131 -0.0469196761  2.209372e-03
sample132 -0.0241745798  1.248608e-01
sample133  0.0907303816 -1.466698e-02
sample134 -0.0350841202 -7.539660e-02
sample135  0.0001334936 -9.185833e-03
sample136 -0.0335874758  9.860178e-02
sample137 -0.0640147210  7.554368e-02
sample138  0.0060964014  1.742783e-02
sample139 -0.0592082705 -5.615007e-02
sample140  0.0427988700  1.099463e-02
sample141  0.0618793171  9.301104e-02
sample142  0.0898552439 -3.573321e-02
sample143  0.0817391120 -8.880528e-02
sample144  0.0787754465  3.821395e-02
sample145  0.1085819435 -1.569460e-01
sample146 -0.0589554901  4.373233e-02
sample147 -0.0495327823 -7.278086e-03
sample148  0.1161590409 -9.078105e-03
sample149 -0.0121575383 -7.788465e-02
sample150 -0.0314511956 -3.520220e-02
sample151  0.0575380900  1.945394e-02
sample152 -0.0494540304 -7.025567e-02
sample153 -0.0941338741 -2.153269e-01
sample154 -0.0335928724 -2.078828e-02
sample155  0.0690459096  2.780360e-02
sample156  0.1039902331  6.292487e-02
sample157 -0.0408645840 -8.065530e-03
sample158  0.1018106379 -7.817027e-03
sample159 -0.0281732597  1.207262e-02
sample160  0.1643052863 -2.977802e-03
sample161  0.0374330100 -8.524588e-02
sample162 -0.0804538361 -8.349631e-02
sample163 -0.0743232538  1.406350e-02
sample164  0.1208804221  2.139526e-02
sample165  0.1608115953 -2.025158e-02
sample166 -0.0425948043  2.660804e-02
sample167 -0.0226849506  4.464257e-02
sample168 -0.0180737426  7.471739e-04
sample169  0.0190780252 -2.645427e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
                   [,1]          [,2]
sample1   -0.0572060227 -1.729087e-02
sample2    0.0875245208  1.112588e-02
sample3    0.0403482602 -3.168994e-02
sample4   -0.0218345996  4.052760e-06
sample5   -0.0150905011  4.795041e-03
sample6   -0.0924362933  4.511003e-02
sample7   -0.0793066751 -1.243823e-02
sample8   -0.1342997187  6.215220e-02
sample9   -0.0338886944 -1.854401e-02
sample10   0.0020547173  1.749421e-02
sample11   0.0037275602 -2.364116e-02
sample12  -0.0753094533  2.772698e-02
sample13   0.0856160091  3.679963e-02
sample14  -0.0737457307  2.668452e-02
sample15  -0.0062111746 -3.554864e-03
sample16  -0.0602355268  6.675115e-02
sample17   0.1086768843  2.524534e-02
sample18   0.0702999472  2.231671e-02
sample19   0.0173785882 -3.024846e-02
sample20   0.0484173812 -3.310904e-02
sample21   0.0124657042  6.517144e-02
sample22  -0.0140989936 -3.159137e-02
sample23  -0.0627028403 -5.393710e-04
sample24   0.0919972100  7.909297e-02
sample25   0.0326998483 -1.945206e-02
sample26   0.1064741246  2.120849e-02
sample27   0.0166058995 -4.964993e-02
sample28   0.0743504770  2.614211e-02
sample29  -0.0511008491 -2.782647e-02
sample30   0.0962250842 -3.974893e-03
sample31  -0.0869563008  5.250819e-02
sample32   0.0271858919  1.552005e-02
sample33  -0.0448364581  6.243160e-03
sample34   0.0718415218  1.469396e-02
sample35   0.0403086451 -1.632629e-02
sample36  -0.1036402827 -1.304320e-02
sample37  -0.0159385744 -3.036525e-02
sample38   0.0182198369 -4.034805e-02
sample39   0.0690363619  8.058350e-03
sample40  -0.0467312750 -2.810325e-02
sample41   0.0263674438 -5.171216e-02
sample42   0.0374578960 -1.268634e-02
sample43   0.0132336869  9.536642e-03
sample44  -0.1119154428  5.028683e-02
sample45   0.0759639367  4.587903e-02
sample46   0.0871885519 -4.670385e-02
sample47   0.0721490571 -1.288540e-02
sample48   0.0005086144 -1.290565e-02
sample49  -0.0858177028  5.173760e-02
sample50   0.0118992665 -7.276215e-02
sample51  -0.0426446855  5.306205e-02
sample52  -0.0381605826  3.086785e-02
sample53  -0.0855757630  6.730043e-02
sample54   0.0261723092  9.184260e-03
sample55  -0.0156418304  4.682404e-04
sample56   0.0307831193  2.597550e-02
sample57  -0.0157242103  4.829381e-02
sample58  -0.0031174404  1.359898e-02
