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CHECK report for STATegRa on tokay2

This page was generated on 2019-10-16 12:31:36 -0400 (Wed, 16 Oct 2019).

Package 1596/1741HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
STATegRa 1.20.0
David Gomez-Cabrero , NĂºria Planell
Snapshot Date: 2019-10-15 17:01:26 -0400 (Tue, 15 Oct 2019)
URL: https://git.bioconductor.org/packages/STATegRa
Branch: RELEASE_3_9
Last Commit: 1457952
Last Changed Date: 2019-05-02 11:53:50 -0400 (Thu, 02 May 2019)
malbec2 Linux (Ubuntu 18.04.2 LTS) / x86_64  OK  OK  OK UNNEEDED, same version exists in internal repository
tokay2 Windows Server 2012 R2 Standard / x64  OK  OK [ OK ] OK UNNEEDED, same version exists in internal repository
celaya2 OS X 10.11.6 El Capitan / x86_64  OK  OK  OK  OK UNNEEDED, same version exists in internal repository

Summary

Package: STATegRa
Version: 1.20.0
Command: C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=C:\Users\biocbuild\bbs-3.9-bioc\R\library --no-vignettes --timings STATegRa_1.20.0.tar.gz
StartedAt: 2019-10-16 07:23:37 -0400 (Wed, 16 Oct 2019)
EndedAt: 2019-10-16 07:30:43 -0400 (Wed, 16 Oct 2019)
EllapsedTime: 425.3 seconds
RetCode: 0
Status:  OK  
CheckDir: STATegRa.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=C:\Users\biocbuild\bbs-3.9-bioc\R\library --no-vignettes --timings STATegRa_1.20.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory 'C:/Users/biocbuild/bbs-3.9-bioc/meat/STATegRa.Rcheck'
* using R version 3.6.1 (2019-07-05)
* 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 '1.20.0'
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'STATegRa' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking 'build' directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* loading checks for arch 'i386'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* loading checks for arch 'x64'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
modelSelection,list-numeric-character: no visible binding for global
  variable 'components'
modelSelection,list-numeric-character: no visible binding for global
  variable 'mylabel'
plotVAF,caClass: no visible binding for global variable 'comp'
plotVAF,caClass: no visible binding for global variable 'VAF'
plotVAF,caClass: no visible binding for global variable 'block'
selectCommonComps,list-numeric: no visible binding for global variable
  'comps'
selectCommonComps,list-numeric: no visible binding for global variable
  'block'
selectCommonComps,list-numeric: no visible binding for global variable
  'comp'
selectCommonComps,list-numeric: no visible binding for global variable
  'ratio'
Undefined global functions or variables:
  VAF block comp components comps mylabel ratio
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of 'data' directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking files in 'vignettes' ... OK
* checking examples ...
** running examples for arch 'i386' ... OK
Examples with CPU or elapsed time > 5s
                  user system elapsed
plotRes           7.11   0.14    7.25
omicsCompAnalysis 6.27   0.24    6.50
plotVAF           5.74   0.00    5.74
** running examples for arch 'x64' ... OK
Examples with CPU or elapsed time > 5s
        user system elapsed
plotRes 5.39   0.05    5.53
* 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-3.9-bioc/meat/STATegRa.Rcheck/00check.log'
for details.



Installation output

STATegRa.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\cygwin\bin\curl.exe -O https://malbec2.bioconductor.org/BBS/3.9/bioc/src/contrib/STATegRa_1.20.0.tar.gz && rm -rf STATegRa.buildbin-libdir && mkdir STATegRa.buildbin-libdir && C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=STATegRa.buildbin-libdir STATegRa_1.20.0.tar.gz && C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD INSTALL STATegRa_1.20.0.zip && rm STATegRa_1.20.0.tar.gz STATegRa_1.20.0.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 3177k  100 3177k    0     0  34.3M      0 --:--:-- --:--:-- --:--:-- 37.3M

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_1.20.0.zip
* DONE (STATegRa)
* installing to library 'C:/Users/biocbuild/bbs-3.9-bioc/R/library'
package 'STATegRa' successfully unpacked and MD5 sums checked

Tests output

STATegRa.Rcheck/tests_i386/runTests.Rout


R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

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

Distinctive components
[[1]]
[1] 0

[[2]]
[1] 0

Common components
[1] 2

Distinctive components
[[1]]
[1] 1

[[2]]
[1] 1

Common components
[1] 2

Distinctive components
[[1]]
[1] 2

[[2]]
[1] 2



RUNIT TEST PROTOCOL -- Wed Oct 16 07:28:14 2019 
*********************************************** 
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 
   3.98    0.25    7.51 

STATegRa.Rcheck/tests_x64/runTests.Rout


R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

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

Distinctive components
[[1]]
[1] 0

[[2]]
[1] 0

Common components
[1] 2

Distinctive components
[[1]]
[1] 1

[[2]]
[1] 1

Common components
[1] 2

Distinctive components
[[1]]
[1] 2

[[2]]
[1] 2



RUNIT TEST PROTOCOL -- Wed Oct 16 07:30:27 2019 
*********************************************** 
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 
   3.26    0.21    3.46 

STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsCLUST.Rout


R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

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

Attaching package: 'BiocGenerics'

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

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

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

    IQR, mad, sd, var, xtabs

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

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

Welcome to Bioconductor

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

> 
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+                  distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+                  map.name = "id",
+                  map.metadata = list(),
+                  params = list())
> 
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
> 
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
> 
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+                        bioDistList = bioDistList,
+                        weights=weights)
> length(bioDistWList)
[1] 4
> 
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
> 
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+                listDistW = bioDistWList,
+                method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
> 
> #############################################
> ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
> #############################################
> 
> ## IDH1
> 
> IDH1.F<-bioDistFeature(Feature = "IDH1" ,
+                        listDistW = bioDistWList,
+                        threshold.cor=0.7)
> bioDistFeaturePlot(data=IDH1.F)
> 
> ## PDGFRA
> 
> #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
> #                       listDistW = bioDistWList,
> #                       threshold.cor=0.7)
> #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
> 
> ## EGFR
> #EGFR.F<-bioDistFeature(Feature = "EGFR" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.7)
> #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
> 
> ## MGMT
> #MGMT.F<-bioDistFeature(Feature = "MGMT" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.5)
> #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
  32.12    0.98   33.12 

STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsCLUST.Rout


R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

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

Attaching package: 'BiocGenerics'

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

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

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

    IQR, mad, sd, var, xtabs

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

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

Welcome to Bioconductor

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

> 
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+                  distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+                  map.name = "id",
+                  map.metadata = list(),
+                  params = list())
> 
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
> 
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
> 
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+                        bioDistList = bioDistList,
+                        weights=weights)
> length(bioDistWList)
[1] 4
> 
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
> 
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+                listDistW = bioDistWList,
+                method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
4: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
5: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
6: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
7: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
> 
> #############################################
> ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
> #############################################
> 
> ## IDH1
> 
> IDH1.F<-bioDistFeature(Feature = "IDH1" ,
+                        listDistW = bioDistWList,
+                        threshold.cor=0.7)
> bioDistFeaturePlot(data=IDH1.F)
> 
> ## PDGFRA
> 
> #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
> #                       listDistW = bioDistWList,
> #                       threshold.cor=0.7)
> #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
> 
> ## EGFR
> #EGFR.F<-bioDistFeature(Feature = "EGFR" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.7)
> #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
> 
> ## MGMT
> #MGMT.F<-bioDistFeature(Feature = "MGMT" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.5)
> #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
  22.90    0.73   23.62 

STATegRa.Rcheck/tests_i386/STATegRa_Example.omicsNPC.Rout


R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 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 
  79.93    0.25   80.23 

STATegRa.Rcheck/tests_x64/STATegRa_Example.omicsNPC.Rout


R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 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 
  87.26    0.32   87.59 

STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsPCA.Rout


R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 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    0.0781574338 -0.0431502524
sample2   -0.1192218429  0.0294087989
sample3   -0.0531412077 -0.0746839818
sample4    0.0292975121 -0.0005960583
sample5    0.0202091749  0.0110463662
sample6    0.1226089054  0.1053467305
sample7    0.1078928140 -0.0322475302
sample8    0.1782895267  0.1449363722
sample9    0.0468698109 -0.0455174280
sample10  -0.0036030532  0.0420110750
sample11  -0.0035566476 -0.0566292531
sample12   0.1006128920  0.0641380864
sample13  -0.1174408390  0.0907488282
sample14   0.0981203265  0.0617738338
sample15   0.0085334337 -0.0087013048
sample16   0.0783148644  0.1581294788
sample17  -0.1483609923  0.0638581911
sample18  -0.0963086232  0.0556640544
sample19  -0.0217244079 -0.0720086522
sample20  -0.0635636388 -0.0779652789
sample21  -0.0201840373  0.1566391284
sample22   0.0218268782 -0.0764104037
sample23   0.0852041994 -0.0032689403
sample24  -0.1287170735  0.1924542631
sample25  -0.0430574160 -0.0456566708
sample26  -0.1453896863  0.0541511716
sample27  -0.0197488765 -0.1185656308
sample28  -0.1025336333  0.0650685294
sample29   0.0706018525 -0.0682987685
sample30  -0.1295627490 -0.0066768587
sample31   0.1147449129  0.1232686939
sample32  -0.0374310852  0.0380178526
sample33   0.0599516042  0.0136867730
sample34  -0.0984200803  0.0375321400
sample35  -0.0543098361 -0.0378105365
sample36   0.1403625455 -0.0343755199
sample37   0.0228941921 -0.0732845051
sample38  -0.0222077215 -0.0962594617
sample39  -0.0941738496  0.0215199043
sample40   0.0643801198 -0.0687869812
sample41  -0.0327637998 -0.1232188155
sample42  -0.0500431832 -0.0292473667
sample43  -0.0184498807  0.0233011331
sample44   0.1487898701  0.1171353209
sample45  -0.1050774212  0.1123201079
sample46  -0.1151195683 -0.1094028517
sample47  -0.0962593712 -0.0288463393
sample48   0.0004837283 -0.0310278611
sample49   0.1135207757  0.1213972712
sample50  -0.0123553099 -0.1740743842
sample51   0.0550529843  0.1258887037
sample52   0.0499121195  0.0728544823
sample53   0.1119773648  0.1588014216
sample54  -0.0360055676  0.0228575673
sample55   0.0210418989  0.0006731873
sample56  -0.0434169254  0.0633126106
sample57   0.0197824570  0.1150713882
sample58   0.0030439899  0.0326098197
sample59   0.0500253147  0.0129419529
sample60   0.0184278658  0.0136086191
sample61   0.0150299401  0.0635026299
sample62  -0.0304763853 -0.0201318777
sample63   0.1102252462  0.1285976892
sample64   0.1552588080  0.0971168446
sample65  -0.0058503062  0.0207115380
sample66  -0.0025605364  0.0424319742
sample67   0.1546634841 -0.0661715612
sample68   0.0536369309 -0.0923683261
sample69   0.0640330395  0.0081983185
sample70   0.0163517781 -0.0663230104
sample71  -0.0102537619 -0.1345921626
sample72  -0.0654196013 -0.0196119084
sample73  -0.1048556117  0.0220938591
sample74   0.0123799485  0.0586115349
sample75   0.0392077949 -0.0209754884
sample76   0.0648953390 -0.0524764385
sample77   0.1172922136 -0.0201186793
sample78  -0.1463068119  0.0708472037
sample79   0.0265211175 -0.1603308525
sample80   0.0279737161 -0.0214204844
sample81   0.0079211500 -0.0738451078
sample82  -0.1544236492 -0.0361467848
sample83  -0.0494211358 -0.0050047996
sample84  -0.0259038483 -0.0346549759
sample85   0.1116484371 -0.0031497298
sample86  -0.1306483007 -0.0377214940
sample87  -0.0554778202 -0.0459748880
sample88  -0.0301623867  0.0382197624
sample89  -0.1016866712  0.0694034016
sample90   0.0086819892 -0.0201320150
sample91   0.1578625358 -0.2097827870
sample92   0.0170936810 -0.1655807464
sample93  -0.0979806808 -0.0121512212
sample94   0.0131484112 -0.0114932096
sample95   0.0315682628 -0.0758859339
sample96   0.0024125619 -0.0470135762
sample97   0.0634545420  0.0270331777
sample98  -0.0359374626 -0.0135488370
sample99  -0.1009163341  0.1124779782
sample100  0.0551753140  0.0246489590
sample101 -0.0080118878 -0.1627368675
sample102 -0.0046444346  0.0095631449
sample103 -0.0472523169 -0.0940393267
sample104  0.0198159470 -0.0591091743
sample105 -0.0400237806 -0.0160912097
sample106 -0.0923808424  0.0369017681
sample107 -0.1019373937  0.0224954181
sample108 -0.0877091652 -0.0128834253
sample109  0.0864824369 -0.0900942104
sample110 -0.1223115543 -0.0096085780
sample111  0.0257354621 -0.0936169417
sample112 -0.0765286602  0.0270347648
sample113  0.0258803225  0.0377497098
sample114  0.0021138932 -0.0882014960
sample115  0.0303460183 -0.0723586032
sample116  0.0780508411 -0.0685066582
sample117  0.0536898088 -0.0911908663
sample118  0.0666651149 -0.0236231132
sample119  0.1021871635 -0.2324936947
sample120  0.0750216560  0.0243379040
sample121 -0.0756936405  0.0942950636
sample122 -0.0259628101  0.0731987058
sample123 -0.1037846251 -0.0369197173
sample124  0.0611207920  0.0421723478
sample125 -0.0738472718  0.0066950123
sample126  0.0972916457  0.0762640114
sample127  0.0824697645 -0.0096637343
sample128 -0.1249407658  0.0929312548
sample129 -0.0734067502 -0.0434362826
sample130 -0.0003501995 -0.0309852690
sample131  0.0930182819  0.0155937192
sample132  0.0736222809  0.0733029694
sample133 -0.0498397984 -0.0462437485
sample134  0.1644873488  0.0720005702
sample135 -0.0752297193  0.0003817855
sample136  0.0227145791 -0.0495505967
sample137  0.0564717430 -0.0288915657
sample138  0.0255988105 -0.0610857168
sample139  0.0621217807  0.0235807781
sample140 -0.0604152526 -0.0435593127
sample141  0.0246743966  0.0532648659
sample142 -0.0409560347  0.0316279792
sample143 -0.0077355233 -0.0476896281
sample144  0.0173240823 -0.0156777944
sample145  0.0485474487  0.1202770578
sample146  0.0419645651 -0.0811281232
sample147 -0.0977308337 -0.0274839926
sample148  0.0368256170  0.0803979674
sample149 -0.0072865792 -0.1532986064
sample150  0.1020825289  0.0624774726
sample151  0.0305399055 -0.0289278296
sample152 -0.0533594801 -0.0638309159
sample153 -0.0891627695  0.1799578115
sample154 -0.0727557461 -0.0834160848
sample155 -0.0880668558 -0.0220819477
sample156 -0.0276561045 -0.0326625252
sample157 -0.1155032185  0.0183616116
sample158 -0.0281507524 -0.0104938564
sample159  0.0663235704  0.0443837278
sample160 -0.0302643898  0.0404265579
sample161  0.0114715553 -0.0591025558
sample162 -0.1337087136  0.1398135472
sample163  0.1330124471  0.1688781242
sample164 -0.0150336107  0.0028416126
sample165  0.0076520260 -0.0164128391
sample166  0.0367794365  0.0630661943
sample167  0.1111988871  0.0030057865
sample168 -0.0672981605  0.0446279284