sample59  -0.0373001859  5.868397e-03
sample60  -0.0142609099  5.831654e-03
sample61  -0.0122255144  2.663579e-02
sample62   0.0228002942 -8.692265e-03
sample63  -0.0833127581  5.473229e-02
sample64  -0.1166548159  4.196500e-02
sample65   0.0038808902  8.568590e-03
sample66   0.0011561811  1.766612e-02
sample67  -0.1129311062 -2.608702e-02
sample68  -0.0382526429 -3.804045e-02
sample69  -0.0476502440  4.003241e-03
sample70  -0.0110329882 -2.752719e-02
sample71   0.0096850282 -5.627056e-02
sample72   0.0487124704 -8.800131e-03
sample73   0.0773058132  8.239864e-03
sample74  -0.0102488176  2.454957e-02
sample75  -0.0286613976 -8.387293e-03
sample76  -0.0472655595 -2.129315e-02
sample77  -0.0865043074 -7.296820e-03
sample78   0.1070293698  2.818346e-02
sample79  -0.0165060681 -6.659721e-02
sample80  -0.0206765949 -8.712112e-03
sample81  -0.0050943615 -3.079175e-02
sample82   0.1153622361 -1.647054e-02
sample83   0.0367979217 -2.538114e-03
sample84   0.0199463070 -1.468961e-02
sample85  -0.0827122185 -2.709824e-04
sample86   0.0969487314 -1.699897e-02
sample87   0.0421957457 -1.965953e-02
sample88   0.0215934743  1.566050e-02
sample89   0.0751559502  2.811652e-02
sample90  -0.0057328000 -8.283795e-03
sample91  -0.1134005268 -8.603522e-02
sample92  -0.0101689918 -6.894992e-02
sample93   0.0725967502 -6.003176e-03
sample94  -0.0096878852 -4.693081e-03
sample95  -0.0223502239 -3.139636e-02
sample96  -0.0013232863 -1.963604e-02
sample97  -0.0476541710  1.183660e-02
sample98   0.0269546160 -5.978398e-03
sample99   0.0728179461  4.597884e-02
sample100 -0.0413398038  1.079347e-02
sample101  0.0087536994 -6.796076e-02
sample102  0.0032509529  3.932612e-03
sample103  0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105  0.0294940465 -7.140722e-03
sample106  0.0686472054  1.462895e-02
sample107  0.0748635927  8.401339e-03
sample108  0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110  0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112  0.0570870472  1.066018e-02
sample113 -0.0200110554  1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586  1.089843e-02
sample121  0.0542443861  3.861344e-02
sample122  0.0178575357  3.027138e-02
sample123  0.0775020581 -1.636852e-02
sample124 -0.0460701050  1.814758e-02
sample125  0.0543846585  2.075898e-03
sample126 -0.0729417144  3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128  0.0908136899  3.758801e-02
sample129  0.0552445878 -1.879062e-02
sample130  0.0007128089 -1.294308e-02
sample131 -0.0693311345  7.357082e-03
sample132 -0.0556565156  3.126995e-02
sample133  0.0375870104 -1.977240e-02
sample134 -0.1229130924  3.159495e-02
sample135  0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201  1.040683e-02
sample140  0.0452288969 -1.876279e-02
sample141 -0.0189142561  2.247042e-02
sample142  0.0297545566  1.280524e-02
sample143  0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491  5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147  0.0726083535 -1.239968e-02
sample148 -0.0284795794  3.389732e-02
sample149  0.0082261455 -6.399305e-02
sample150 -0.0765013197  2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152  0.0403422737 -2.714879e-02
sample153  0.0629117719  7.425085e-02
sample154  0.0551622927 -3.548984e-02
sample155  0.0654439133 -1.005306e-02
sample156  0.0209310714 -1.390213e-02
sample157  0.0851522597  6.577150e-03
sample158  0.0208354599 -4.663078e-03
sample159 -0.0498794349  1.913257e-02
sample160  0.0216074437  1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162  0.0963663017  5.705881e-02
sample163 -0.1009542191  7.174224e-02
sample164  0.0109881996  1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357  2.673084e-02
sample167 -0.0825048036  2.278863e-03
sample168  0.0486147429  1.793843e-02
sample169  0.0302506727  8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
                   [,1]          [,2]
sample1   -0.0621842115 -1.364509e-02
sample2    0.0944623785  9.720892e-03
sample3    0.0406196267 -2.236338e-02
sample4   -0.0229316496 -3.932487e-04
sample5   -0.0157330047  3.231033e-03
sample6   -0.0945794025  3.120720e-02
sample7   -0.0854427118 -1.052880e-02
sample8   -0.1376625920  4.286608e-02
sample9   -0.0377115311 -1.415134e-02
sample10   0.0035244506  1.280825e-02
sample11   0.0016639987 -1.717895e-02
sample12  -0.0781403168  1.884368e-02
sample13   0.0938400516  2.838858e-02
sample14  -0.0759839772  1.810989e-02
sample15  -0.0068340837 -2.705361e-03
sample16  -0.0590150849  4.757848e-02
sample17   0.1178805097  2.040526e-02
sample18   0.0767858320  1.756604e-02
sample19   0.0157112113 -2.172867e-02
sample20   0.0485318300 -2.327033e-02
sample21   0.0185928176  4.777095e-02
sample22  -0.0191358702 -2.329775e-02
sample23  -0.0672994194 -1.535656e-03
sample24   0.1047476642  5.935707e-02
sample25   0.0329844953 -1.358036e-02
sample26   0.1154952052  1.741529e-02
sample27   0.0133849853 -3.590922e-02
sample28   0.0821554039  2.042376e-02
sample29  -0.0567643690 -2.123848e-02
sample30   0.1016073931 -1.134728e-03
sample31  -0.0880396372  3.670548e-02
sample32   0.0300363338  1.182406e-02
sample33  -0.0467252272  3.739254e-03
sample34   0.0783666394  1.203777e-02
sample35   0.0424227097 -1.118559e-02
sample36  -0.1107646166 -1.143464e-02
sample37  -0.0191667664 -2.246060e-02
sample38   0.0155968095 -2.909621e-02
sample39   0.0746847148  7.148218e-03
sample40  -0.0517028178 -2.137267e-02
sample41   0.0234979494 -3.723018e-02
sample42   0.0388797356 -8.557228e-03
sample43   0.0149555568  7.210002e-03
sample44  -0.1150305613  3.461805e-02
sample45   0.0846146236  3.486020e-02
sample46   0.0884426404 -3.246853e-02
sample47   0.0748644971 -8.083045e-03
sample48  -0.0012033198 -9.403647e-03
sample49  -0.0872662737  3.616245e-02
sample50   0.0066941314 -5.284863e-02
sample51  -0.0411777630  3.791830e-02
sample52  -0.0379355780  2.180834e-02
sample53  -0.0851639886  4.751761e-02
sample54   0.0288006248  7.184424e-03
sample55  -0.0164920835  5.919925e-05
sample56   0.0355115616  1.951043e-02
sample57  -0.0141146068  3.492409e-02
sample58  -0.0015636132  9.862883e-03
sample59  -0.0390656483  3.590929e-03
sample60  -0.0139454780  3.963030e-03
sample61  -0.0106410274  1.919705e-02
sample62   0.0236748439 -5.922677e-03
sample63  -0.0846790877  3.839102e-02
sample64  -0.1202581015  2.846469e-02
sample65   0.0050548584  6.328644e-03
sample66   0.0028013072  1.291807e-02
sample67  -0.1231623009 -2.112565e-02
sample68  -0.0437782161 -2.845072e-02
sample69  -0.0501199692  2.053469e-03
sample70  -0.0140278645 -2.027157e-02
sample71   0.0057489505 -4.085977e-02
sample72   0.0511212704 -5.522408e-03
sample73   0.0828141409  7.431582e-03
sample74  -0.0085959456  1.772951e-02
sample75  -0.0312180394 -6.636869e-03
sample76  -0.0519051781 -1.640191e-02
sample77  -0.0925924762 -6.907800e-03
sample78   0.1163971046  2.251122e-02
sample79  -0.0240906926 -4.887766e-02
sample80  -0.0221327065 -6.730703e-03
sample81  -0.0072114968 -2.254399e-02
sample82   0.1204416674 -9.907422e-03
sample83   0.0386739485 -1.171663e-03
sample84   0.0195988488 -1.033806e-02
sample85  -0.0877680171 -1.725057e-03
sample86   0.1023541048 -1.062501e-02
sample87   0.0425213089 -1.356865e-02
sample88   0.0244788514  1.180820e-02
sample89   0.0804276691  2.188588e-02
sample90  -0.0074639871 -6.140721e-03
sample91  -0.1278832404 -6.485140e-02
sample92  -0.0162199697 -5.048358e-02
sample93   0.0769344893 -3.045135e-03
sample94  -0.0104345587 -3.593172e-03
sample95  -0.0260058453 -2.330475e-02
sample96  -0.0025018700 -1.433516e-02