sample169 -0.0413004970  0.0224394382
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
                      1             2
sample1   -0.0420514627  0.0867863105
sample2   -0.0820828639 -0.0410977997
sample3    0.0155899966 -0.0195182384
sample4   -0.1001337137 -0.0410786644
sample5   -0.0153466030 -0.0253259676
sample6    0.0340325631 -0.0408223196
sample7    0.0722579822  0.0002332208
sample8   -0.0457500244 -0.0370016189
sample9   -0.0086249219  0.0820184900
sample10  -0.0423598703 -0.0083923263
sample11   0.0022548715  0.0787766035
sample12   0.0322105742  0.1479824684
sample13  -0.0293889982 -0.0306748601
sample14   0.0337482558 -0.0367506862
sample15   0.0815539176  0.1275622459
sample16   0.0508451927  0.0540604654
sample17   0.0062596619  0.0041023721
sample18   0.0705639748 -0.0351047709
sample19  -0.0476841588 -0.0509598077
sample20   0.0522962956  0.0715521833
sample21  -0.0119126965 -0.0376093069
sample22   0.0724393260 -0.0095625147
sample23  -0.0992532155  0.0134288826
sample24  -0.1595118420  0.0728662095
sample25  -0.0920693305 -0.0749757239
sample26  -0.0595540144  0.0848966069
sample27   0.0826485842 -0.0086735467
sample28  -0.0384788271  0.0440966895
sample29   0.0777671688  0.1735308482
sample30   0.1229471296 -0.0819005564
sample31   0.0579846192 -0.0238644760
sample32   0.0970393109 -0.0111426329
sample33   0.1017588003 -0.0630442598
sample34   0.0637922834  0.0377941693
sample35   0.0789984495 -0.0229723251
sample36   0.1224939267 -0.1274954985
sample37   0.1798820815 -0.1673427494
sample38   0.0466304669  0.0888160921
sample39  -0.0168687656  0.0421533762
sample40   0.1756392137 -0.1526642419
sample41   0.0042370952  0.0004928789
sample42  -0.0447849667 -0.0651504994
sample43   0.0482308392 -0.0253529286
sample44  -0.1986714946 -0.0545777794
sample45  -0.0741836565  0.0054703317
sample46   0.0478772272 -0.0007072043
sample47   0.0608188568  0.0481622615
sample48  -0.1381489388  0.0578287813
sample49  -0.0530520580 -0.1405532805
sample50  -0.0173799610  0.1602389658
sample51   0.0462560884  0.0303473833
sample52   0.0280064935  0.0280388390
sample53   0.0667621274  0.0237702037
sample54   0.0121833526 -0.0521354324
sample55   0.0182395898  0.0221328426
sample56  -0.0001255627  0.0030907362
sample57   0.0316675349  0.0530190280
sample58   0.0393918140 -0.0297798754
sample59   0.1278290889 -0.0546527989
sample60   0.1486985087  0.1069156513
sample61   0.0793122438  0.0569796505
sample62   0.1172801050 -0.0149198522
sample63  -0.0028726825  0.1300519847
sample64   0.0237364641  0.1073287722
sample65  -0.0126534915  0.0589808443
sample66  -0.0468194832 -0.0771072676
sample67   0.1494264576 -0.0769860344
sample68   0.0977961231 -0.0577351089
sample69   0.0403087314  0.0156042109
sample70   0.0221531335  0.0315440948
sample71  -0.0546434535 -0.0272396433
sample72   0.1107487798 -0.0537319409
sample73   0.0906761258  0.0579966582
sample74   0.0586555371  0.0121421661
sample75   0.0390493277  0.0349282800
sample76  -0.0022960726 -0.1676558796
sample77  -0.0232096245 -0.2067302789
sample78  -0.0929754968 -0.0434939454
sample79  -0.1619496762 -0.0378114200
sample80   0.0680365541  0.1424663471
sample81  -0.0530784048 -0.0358350836
sample82   0.0266821975 -0.0577445117
sample83   0.1517235195 -0.0448554384
sample84  -0.0570967124 -0.0273813227
sample85   0.1086289677 -0.1228119363
sample86   0.0833859953 -0.0442915012
sample87   0.0022018387 -0.0943906861
sample88  -0.0078224947 -0.1140506529
sample89   0.0611057236 -0.0094585166
sample90   0.0022928097 -0.0936253993
sample91   0.0433590584  0.3205982756
sample92  -0.1815335502 -0.0334680248
sample93   0.0267630776  0.0614429017
sample94   0.0181877791  0.0605090403
sample95  -0.0720375705 -0.0013045636
sample96  -0.0559714773 -0.0118791401
sample97  -0.0217411012  0.0195414157
sample98   0.0379177408  0.0588357093
sample99  -0.0792427294 -0.0151273766
sample100  0.0222116432 -0.0023321436
sample101 -0.0387228555  0.1224226220
sample102 -0.2094614182 -0.0516442549
sample103  0.0138481228  0.0301051942
sample104 -0.0807987029 -0.0162718897
sample105 -0.0520493376 -0.1229665150
sample106 -0.0192613299 -0.0185238178
sample107  0.0319017171  0.0405123281
sample108 -0.0140690987  0.0163421385
sample109 -0.1831930096  0.0613007617
sample110 -0.0292790595 -0.0199849073
sample111 -0.1423252026  0.0327340371
sample112  0.0426332848 -0.0029083454
sample113 -0.0771904541  0.0268733676
sample114 -0.0241641419 -0.0184080412
sample115 -0.1959015580  0.0460130742
sample116 -0.1394475993 -0.0530805764
sample117 -0.1672361824 -0.1386536351
sample118 -0.0448344274 -0.0117621919
sample119 -0.0910386305  0.2217433397
sample120 -0.0331392210 -0.0057274479
sample121  0.0307574887  0.1392506541
sample122 -0.0839781187 -0.0291994386
sample123  0.0239650408 -0.0642163738
sample124 -0.0909150622  0.0130419539
sample125 -0.0065350878 -0.1092631812
sample126  0.0935311863  0.1368284024
sample127  0.0035387915  0.0292755638
sample128 -0.0660295473  0.1018566356
sample129  0.0693638595 -0.0695421767
sample130  0.0008493442 -0.0669704329
sample131  0.0431024017  0.0174064855
sample132 -0.0637040126  0.0029374755
sample133 -0.0289494783 -0.0390818826
sample134  0.0446203010  0.0456334497
sample135  0.0712336983  0.0521634928
sample136  0.0596271016  0.0197299299
sample137  0.0793151977 -0.0380628335
sample138 -0.0973548338 -0.0454218231
sample139  0.0539904700 -0.1534327381
sample140  0.0850827024  0.0955814496
sample141 -0.0192681839 -0.0554450055
sample142 -0.0672262036 -0.0461320888
sample143 -0.0303730446 -0.0519260233
sample144 -0.0089364588  0.0145814917
sample145 -0.0638769985  0.0122258441
sample146  0.0585856333  0.0063083322
sample147  0.0894133332 -0.1124615738
sample148 -0.0216366990 -0.0615967111
sample149 -0.0515420701 -0.0839903488
sample150  0.0568283331 -0.0124468937
sample151 -0.0789532403 -0.0261831158
sample152 -0.0330753454  0.1306443592
sample153 -0.1751931277  0.1497732183
sample154  0.0421424349 -0.0037010219
sample155  0.0680177498  0.0095711201
sample156  0.0388911156  0.1057562942
sample157  0.0314769420  0.0561367413
sample158  0.0329620536  0.0353947308
sample159 -0.0398416587 -0.1007373753
sample160  0.0424938722  0.0108496162
sample161 -0.0888371333 -0.0679700144
sample162 -0.0027475917  0.1237843875
sample163 -0.0126105267  0.0725434367
sample164 -0.0566779686 -0.0458324160
sample165 -0.0315336417 -0.0236362340
sample166 -0.0612058148 -0.0425233007
sample167  0.0142729876  0.0179308272
sample168 -0.0169503508 -0.0769617888
sample169  0.0675080344  0.0131505305