sample97  -0.0492358305  7.774183e-03
sample98   0.0279220220 -3.862141e-03
sample99   0.0813921923  3.487339e-02
sample100 -0.0428797405  7.112807e-03
sample101  0.0032855240 -4.940743e-02
sample102  0.0038439317  2.938008e-03
sample103  0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105  0.0314853405 -4.656633e-03
sample106  0.0726456731  1.192390e-02
sample107  0.0807342975  7.508627e-03
sample108  0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110  0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112  0.0599954082  8.820317e-03
sample113 -0.0195006577  1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131  6.937326e-03
sample121  0.0613899126  2.915307e-02
sample122  0.0218424338  2.241775e-02
sample123  0.0809008460 -1.051759e-02
sample124 -0.0472109313  1.239887e-02
sample125  0.0583180947  2.521167e-03
sample126 -0.0753941872  2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128  0.1000212216  2.908091e-02
sample129  0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877  4.091672e-03
sample132 -0.0566219024  2.179861e-02
sample133  0.0384172955 -1.372840e-02
sample134 -0.1280862736  2.077912e-02
sample135  0.0592633273  6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861  6.729304e-03
sample140  0.0468926306 -1.285498e-02
sample141 -0.0186248693  1.605439e-02
sample142  0.0328031246  9.887746e-03
sample143  0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310  3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147  0.0765025670 -7.714769e-03
sample148 -0.0276016641  2.420589e-02
sample149  0.0027545308 -4.653007e-02
sample150 -0.0792296010  1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152  0.0409796547 -1.907063e-02
sample153  0.0734301757  5.528780e-02
sample154  0.0557740684 -2.487723e-02
sample155  0.0689436560 -6.127635e-03
sample156  0.0212272938 -9.747423e-03
sample157  0.0911931194  6.355708e-03
sample158  0.0220840645 -3.016357e-03
sample159 -0.0513244242  1.304175e-02
sample160  0.0246213576  1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162  0.1078802043  4.337260e-02
sample163 -0.1017965082  5.047171e-02
sample164  0.0119430799  9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180  1.899222e-02
sample167 -0.0872832229  1.516582e-04
sample168  0.0540714512  1.397701e-02
sample169  0.0328432652  7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
                   [,1]          [,2]
sample1    0.0133684846  2.195848e-02
sample2    0.0254157197 -1.058416e-02
sample3   -0.0049551479 -4.840017e-03
sample4    0.0310390570 -1.063929e-02
sample5    0.0046941318 -6.488426e-03
sample6   -0.0107406753 -1.026702e-02
sample7   -0.0225157631  2.624712e-04
sample8    0.0141320952 -9.505821e-03
sample9    0.0029681280  2.078210e-02
sample10   0.0131729174 -2.275042e-03
sample11  -0.0004164298  1.994019e-02
sample12  -0.0095211620  3.759883e-02
sample13   0.0091018604 -7.953956e-03
sample14  -0.0106557524 -9.181659e-03
sample15  -0.0249924121  3.262724e-02
sample16  -0.0156216400  1.375700e-02
sample17  -0.0019382446  1.073994e-03
sample18  -0.0221072481 -8.703592e-03
sample19   0.0146917619 -1.311712e-02
sample20  -0.0160353760  1.826290e-02
sample21   0.0035947899 -9.616341e-03
sample22  -0.0225060762 -2.532589e-03
sample23   0.0310000683  3.033060e-03
sample24   0.0499544372  1.809450e-02
sample25   0.0284442301 -1.932558e-02
sample26   0.0188220043  2.146985e-02
sample27  -0.0257763219 -1.999228e-03
sample28   0.0120888648  1.125834e-02
sample29  -0.0236482520  4.426726e-02
sample30  -0.0385486305 -2.055935e-02
sample31  -0.0181539336 -5.877838e-03
sample32  -0.0302630460 -2.607192e-03
sample33  -0.0319565715 -1.562628e-02
sample34  -0.0197970124  9.906813e-03
sample35  -0.0247412713 -5.434440e-03
sample36  -0.0386259060 -3.190394e-02
sample37  -0.0566199273 -4.192574e-02
sample38  -0.0142060273  2.259644e-02
sample39   0.0053589035  1.076485e-02
sample40  -0.0552546493 -3.819896e-02
sample41  -0.0013089975  9.278818e-05
sample42   0.0137252142 -1.664652e-02
sample43  -0.0151259626 -6.290953e-03
sample44   0.0617391754 -1.442883e-02
sample45   0.0231410886  1.163143e-03
sample46  -0.0148898209 -1.384176e-04
sample47  -0.0187252536  1.221690e-02
sample48   0.0432839432  1.416671e-02
sample49   0.0160818605 -3.588745e-02
sample50   0.0059333545  4.067003e-02
sample51  -0.0142914866  7.776270e-03
sample52  -0.0086339952  7.208917e-03
sample53  -0.0207386980  6.272432e-03
sample54  -0.0039856719 -1.316934e-02
sample55  -0.0056217017  5.692315e-03
sample56   0.0000123292  8.978290e-04
sample57  -0.0095805555  1.324253e-02
sample58  -0.0124160295 -7.326376e-03
sample59  -0.0400195442 -1.349736e-02
sample60  -0.0460063358  2.770091e-02
sample61  -0.0245266456  1.470710e-02
sample62  -0.0366022783 -3.437352e-03
sample63   0.0013742171  3.288796e-02
sample64  -0.0070599859  2.739588e-02
sample65   0.0041201911  1.498268e-02
sample66   0.0143173351 -1.968812e-02
sample67  -0.0467477531 -1.929938e-02
sample68  -0.0306751978 -1.436184e-02
sample69  -0.0125317217  4.130407e-03
sample70  -0.0068071487  8.080857e-03
sample71   0.0169170264 -7.027348e-03
sample72  -0.0346909749 -1.333770e-02
sample73  -0.0280506153  1.493843e-02
sample74  -0.0182611498  3.294697e-03
sample75  -0.0120563964  8.974612e-03
sample76   0.0001437236 -4.253184e-02
sample77   0.0065330299 -5.252886e-02
sample78   0.0288278141 -1.127782e-02
sample79   0.0503961481 -1.023318e-02
sample80  -0.0207693429  3.648391e-02
sample81   0.0163562768 -9.074596e-03
sample82  -0.0084317129 -1.478976e-02
sample83  -0.0474097918 -1.103126e-02
sample84   0.0177181395 -7.191197e-03
sample85  -0.0342718548 -3.082360e-02
sample86  -0.0261671791 -1.089491e-02
sample87  -0.0009486358 -2.411514e-02
sample88   0.0020528931 -2.894615e-02
sample89  -0.0189361111 -2.638639e-03
sample90  -0.0009863658 -2.390075e-02
sample91  -0.0124352695  8.153234e-02
sample92   0.0564264106 -8.909537e-03
sample93  -0.0081461774  1.570851e-02
sample94  -0.0054896581  1.547251e-02
sample95   0.0224073150 -4.374348e-04
sample96   0.0173528924 -3.050441e-03
sample97   0.0067948115  5.008237e-03
sample98  -0.0116030825  1.498764e-02
sample99   0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101  0.0124923691  3.091503e-02
sample102  0.0650835386 -1.367400e-02
sample103 -0.0042741828  7.855985e-03
sample104  0.0250591040 -4.171938e-03
sample105  0.0157516368 -3.121990e-02
sample106  0.0060593853 -5.101693e-03
sample107 -0.0098329626  1.044506e-02
sample108  0.0044269853  4.142036e-03
sample109  0.0572473486  1.517542e-02
sample110  0.0090474827 -5.119868e-03
sample111  0.0444263015  7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113  0.0241047399  6.706740e-03
sample114  0.0074558775 -4.728652e-03
sample115  0.0611851433  1.117210e-02
sample116  0.0432646951 -1.380556e-02
sample117  0.0516750066 -3.575617e-02
sample118  0.0139942100 -3.279138e-03
sample119  0.0291722987  5.587946e-02
sample120  0.0103515853 -1.690016e-03
sample121 -0.0091396331  3.552116e-02
sample122  0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124  0.0283466326  3.127845e-03
sample125  0.0016472378 -2.770692e-02
sample126 -0.0286529417  3.489336e-02
sample127 -0.0010224500  7.483214e-03
sample128  0.0209049296  2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968  4.637390e-03
sample132  0.0198526786  5.723983e-04
sample133  0.0088812957 -9.988115e-03
sample134 -0.0137484514  1.172591e-02
sample135 -0.0220314568  1.347465e-02