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
                      1             2
sample1   -0.0012329690 -1.635717e-01
sample2   -0.0724350136 -6.021270e-03
sample3   -0.0188460433 -1.080036e-01
sample4    0.0390145243  3.114030e-04
sample5    0.1774811616 -2.996385e-02
sample6   -0.0451444473 -3.455859e-02
sample7   -0.0226466190 -7.020148e-03
sample8   -0.1033680330 -9.856797e-03
sample9    0.1350011798  8.979098e-02
sample10   0.1259887188 -5.097854e-02
sample11   0.0979788428  7.086535e-02
sample12  -0.0863019145 -8.620317e-02
sample13  -0.1381401134  1.828007e-01
sample14  -0.0615073883 -2.642803e-02
sample15   0.0381598983 -3.101663e-02
sample16  -0.0048776797  1.271830e-03
sample17  -0.0788481007 -1.547554e-02
sample18  -0.0884188783 -3.795486e-02
sample19   0.0703044405 -1.084004e-01
sample20  -0.0025585434  7.975876e-02
sample21   0.0941601544 -4.126743e-02
sample22  -0.0550273361 -7.806742e-02
sample23   0.0679495263 -4.102006e-02
sample24  -0.1310962937  1.649309e-01
sample25   0.0113585249 -4.426863e-02
sample26  -0.1402945982  2.016542e-02
sample27   0.0261561232  1.588464e-03
sample28  -0.0724198765  5.850591e-02
sample29  -0.0330058489  2.060843e-03
sample30  -0.0228752525 -2.015429e-02
sample31  -0.0635068005 -6.670334e-02
sample32   0.0685099647 -4.955272e-02
sample33  -0.0777765220 -1.272078e-01
sample34   0.0157842396 -3.024314e-02
sample35  -0.0529632662  1.500972e-01
sample36   0.0070900912  2.025307e-01
sample37  -0.0442420418  1.802089e-01
sample38  -0.0781511236 -3.676419e-02
sample39   0.0120331817 -3.388842e-02
sample40  -0.0473291903  1.471562e-01
sample41   0.0228189473 -2.673553e-02
sample42  -0.0245360277 -7.960867e-02
sample43   0.1036362784 -8.229577e-02
sample44  -0.1012228912  7.049449e-02
sample45   0.0013731925 -2.450913e-02
sample46  -0.0558509945  2.947398e-03
sample47  -0.0380481132  4.554174e-02
sample48   0.0784342062  4.888979e-02
sample49  -0.0605164049 -1.162357e-02
sample50   0.0530079355 -2.737931e-02
sample51   0.1514646499  5.678344e-02
sample52   0.1860935249  1.246717e-01
sample53  -0.0064177160 -2.700994e-02
sample54   0.0697038323 -2.308389e-02
sample55   0.1633577046  1.366442e-02
sample56   0.1011485074  4.682204e-02
sample57   0.1730374211  1.609603e-01
sample58  -0.0071384715 -1.666955e-02
sample59  -0.0030461625  3.005286e-02
sample60   0.0215835278  2.665878e-01
sample61   0.1510583667  1.002385e-01
sample62  -0.0925533911 -4.845840e-02
sample63  -0.0596311869 -4.137023e-02
sample64  -0.0449225830 -2.600585e-03
sample65   0.0939383728 -4.406909e-02
sample66   0.1063400690 -5.709994e-02
sample67  -0.0201589823  2.361728e-01
sample68   0.0037203311  2.418391e-02
sample69  -0.0645161225 -1.155622e-01
sample70  -0.1013440007 -1.351789e-01
sample71  -0.0016467831 -2.976841e-02
sample72   0.0328893061 -2.835856e-02
sample73   0.0275080041 -5.148185e-02
sample74   0.1341719652 -7.895280e-02
sample75   0.0951575683 -3.943184e-02
sample76  -0.0864721919  3.034993e-02
sample77  -0.1035749561 -2.545353e-02
sample78  -0.1575644213  4.939593e-02
sample79   0.0189137124  4.874679e-02
sample80   0.1384140630  4.265745e-05
sample81  -0.0118846456 -6.357931e-02
sample82  -0.1675308144  3.533912e-02
sample83  -0.0065673375 -7.812608e-02
sample84   0.1486891596 -3.109057e-02
sample85  -0.0532724341  7.417886e-02
sample86  -0.1138477295 -1.914192e-05
sample87   0.0432864024  6.080473e-02
sample88   0.0433450384  1.402491e-01
sample89   0.0331205748 -1.395401e-02
sample90  -0.0607412828 -8.610414e-02
sample91  -0.0566272442  1.303747e-01
sample92  -0.0359582448  1.061604e-01
sample93  -0.0433646368 -4.443634e-02
sample94  -0.0477291319 -1.059574e-01
sample95  -0.0249595766 -3.980525e-02
sample96   0.0035218985 -9.293928e-02
sample97  -0.0066048819 -1.527231e-01
sample98   0.0020366818 -5.579549e-02
sample99  -0.0886616209 -3.728227e-02
sample100 -0.1091259146 -3.560420e-02
sample101 -0.0739726421 -4.317997e-02
sample102  0.0574461058 -2.783916e-02
sample103  0.0142731078  9.705567e-03
sample104  0.0710395227  4.068351e-02
sample105  0.0980831323 -3.452953e-02
sample106 -0.0254259338  3.628983e-02
sample107 -0.0160653487 -9.173395e-02
sample108 -0.0200987671 -2.379692e-02
sample109 -0.0389780662  1.692358e-02
sample110 -0.0326304855  2.988109e-02
sample111  0.0676937545 -6.038213e-02
sample112  0.0167883429  5.336937e-03
sample113  0.0969216960 -2.757605e-02
sample114 -0.0026398341 -9.209156e-02
sample115 -0.0308047346  1.603822e-02
sample116 -0.1240307144  1.273000e-01
sample117  0.0334729089  5.392710e-02
sample118 -0.1037152897  6.252431e-02
sample119 -0.1064176516  1.196203e-01
sample120 -0.0771355142 -1.004933e-01
sample121 -0.0129350790  3.181975e-02
sample122  0.0847492198 -5.568328e-02
sample123 -0.0041336756  7.693184e-03
sample124 -0.0583458079 -8.396390e-02
sample125  0.0634844571 -5.232540e-02
sample126 -0.0662580979 -1.091732e-01
sample127 -0.0865024624 -1.094176e-01
sample128 -0.0627817527 -1.470965e-02
sample129 -0.0336276419 -4.007858e-02
sample130 -0.0293517755 -8.046117e-02
sample131 -0.0469197649 -2.209745e-03
sample132 -0.0241740775 -1.248598e-01
sample133  0.0907303226  1.466700e-02
sample134 -0.0350842064  7.539662e-02
sample135  0.0001333429  9.185383e-03
sample136 -0.0335876038 -9.860273e-02
sample137 -0.0640148881 -7.554469e-02
sample138  0.0060964837 -1.742762e-02
sample139 -0.0592084430  5.614969e-02
sample140  0.0427985974 -1.099550e-02
sample141  0.0618796322 -9.301039e-02
sample142  0.0898554433  3.573416e-02
sample143  0.0817389256  8.880524e-02
sample144  0.0787754777 -3.821392e-02
sample145  0.1085821542  1.569476e-01
sample146 -0.0589557883 -4.373358e-02
sample147 -0.0495330400  7.277212e-03
sample148  0.1161592746  9.079071e-03
sample149 -0.0121579369  7.788375e-02
sample150 -0.0314512532  3.520213e-02
sample151  0.0575382155 -1.945353e-02
sample152 -0.0494542060  7.025538e-02
sample153 -0.0941332848  2.153297e-01
sample154 -0.0335931948  2.078730e-02
sample155  0.0690457670 -2.780409e-02
sample156  0.1039901632 -6.292525e-02
sample157 -0.0408645790  8.065516e-03
sample158  0.1018105325  7.816876e-03
sample159 -0.0281730575 -1.207207e-02
sample160  0.1643053001  2.978099e-03
sample161  0.0374329278  8.524610e-02
sample162 -0.0804535388  8.349753e-02
sample163 -0.0743228063 -1.406226e-02
sample164  0.1208805981 -2.139461e-02
sample165  0.1608115919  2.025191e-02
sample166 -0.0425944701 -2.660715e-02
sample167 -0.0226849480 -4.464281e-02
sample168 -0.0180735620 -7.466258e-04
sample169  0.0190779023  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 
  15.45    0.39   15.81 

STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsPCA.Rout


R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

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

Distinctive components
[[1]]
[1] 2

[[2]]
[1] 2

> 
> 
> #########################
> ## PART 3. Component Analysis
> 
> ## 3.1 Component analysis of the three methods
> discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                               scale=TRUE,weight=TRUE)
> jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                              scale=TRUE,weight=TRUE)
> o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                               scale=TRUE,weight=TRUE)
> 
> ## 3.2 Exploring scores structures
> 
> # Exploring DISCO-SCA scores structure
> discoRes@scores$common ## Common scores
                      1             2
sample1    0.0781574706 -0.0431501151
sample2   -0.1192218428  0.0294087200
sample3   -0.0531411918 -0.0746840261
sample4    0.0292975182 -0.0005961228
sample5    0.0202091721  0.0110463104
sample6    0.1226088802  0.1053466907
sample7    0.1078928114 -0.0322475084
sample8    0.1782895038  0.1449363538
sample9    0.0468698241 -0.0455173001
sample10  -0.0036030537  0.0420110448
sample11  -0.0035566327 -0.0566291362
sample12   0.1006129053  0.0641383375
sample13  -0.1174408675  0.0907487854
sample14   0.0981203089  0.0617737984
sample15   0.0085334455 -0.0087011092
sample16   0.0783148389  0.1581295777
sample17  -0.1483610017  0.0638581791
sample18  -0.0963086430  0.0556639909
sample19  -0.0217243898 -0.0720087500
sample20  -0.0635636273 -0.0779651721
sample21  -0.0201840657  0.1566390542
sample22   0.0218268870 -0.0764104132
sample23   0.0852042165 -0.0032689160
sample24  -0.1287170872  0.1924543754
sample25  -0.0430574046 -0.0456568004
sample26  -0.1453896764  0.0541512931
sample27  -0.0197488680 -0.1185656487
sample28  -0.1025336370  0.0650685908
sample29   0.0706018798 -0.0682984816
sample30  -0.1295627731 -0.0066770052
sample31   0.1147448866  0.1232686794
sample32  -0.0374311022  0.0380178205
sample33   0.0599515901  0.0136866864
sample34  -0.0984200870  0.0375321796
sample35  -0.0543098530 -0.0378105652
sample36   0.1403625033 -0.0343756799
sample37   0.0228941459 -0.0732847425
sample38  -0.0222076948 -0.0962593211
sample39  -0.0941738428  0.0215199502
sample40   0.0643800777 -0.0687871906
sample41  -0.0327637775 -0.1232188243
sample42  -0.0500431762 -0.0292474801
sample43  -0.0184498891  0.0233010757
sample44   0.1487898623  0.1171352725
sample45  -0.1050774288  0.1123200955
sample46  -0.1151195554 -0.1094028659
sample47  -0.0962593697 -0.0288462730
sample48   0.0004837547 -0.0310277780
sample49   0.1135207425  0.1213970761
sample50  -0.0123552534 -0.1740741442
sample51   0.0550529563  0.1258887497
sample52   0.0499120973  0.0728545245
sample53   0.1119773350  0.1588014778
sample54  -0.0360055794  0.0228574719
sample55   0.0210418975  0.0006732103
sample56  -0.0434169398  0.0633126010
sample57   0.0197824283  0.1150714678
sample58   0.0030439765  0.0326097742
sample59   0.0500252868  0.0129418808
sample60   0.0184278413  0.0136088062
sample61   0.0150299194  0.0635027140
sample62  -0.0304763942 -0.0201318981
sample63   0.1102252467  0.1285979137
sample64   0.1552588045  0.0971170428
sample65  -0.0058502972  0.0207116164
sample66  -0.0025605457  0.0424318387
sample67   0.1546634491 -0.0661716349
sample68   0.0536369245 -0.0923684040
sample69   0.0640330442  0.0081983534
sample70   0.0163518018 -0.0663229568
sample71  -0.0102537340 -0.1345922094
sample72  -0.0654196175 -0.0196120064
sample73  -0.1048556148  0.0220939270
sample74   0.0123799377  0.0586115386
sample75   0.0392078008 -0.0209754387
sample76   0.0648953226 -0.0524766778
sample77   0.1172921927 -0.0201189727
sample78  -0.1463068213  0.0708471283
sample79   0.0265211556 -0.1603309084
sample80   0.0279737310 -0.0214202705
sample81   0.0079211686 -0.0738451661
sample82  -0.1544236559 -0.0361468817
sample83  -0.0494211543 -0.0050048796
sample84  -0.0259038382 -0.0346550407
sample85   0.1116484017 -0.0031498899
sample86  -0.1306483100 -0.0377215725
sample87  -0.0554778309 -0.0459750448
sample88  -0.0301624192  0.0382195843
sample89  -0.1016866912  0.0694033664
sample90   0.0086819855 -0.0201321587
sample91   0.1578626036 -0.2097822463
sample92   0.0170937193 -0.1655807883
sample93  -0.0979806696 -0.0121511404
sample94   0.0131484276 -0.0114931149
sample95   0.0315682875 -0.0758859319
sample96   0.0024125817 -0.0470136015
sample97   0.0634545539  0.0270332094
sample98  -0.0359374524 -0.0135487547
sample99  -0.1009163424  0.1124779425
sample100  0.0551753101  0.0246489737
sample101 -0.0080118339 -0.1627366744
sample102 -0.0046444164  0.0095630526
sample103 -0.0472522990 -0.0940392885
sample104  0.0198159612 -0.0591092021
sample105 -0.0400237875 -0.0160914208
sample106 -0.0923808515  0.0369017280
sample107 -0.1019373889  0.0224954600
sample108 -0.0877091570 -0.0128834146
sample109  0.0864824826 -0.0900940984
sample110 -0.1223115538 -0.0096086253
sample111  0.0257355041 -0.0936168994
sample112 -0.0765286710  0.0270347466
sample113  0.0258803307  0.0377497433
sample114  0.0021139152 -0.0882015304
sample115  0.0303460604 -0.0723585257
sample116  0.0780508540 -0.0685067094
sample117  0.0536898207 -0.0911910757
sample118  0.0666651190 -0.0236231066
sample119  0.1021872385 -0.2324933155
sample120  0.0750216629  0.0243379085
sample121 -0.0756936425  0.0942952713
sample122 -0.0259628132  0.0731986425
sample123 -0.1037846315 -0.0369198336
sample124  0.0611208043  0.0421723773
sample125 -0.0738472846  0.0066948198
sample126  0.0972916493  0.0762642422
sample127  0.0824697785 -0.0096636731
sample128 -0.1249407573  0.0929313961
sample129 -0.0734067582 -0.0434364014
sample130 -0.0003501980 -0.0309853756
sample131  0.0930182770  0.0155937675
sample132  0.0736222858  0.0733029798
sample133 -0.0498397944 -0.0462438264
sample134  0.1644873324  0.0720006785
sample135 -0.0752297213  0.0003818557
sample136  0.0227145904 -0.0495505641
sample137  0.0564717386 -0.0288916124
sample138  0.0255988275 -0.0610857865
sample139  0.0621217443  0.0235805604
sample140 -0.0604152414 -0.0435591777
sample141  0.0246743880  0.0532647714
sample142 -0.0409560417  0.0316278926
sample143 -0.0077355259 -0.0476897131
sample144  0.0173240903 -0.0156777792
sample145  0.0485474256  0.1202770834
sample146  0.0419645763 -0.0811281021
sample147 -0.0977308564 -0.0274841774
sample148  0.0368255955  0.0803978665
sample149 -0.0072865643 -0.1532987330
sample150  0.1020825072  0.0624774770
sample151  0.0305399173 -0.0289278735
sample152 -0.0533594510 -0.0638307128
sample153 -0.0891627719  0.1799580495
sample154 -0.0727557388 -0.0834160976
sample155 -0.0880668575 -0.0220819553
sample156 -0.0276560847 -0.0326623790
sample157 -0.1155032177  0.0183616844
sample158 -0.0281507509 -0.0104938152
sample159  0.0663235543  0.0443835836
sample160 -0.0302644018  0.0404265542
sample161  0.0114715600 -0.0591026594
sample162 -0.1337087247  0.1398137304
sample163  0.1330124318  0.1688782661
sample164 -0.0150336102  0.0028415243
sample165  0.0076520266 -0.0164128898
sample166  0.0367794293  0.0630661358
sample167  0.1111988908  0.0030058331
sample168 -0.0672981770  0.0446277986
sample169 -0.0413005092  0.0224394525