sample136 -0.0185173353  5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138  0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762  2.456863e-02
sample141  0.0058369763 -1.420854e-02
sample142  0.0207886071 -1.188764e-02
sample143  0.0092832598 -1.324238e-02
sample144  0.0028442140  3.627979e-03
sample145  0.0199749569  2.862202e-03
sample146 -0.0182236697  1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148  0.0065435868 -1.572917e-02
sample149  0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151  0.0245166984 -6.888241e-03
sample152  0.0107259913  3.314630e-02
sample153  0.0550963965  3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574  2.642246e-03
sample156 -0.0117803505  2.698265e-02
sample157 -0.0096167165  1.433840e-02
sample158 -0.0101754772  9.137620e-03
sample159  0.0120662931 -2.565236e-02
sample160 -0.0132238202  2.916023e-03
sample161  0.0274491966 -1.748284e-02
sample162  0.0012482909  3.152261e-02
sample163  0.0042031315  1.830701e-02
sample164  0.0174896157 -1.175915e-02
sample165  0.0097517662 -6.119019e-03
sample166  0.0190134679 -1.121582e-02
sample167 -0.0044140836  4.665585e-03
sample168  0.0049689168 -1.941822e-02
sample169 -0.0209802098  3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
                   [,1]          [,2]
sample1   -0.0515543627 -0.0305856787
sample2   -0.0144993256  0.0236342950
sample3   -0.0371833108 -0.0140263348
sample4    0.0068945388 -0.0132539692
sample5    0.0215035333 -0.0663338101
sample6   -0.0187055152  0.0088773016
sample7   -0.0061521552  0.0064029054
sample8   -0.0210874459  0.0334652901
sample9    0.0516865043 -0.0291142799
sample10   0.0059440366 -0.0527217447
sample11   0.0393010793 -0.0200624712
sample12  -0.0420837100  0.0131331362
sample13   0.0333252565  0.0818552509
sample14  -0.0190062644  0.0160202175
sample15  -0.0030968049 -0.0189230681
sample16  -0.0004452158  0.0018880102
sample17  -0.0185848615  0.0240170131
sample18  -0.0273093598  0.0230213640
sample19  -0.0217761111 -0.0445894441
sample20   0.0245820821  0.0159812738
sample21   0.0034527644 -0.0400016054
sample22  -0.0340789054  0.0039289109
sample23  -0.0010344929 -0.0310161212
sample24   0.0289468503  0.0760962436
sample25  -0.0119098496 -0.0122798760
sample26  -0.0181001057  0.0517892852
sample27   0.0050465417 -0.0086515844
sample28   0.0057491502  0.0358830107
sample29  -0.0051104246  0.0116605117
sample30  -0.0103085904  0.0039678538
sample31  -0.0319929858  0.0090606113
sample32  -0.0036232521 -0.0328202010
sample33  -0.0534742153  0.0024751837
sample34  -0.0067495749 -0.0111000311
sample35   0.0378745721  0.0465929296
sample36   0.0647886800  0.0359987924
sample37   0.0488441236  0.0492906912
sample38  -0.0251514062  0.0197110110
sample39  -0.0085428066 -0.0105117852
sample40   0.0379324087  0.0440810741
sample41  -0.0044199152 -0.0128820644
sample42  -0.0292553573 -0.0067045265
sample43  -0.0077829155 -0.0510178219
sample44   0.0045122248  0.0479660309
sample45  -0.0074444298 -0.0051116726
sample46  -0.0088025512  0.0196186661
sample47   0.0076696301  0.0215947965
sample48   0.0290108585 -0.0175568376
sample49  -0.0141754858  0.0184717099
sample50   0.0006282201 -0.0233054373
sample51   0.0441995177 -0.0410022921
sample52   0.0715329391 -0.0399499475
sample53  -0.0095954087 -0.0029140909
sample54   0.0048933768 -0.0281884386
sample55   0.0327325487 -0.0532290012
sample56   0.0323068984 -0.0256595538
sample57   0.0806603122 -0.0286748097
sample58  -0.0064792049 -0.0006945349
sample59   0.0088958941  0.0067389649
sample60   0.0874124612  0.0431964341
sample61   0.0577604571 -0.0326112099
sample62  -0.0313318464  0.0224391756
sample63  -0.0233625220  0.0125110562
sample64  -0.0086426068  0.0148770341
sample65   0.0025256193 -0.0404466327
sample66   0.0006014071 -0.0471576264
sample67   0.0706087042  0.0516228406
sample68   0.0082301011  0.0033109509
sample69  -0.0475076743  0.0001452708
sample70  -0.0600773716  0.0089986962
sample71  -0.0096321627 -0.0050761187
sample72  -0.0031773546 -0.0166221542
sample73  -0.0113700517 -0.0191726684
sample74  -0.0014179662 -0.0608101325
sample75   0.0041911740 -0.0399981269
sample76  -0.0055326449  0.0353114263
sample77  -0.0260214459  0.0305731380
sample78  -0.0119267436  0.0632236007
sample79   0.0186017239  0.0027402910
sample80   0.0241047889 -0.0472697181
sample81  -0.0220288317 -0.0079577210
sample82  -0.0180751258  0.0639051029
sample83  -0.0256671713 -0.0125898269
sample84   0.0161392598 -0.0567222449
sample85   0.0139988188  0.0322763454
sample86  -0.0198382995  0.0389225776
sample87   0.0266270281 -0.0032979996
sample88   0.0515677078  0.0117902495
sample89   0.0014022125 -0.0140510488
sample90  -0.0375949749  0.0044004551
sample91   0.0310397965  0.0440610926
sample92   0.0270570567  0.0324380452
sample93  -0.0215009202  0.0063993941
sample94  -0.0415702912 -0.0037692077
sample95  -0.0168416047  0.0010019120
sample96  -0.0285582661 -0.0187991000
sample97  -0.0490843868 -0.0266760748
sample98  -0.0171579033 -0.0112897471
sample99  -0.0271316525  0.0232395583
sample100 -0.0301789816  0.0305498693
sample101 -0.0264371151  0.0170723968
sample102  0.0012767734 -0.0248949597
sample103  0.0055214687 -0.0030040587
sample104  0.0251346074 -0.0165212671
sample105  0.0062424215 -0.0400309901
sample106  0.0069768684  0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679  0.0023637162
sample109 -0.0014762845  0.0165583675
sample110  0.0036971063  0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112  0.0046098120 -0.0048009350
sample113  0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453  0.0135805368
sample116  0.0183575759  0.0665377581
sample117  0.0227640036 -0.0012287760
sample118  0.0015695248  0.0472617382
sample119  0.0190084932  0.0590034062
sample120 -0.0449645755  0.0072755697
sample121  0.0077307184  0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123  0.0016959300  0.0028593594
sample124 -0.0365091615  0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408  0.0019165544
sample127 -0.0494064872  0.0088209044
sample128 -0.0155454766  0.0186819802
sample129 -0.0184340400  0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422  0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133  0.0204029276 -0.0282209049
sample134  0.0175513332  0.0262883962
sample135  0.0029009925  0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323  0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139  0.0073103935  0.0308956174
sample140  0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142  0.0268670676 -0.0239229634
sample143  0.0421049126 -0.0110888235
sample144  0.0017253664 -0.0341766012
sample145  0.0681741320 -0.0073526377
sample146 -0.0239965222  0.0118396767
sample147 -0.0063453522  0.0183130585
sample148  0.0230825251 -0.0379753037
sample149  0.0223298673  0.0188909118
sample150  0.0055709108  0.0174179009
sample151  0.0039177786 -0.0233533275
sample152  0.0134325667  0.0302344591
sample153  0.0511990309  0.0730230140
sample154  0.0006698324  0.0154177486
sample155  0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599  0.0154934573
sample158  0.0201775524 -0.0332982124
sample159 -0.0086909001  0.0073496711
sample160  0.0295437331 -0.0555734536
sample161  0.0332754288  0.0033779619
sample162  0.0121954537  0.0433540412
sample163 -0.0173490933  0.0227219128
sample164  0.0143374783 -0.0453542590
sample165  0.0343612593 -0.0511194536
sample166 -0.0157536004  0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919  0.0060747155
sample169  0.0116231468 -0.0015112800
> 
> ## 3.3 Plotting VAF
> 