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
                      1             2
sample1    0.0420515983  0.0867863178
sample2    0.0820828099 -0.0410979674
sample3   -0.0155900289 -0.0195180696
sample4    0.1001336363 -0.0410788454
sample5    0.0153465473 -0.0253260196
sample6   -0.0340326472 -0.0408224339
sample7   -0.0722579949  0.0002333845
sample8    0.0457499444 -0.0370019434
sample9    0.0086250566  0.0820185179
sample10   0.0423598492 -0.0083924644
sample11  -0.0022547356  0.0787766800
sample12  -0.0322103254  0.1479824284
sample13   0.0293889735 -0.0306750566
sample14  -0.0337483295 -0.0367507300
sample15  -0.0815536980  0.1275624039
sample16  -0.0508451045  0.0540602972
sample17  -0.0062596325  0.0041023117
sample18  -0.0705640211 -0.0351047093
sample19   0.0476840636 -0.0509597633
sample20  -0.0522961605  0.0715523934
sample21   0.0119126300 -0.0376095675
sample22  -0.0724393473 -0.0095622570
sample23   0.0992532240  0.0134287040
sample24   0.1595120010  0.0728656307
sample25   0.0920692014 -0.0749758018
sample26   0.0595541890  0.0848964437
sample27  -0.0826486002 -0.0086732161
sample28   0.0384789236  0.0440965305
sample29  -0.0777668729  0.1735310822
sample30  -0.1229472572 -0.0819003074
sample31  -0.0579846727 -0.0238645691
sample32  -0.0970393302 -0.0111425126
sample33  -0.1017589193 -0.0630440877
sample34  -0.0637922063  0.0377942407
sample35  -0.0789984757 -0.0229721400
sample36  -0.1224941606 -0.1274952819
sample37  -0.1798823701 -0.1673423485
sample38  -0.0466303081  0.0888163368
sample39   0.0168688496  0.0421533313
sample40  -0.1756394827 -0.1526638562
sample41  -0.0042370944  0.0004930857
sample42   0.0447848561 -0.0651505116
sample43  -0.0482308887 -0.0253528706
sample44   0.1986713899 -0.0545783368
sample45   0.0741836799  0.0054700457
sample46  -0.0478772136 -0.0007069309
sample47  -0.0608187577  0.0481624220
sample48   0.1381490379  0.0578285756
sample49   0.0530518020 -0.1405535738
sample50   0.0173802371  0.1602392094
sample51  -0.0462560443  0.0303472436
sample52  -0.0280064527  0.0280387383
sample53  -0.0667620975  0.0237700619
sample54  -0.0121834432 -0.0521354417
sample55  -0.0182395603  0.0221328596
sample56   0.0001255718  0.0030906311
sample57  -0.0316674439  0.0530188675
sample58  -0.0393918663 -0.0297798546
sample59  -0.1278291894 -0.0546526066
sample60  -0.1486983140  0.1069158462
sample61  -0.0793121475  0.0569796650
sample62  -0.1172801256 -0.0149195985
sample63   0.0028729003  0.1300517732
sample64  -0.0237362914  0.1073286431
sample65   0.0126535901  0.0589807924
sample66   0.0468193421 -0.0771074124
sample67  -0.1494266027 -0.0769857269
sample68  -0.0977962318 -0.0577348053
sample69  -0.0403087133  0.0156042807
sample70  -0.0221530820  0.0315442607
sample71   0.0546434036 -0.0272395234
sample72  -0.1107488689 -0.0537317042
sample73  -0.0906760140  0.0579968043
sample74  -0.0586555252  0.0121421808
sample75  -0.0390492774  0.0349283763
sample76   0.0022957748 -0.1676558113
sample77   0.0232092510 -0.2067302957
sample78   0.0929754486 -0.0434941967
sample79   0.1619496049 -0.0378114667
sample80  -0.0680363143  0.1424664883
sample81   0.0530783378 -0.0358350525
sample82  -0.0266822716 -0.0577443827
sample83  -0.1517235949 -0.0448551452
sample84   0.0570966591 -0.0273813684
sample85  -0.1086291915 -0.1228117656
sample86  -0.0833860526 -0.0442912713
sample87  -0.0022019978 -0.0943906143
sample88   0.0078223025 -0.1140507456
sample89  -0.0611057286 -0.0094585025
sample90  -0.0022929759 -0.0936253487
sample91  -0.0433585118  0.3205986384
sample92   0.1815334922 -0.0334681022
sample93  -0.0267629579  0.0614429909
sample94  -0.0181876768  0.0605091070
sample95   0.0720375626 -0.0013045689
sample96   0.0559714517 -0.0118791513
sample97   0.0217411227  0.0195413501
sample98  -0.0379176357  0.0588358105
sample99   0.0792427197 -0.0151276655
sample100 -0.0222116502 -0.0023321397
sample101  0.0387230700  0.1224228191
sample102  0.2094613248 -0.0516446352
sample103 -0.0138480661  0.0301053694
sample104  0.0807986693 -0.0162719522
sample105  0.0520491210 -0.1229665754
sample106  0.0192613119 -0.0185238998
sample107 -0.0319016360  0.0405123786
sample108  0.0140691378  0.0163421513
sample109  0.1831931073  0.0613005678
sample110  0.0292790419 -0.0199849289
sample111  0.1423252499  0.0327339351
sample112 -0.0426332806 -0.0029083034
sample113  0.0771904933  0.0268731688
sample114  0.0241641045 -0.0184079324
sample115  0.1959016357  0.0460128381
sample116  0.1394475058 -0.0530807384
sample117  0.1672359317 -0.1386538042
sample118  0.0448344051 -0.0117622473
sample119  0.0910390120  0.2217435146
sample120  0.0331392017 -0.0057275352
sample121 -0.0307572319  0.1392505680
sample122  0.0839780661 -0.0291996921
sample123 -0.0239651409 -0.0642162603
sample124  0.0909150777  0.0130417361
sample125  0.0065349006 -0.1092631878
sample126 -0.0935309582  0.1368284539
sample127 -0.0035387508  0.0292755946
sample128  0.0660297451  0.1018563988
sample129 -0.0693639733 -0.0695419690
sample130 -0.0008494639 -0.0669703692
sample131 -0.0431023803  0.0174065262
sample132  0.0637040063  0.0029372595
sample133  0.0289494116 -0.0390818614
sample134 -0.0446202354  0.0456333825
sample135 -0.0712335973  0.0521636266
sample136 -0.0596270730  0.0197301252
sample137 -0.0793152719 -0.0380626426
sample138  0.0973547490 -0.0454219001
sample139 -0.0539907415 -0.1534326945
sample140 -0.0850825305  0.0955816747
sample141  0.0192680783 -0.0554451148
sample142  0.0672261256 -0.0461322603
sample143  0.0303729535 -0.0519260192
sample144  0.0089364770  0.0145814995
sample145  0.0638770173  0.0122255080
sample146 -0.0585856278  0.0063085650
sample147 -0.0894135164 -0.1124613586
sample148  0.0216365831 -0.0615968872
sample149  0.0515419246 -0.0839902108
sample150 -0.0568283637 -0.0124469098
sample151  0.0789531868 -0.0261832137
sample152  0.0330755848  0.1306444001
sample153  0.1751934168  0.1497726160
sample154 -0.0421424316 -0.0037008090
sample155 -0.0680177267  0.0095712870
sample156 -0.0388909344  0.1057564212
sample157 -0.0314768270  0.0561367855
sample158 -0.0329619925  0.0353948023
sample159  0.0398414756 -0.1007375204
sample160 -0.0424938559  0.0108496229
sample161  0.0888370133 -0.0679700949
sample162  0.0027478343  0.1237841759
sample163  0.0126106432  0.0725431388
sample164  0.0566778840 -0.0458325209
sample165  0.0315335927 -0.0236362766
sample166  0.0612057377 -0.0425235050
sample167 -0.0142729704  0.0179308396
sample168  0.0169502256 -0.0769618771
sample169 -0.0675080057  0.0131506149
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
                      1             2
sample1   -0.0012329519  1.635717e-01
sample2   -0.0724350096  6.021297e-03
sample3   -0.0188460435  1.080036e-01
sample4    0.0390145266 -3.113985e-04
sample5    0.1774811647  2.996384e-02
sample6   -0.0451444366  3.455859e-02
sample7   -0.0226466240  7.020137e-03
sample8   -0.1033680164  9.856810e-03
sample9    0.1350011726 -8.979101e-02
sample10   0.1259887288  5.097853e-02
sample11   0.0979788351 -7.086537e-02
sample12  -0.0863018933  8.620315e-02
sample13  -0.1381401215 -1.828007e-01
sample14  -0.0615073826  2.642803e-02
sample15   0.0381599035  3.101660e-02
sample16  -0.0048776629 -1.271836e-03
sample17  -0.0788480942  1.547557e-02