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
> 
> # JIVE plotVAF
> plotVAF(jiveRes)
> 
> 
> #########################
> ## PART 4. Plot Results
> 
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+              combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+              background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+              axisSize=NULL,titleSize=NULL)
> 
> # Scores for common part. JIVE
> plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common",
+              combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+              background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+              axisSize=NULL,titleSize=NULL)
> 
> # Scores for common part. O2PLS.
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for common part. O2PLS.
> plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common",
+              combined=TRUE,block=NULL,color="classname",shape=NULL,
+              labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+              labelSize=NULL,axisSize=NULL,titleSize=NULL)
> 
> 
> # Scores for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for distinctive part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual",
+              combined=TRUE,block=NULL,color="classname",shape=NULL,
+              labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+              labelSize=NULL,axisSize=NULL,titleSize=NULL)
> 
> # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block)
> p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for common and distinctive part. DISCO  (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Loadings for common part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> # Loadings for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> # Combined plot for loadings from common and distinctive part  (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> 
> ## Plot scores and loadings togheter: Common components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+         combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+         background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+         axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> ## Plot scores and loadings togheter:  Common components O2PLS
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> ## Plot scores and loadings togheter: Distintive components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
  14.01    0.46   14.62 

Example timings

STATegRa.Rcheck/examples_i386/STATegRa-Ex.timings

nameusersystemelapsed
STATegRaUsersGuide000
STATegRa_data0.350.000.34
STATegRa_data_TCGA_BRCA000
bioDist0.690.020.70
bioDistFeature0.450.030.49
bioDistFeaturePlot0.410.030.43
bioDistW0.390.020.41
bioDistWPlot0.390.030.42
bioMap0.000.010.02
combiningMappings0.010.000.01
createOmicsExpressionSet0.170.000.18
getInitialData0.630.110.73
getLoadings0.650.170.81
getMethodInfo0.670.160.83
getPreprocessing1.030.201.23
getScores0.650.140.80
getVAF0.600.130.72
holistOmics000
modelSelection2.050.442.49
omicsCompAnalysis4.420.184.61
omicsNPC0.020.000.01
plotRes5.190.075.25
plotVAF4.460.034.50

STATegRa.Rcheck/examples_x64/STATegRa-Ex.timings

nameusersystemelapsed
STATegRaUsersGuide000
STATegRa_data0.240.030.27
STATegRa_data_TCGA_BRCA000
bioDist0.490.020.50
bioDistFeature0.400.010.42
bioDistFeaturePlot0.470.030.50
bioDistW0.380.000.37
bioDistWPlot0.450.020.47
bioMap000
combiningMappings0.010.000.02
createOmicsExpressionSet0.190.010.20
getInitialData0.890.151.03
getLoadings0.640.100.75
getMethodInfo0.910.151.05
getPreprocessing1.200.401.61
getScores1.070.131.18
getVAF0.850.171.04
holistOmics000
modelSelection2.110.472.58
omicsCompAnalysis5.340.205.55
omicsNPC000
plotRes6.930.066.98
plotVAF4.730.114.84