sample18  -0.0884188752  3.795488e-02
sample19   0.0703044420  1.084004e-01
sample20  -0.0025585571 -7.975877e-02
sample21   0.0941601719  4.126744e-02
sample22  -0.0550273405  7.806741e-02
sample23   0.0679495353  4.102005e-02
sample24  -0.1310962789 -1.649309e-01
sample25   0.0113585239  4.426865e-02
sample26  -0.1402945885 -2.016540e-02
sample27   0.0261561070 -1.588474e-03
sample28  -0.0724198718 -5.850590e-02
sample29  -0.0330058494 -2.060879e-03
sample30  -0.0228752622  2.015431e-02
sample31  -0.0635067855  6.670335e-02
sample32   0.0685099676  4.955272e-02
sample33  -0.0777765170  1.272078e-01
sample34   0.0157842445  3.024314e-02
sample35  -0.0529632882 -1.500972e-01
sample36   0.0070900589 -2.025307e-01
sample37  -0.0442420816 -1.802089e-01
sample38  -0.0781511274  3.676417e-02
sample39   0.0120331891  3.388842e-02
sample40  -0.0473292255 -1.471562e-01
sample41   0.0228189369  2.673552e-02
sample42  -0.0245360256  7.960868e-02
sample43   0.1036362843  8.229577e-02
sample44  -0.1012228787 -7.049447e-02
sample45   0.0013732085  2.450915e-02
sample46  -0.0558510086 -2.947394e-03
sample47  -0.0380481209 -4.554174e-02
sample48   0.0784342081 -4.888980e-02
sample49  -0.0605163962  1.162359e-02
sample50   0.0530079299  2.737927e-02
sample51   0.1514646574 -5.678345e-02
sample52   0.1860935219 -1.246717e-01
sample53  -0.0064176987  2.700994e-02
sample54   0.0697038329  2.308390e-02
sample55   0.1633577040 -1.366444e-02
sample56   0.1011485095 -4.682204e-02
sample57   0.1730374201 -1.609603e-01
sample58  -0.0071384702  1.666955e-02
sample59  -0.0030461721 -3.005286e-02
sample60   0.0215835052 -2.665878e-01
sample61   0.1510583639 -1.002385e-01
sample62  -0.0925533951  4.845841e-02
sample63  -0.0596311625  4.137022e-02
sample64  -0.0449225673  2.600566e-03
sample65   0.0939383824  4.406908e-02
sample66   0.1063400761  5.709995e-02
sample67  -0.0201590191 -2.361728e-01
sample68   0.0037203127 -2.418392e-02
sample69  -0.0645161121  1.155622e-01
sample70  -0.1013439947  1.351789e-01
sample71  -0.0016467930  2.976841e-02
sample72   0.0328892984  2.835857e-02
sample73   0.0275080091  5.148185e-02
sample74   0.1341719761  7.895279e-02
sample75   0.0951575701  3.943182e-02
sample76  -0.0864722082 -3.034990e-02
sample77  -0.1035749651  2.545356e-02
sample78  -0.1575644179 -4.939589e-02
sample79   0.0189136976 -4.874679e-02
sample80   0.1384140657 -4.269735e-05
sample81  -0.0118846469  6.357932e-02
sample82  -0.1675308265 -3.533909e-02
sample83  -0.0065673407  7.812609e-02
sample84   0.1486891598  3.109057e-02
sample85  -0.0532724515 -7.417884e-02
sample86  -0.1138477403  1.916242e-05
sample87   0.0432863869 -6.080472e-02
sample88   0.0433450239 -1.402490e-01
sample89   0.0331205790  1.395402e-02
sample90  -0.0607412824  8.610415e-02
sample91  -0.0566272598 -1.303748e-01
sample92  -0.0359582641 -1.061604e-01
sample93  -0.0433646328  4.443634e-02
sample94  -0.0477291212  1.059574e-01
sample95  -0.0249595773  3.980525e-02
sample96   0.0035219038  9.293928e-02
sample97  -0.0066048634  1.527231e-01
sample98   0.0020366866  5.579549e-02
sample99  -0.0886616045  3.728230e-02
sample100 -0.1091259097  3.560420e-02
sample101 -0.0739726463  4.317995e-02
sample102  0.0574461156  2.783917e-02
sample103  0.0142730983 -9.705576e-03
sample104  0.0710395161 -4.068351e-02
sample105  0.0980831290  3.452955e-02
sample106 -0.0254259342 -3.628982e-02
sample107 -0.0160653384  9.173395e-02
sample108 -0.0200987654  2.379693e-02
sample109 -0.0389780645 -1.692359e-02
sample110 -0.0326304898 -2.988108e-02
sample111  0.0676937588  6.038212e-02
sample112  0.0167883425 -5.336933e-03
sample113  0.0969217072  2.757604e-02
sample114 -0.0026398347  9.209156e-02
sample115 -0.0308047318 -1.603822e-02
sample116 -0.1240307284 -1.273000e-01
sample117  0.0334728954 -5.392708e-02
sample118 -0.1037152957 -6.252430e-02
sample119 -0.1064176683 -1.196203e-01
sample120 -0.0771355013  1.004933e-01
sample121 -0.0129350669 -3.181976e-02
sample122  0.0847492339  5.568329e-02
sample123 -0.0041336852 -7.693170e-03
sample124 -0.0583457912  8.396391e-02
sample125  0.0634844562  5.232542e-02
sample126 -0.0662580768  1.091732e-01
sample127 -0.0865024518  1.094176e-01
sample128 -0.0627817347  1.470966e-02
sample129 -0.0336276501  4.007859e-02
sample130 -0.0293517752  8.046118e-02
sample131 -0.0469197635  2.209739e-03
sample132 -0.0241740558  1.248598e-01
sample133  0.0907303155 -1.466700e-02
sample134 -0.0350842041 -7.539663e-02
sample135  0.0001333412 -9.185389e-03
sample136 -0.0335876015  9.860272e-02
sample137 -0.0640148895  7.554469e-02
sample138  0.0060964812  1.742763e-02
sample139 -0.0592084558 -5.614967e-02
sample140  0.0427985950  1.099548e-02
sample141  0.0618796437  9.301039e-02
sample142  0.0898554435 -3.573416e-02
sample143  0.0817389116 -8.880524e-02
sample144  0.0787754808  3.821390e-02
sample145  0.1085821564 -1.569476e-01
sample146 -0.0589557945  4.373358e-02
sample147 -0.0495330541 -7.277188e-03
sample148  0.1161592797 -9.079068e-03
sample149 -0.0121579612 -7.788374e-02
sample150 -0.0314512525 -3.520213e-02
sample151  0.0575382167  1.945353e-02
sample152 -0.0494542104 -7.025539e-02
sample153 -0.0941332705 -2.153297e-01
sample154 -0.0335932075 -2.078730e-02
sample155  0.0690457641  2.780409e-02
sample156  0.1039901692  6.292522e-02
sample157 -0.0408645771 -8.065512e-03
sample158  0.1018105310 -7.816890e-03
sample159 -0.0281730552  1.207208e-02
sample160  0.1643053024 -2.978112e-03
sample161  0.0374329148 -8.524610e-02
sample162 -0.0804535264 -8.349753e-02
sample163 -0.0743227828  1.406226e-02
sample164  0.1208806002  2.139461e-02
sample165  0.1608115887 -2.025192e-02
sample166 -0.0425944609  2.660717e-02
sample167 -0.0226849427  4.464280e-02
sample168 -0.0180735614  7.466475e-04
sample169  0.0190778996 -2.645403e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
                   [,1]          [,2]
sample1   -0.0572060227 -1.729087e-02
sample2    0.0875245208  1.112588e-02
sample3    0.0403482602 -3.168994e-02
sample4   -0.0218345996  4.052760e-06
sample5   -0.0150905011  4.795041e-03
sample6   -0.0924362933  4.511003e-02
sample7   -0.0793066751 -1.243823e-02
sample8   -0.1342997187  6.215220e-02
sample9   -0.0338886944 -1.854401e-02
sample10   0.0020547173  1.749421e-02
sample11   0.0037275602 -2.364116e-02
sample12  -0.0753094533  2.772698e-02
sample13   0.0856160091  3.679963e-02
sample14  -0.0737457307  2.668452e-02
sample15  -0.0062111746 -3.554864e-03
sample16  -0.0602355268  6.675115e-02
sample17   0.1086768843  2.524534e-02
sample18   0.0702999472  2.231671e-02
sample19   0.0173785882 -3.024846e-02
sample20   0.0484173812 -3.310904e-02
sample21   0.0124657042  6.517144e-02
sample22  -0.0140989936 -3.159137e-02
sample23  -0.0627028403 -5.393710e-04
sample24   0.0919972100  7.909297e-02
sample25   0.0326998483 -1.945206e-02
sample26   0.1064741246  2.120849e-02
sample27   0.0166058995 -4.964993e-02
sample28   0.0743504770  2.614211e-02
sample29  -0.0511008491 -2.782647e-02
sample30   0.0962250842 -3.974893e-03
sample31  -0.0869563008  5.250819e-02
sample32   0.0271858919  1.552005e-02
sample33  -0.0448364581  6.243160e-03
sample34   0.0718415218  1.469396e-02
sample35   0.0403086451 -1.632629e-02
sample36  -0.1036402827 -1.304320e-02
sample37  -0.0159385744 -3.036525e-02
sample38   0.0182198369 -4.034805e-02
sample39   0.0690363619  8.058350e-03
sample40  -0.0467312750 -2.810325e-02
sample41   0.0263674438 -5.171216e-02
sample42   0.0374578960 -1.268634e-02
sample43   0.0132336869  9.536642e-03
sample44  -0.1119154428  5.028683e-02
sample45   0.0759639367  4.587903e-02
sample46   0.0871885519 -4.670385e-02
sample47   0.0721490571 -1.288540e-02
sample48   0.0005086144 -1.290565e-02
sample49  -0.0858177028  5.173760e-02
sample50   0.0118992665 -7.276215e-02
sample51  -0.0426446855  5.306205e-02
sample52  -0.0381605826  3.086785e-02
sample53  -0.0855757630  6.730043e-02
sample54   0.0261723092  9.184260e-03
sample55  -0.0156418304  4.682404e-04
sample56   0.0307831193  2.597550e-02
sample57  -0.0157242103  4.829381e-02
sample58  -0.0031174404  1.359898e-02
sample59  -0.0373001859  5.868397e-03
sample60  -0.0142609099  5.831654e-03
sample61  -0.0122255144  2.663579e-02
sample62   0.0228002942 -8.692265e-03
sample63  -0.0833127581  5.473229e-02
sample64  -0.1166548159  4.196500e-02
sample65   0.0038808902  8.568590e-03
sample66   0.0011561811  1.766612e-02
sample67  -0.1129311062 -2.608702e-02
sample68  -0.0382526429 -3.804045e-02
sample69  -0.0476502440  4.003241e-03
sample70  -0.0110329882 -2.752719e-02
sample71   0.0096850282 -5.627056e-02
sample72   0.0487124704 -8.800131e-03
sample73   0.0773058132  8.239864e-03
sample74  -0.0102488176  2.454957e-02
sample75  -0.0286613976 -8.387293e-03
sample76  -0.0472655595 -2.129315e-02
sample77  -0.0865043074 -7.296820e-03
sample78   0.1070293698  2.818346e-02
sample79  -0.0165060681 -6.659721e-02
sample80  -0.0206765949 -8.712112e-03
sample81  -0.0050943615 -3.079175e-02
sample82   0.1153622361 -1.647054e-02
sample83   0.0367979217 -2.538114e-03
sample84   0.0199463070 -1.468961e-02
sample85  -0.0827122185 -2.709824e-04
sample86   0.0969487314 -1.699897e-02
sample87   0.0421957457 -1.965953e-02
sample88   0.0215934743  1.566050e-02
sample89   0.0751559502  2.811652e-02
sample90  -0.0057328000 -8.283795e-03
sample91  -0.1134005268 -8.603522e-02
sample92  -0.0101689918 -6.894992e-02
sample93   0.0725967502 -6.003176e-03
sample94  -0.0096878852 -4.693081e-03
sample95  -0.0223502239 -3.139636e-02
sample96  -0.0013232863 -1.963604e-02
sample97  -0.0476541710  1.183660e-02
sample98   0.0269546160 -5.978398e-03
sample99   0.0728179461  4.597884e-02
sample100 -0.0413398038  1.079347e-02
sample101  0.0087536994 -6.796076e-02
sample102  0.0032509529  3.932612e-03
sample103  0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105  0.0294940465 -7.140722e-03
sample106  0.0686472054  1.462895e-02
sample107  0.0748635927  8.401339e-03
sample108  0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110  0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112  0.0570870472  1.066018e-02
sample113 -0.0200110554  1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586  1.089843e-02
sample121  0.0542443861  3.861344e-02
sample122  0.0178575357  3.027138e-02
sample123  0.0775020581 -1.636852e-02
sample124 -0.0460701050  1.814758e-02
sample125  0.0543846585  2.075898e-03
sample126 -0.0729417144  3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128  0.0908136899  3.758801e-02
sample129  0.0552445878 -1.879062e-02
sample130  0.0007128089 -1.294308e-02
sample131 -0.0693311345  7.357082e-03
sample132 -0.0556565156  3.126995e-02
sample133  0.0375870104 -1.977240e-02
sample134 -0.1229130924  3.159495e-02
sample135  0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201  1.040683e-02
sample140  0.0452288969 -1.876279e-02
sample141 -0.0189142561  2.247042e-02
sample142  0.0297545566  1.280524e-02
sample143  0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491  5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147  0.0726083535 -1.239968e-02
sample148 -0.0284795794  3.389732e-02
sample149  0.0082261455 -6.399305e-02
sample150 -0.0765013197  2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152  0.0403422737 -2.714879e-02
sample153  0.0629117719  7.425085e-02
sample154  0.0551622927 -3.548984e-02
sample155  0.0654439133 -1.005306e-02
sample156  0.0209310714 -1.390213e-02
sample157  0.0851522597  6.577150e-03
sample158  0.0208354599 -4.663078e-03
sample159 -0.0498794349  1.913257e-02
sample160  0.0216074437  1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162  0.0963663017  5.705881e-02
sample163 -0.1009542191  7.174224e-02
sample164  0.0109881996  1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357  2.673084e-02
sample167 -0.0825048036  2.278863e-03
sample168  0.0486147429  1.793843e-02
sample169  0.0302506727  8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
                   [,1]          [,2]
sample1   -0.0621842115 -1.364509e-02
sample2    0.0944623785  9.720892e-03
sample3    0.0406196267 -2.236338e-02
sample4   -0.0229316496 -3.932487e-04
sample5   -0.0157330047  3.231033e-03
sample6   -0.0945794025  3.120720e-02
sample7   -0.0854427118 -1.052880e-02
sample8   -0.1376625920  4.286608e-02
sample9   -0.0377115311 -1.415134e-02
sample10   0.0035244506  1.280825e-02
sample11   0.0016639987 -1.717895e-02
sample12  -0.0781403168  1.884368e-02
sample13   0.0938400516  2.838858e-02
sample14  -0.0759839772  1.810989e-02
sample15  -0.0068340837 -2.705361e-03
sample16  -0.0590150849  4.757848e-02
sample17   0.1178805097  2.040526e-02
sample18   0.0767858320  1.756604e-02
sample19   0.0157112113 -2.172867e-02
sample20   0.0485318300 -2.327033e-02
sample21   0.0185928176  4.777095e-02
sample22  -0.0191358702 -2.329775e-02
sample23  -0.0672994194 -1.535656e-03
sample24   0.1047476642  5.935707e-02
sample25   0.0329844953 -1.358036e-02
sample26   0.1154952052  1.741529e-02
sample27   0.0133849853 -3.590922e-02
sample28   0.0821554039  2.042376e-02
sample29  -0.0567643690 -2.123848e-02
sample30   0.1016073931 -1.134728e-03
sample31  -0.0880396372  3.670548e-02
sample32   0.0300363338  1.182406e-02
sample33  -0.0467252272  3.739254e-03
sample34   0.0783666394  1.203777e-02
sample35   0.0424227097 -1.118559e-02
sample36  -0.1107646166 -1.143464e-02
sample37  -0.0191667664 -2.246060e-02
sample38   0.0155968095 -2.909621e-02
sample39   0.0746847148  7.148218e-03
sample40  -0.0517028178 -2.137267e-02
sample41   0.0234979494 -3.723018e-02
sample42   0.0388797356 -8.557228e-03
sample43   0.0149555568  7.210002e-03
sample44  -0.1150305613  3.461805e-02
sample45   0.0846146236  3.486020e-02
sample46   0.0884426404 -3.246853e-02
sample47   0.0748644971 -8.083045e-03
sample48  -0.0012033198 -9.403647e-03
sample49  -0.0872662737  3.616245e-02
sample50   0.0066941314 -5.284863e-02
sample51  -0.0411777630  3.791830e-02
sample52  -0.0379355780  2.180834e-02
sample53  -0.0851639886  4.751761e-02
sample54   0.0288006248  7.184424e-03
sample55  -0.0164920835  5.919925e-05
sample56   0.0355115616  1.951043e-02
sample57  -0.0141146068  3.492409e-02
sample58  -0.0015636132  9.862883e-03
sample59  -0.0390656483  3.590929e-03
sample60  -0.0139454780  3.963030e-03
sample61  -0.0106410274  1.919705e-02
sample62   0.0236748439 -5.922677e-03
sample63  -0.0846790877  3.839102e-02
sample64  -0.1202581015  2.846469e-02
sample65   0.0050548584  6.328644e-03
sample66   0.0028013072  1.291807e-02
sample67  -0.1231623009 -2.112565e-02
sample68  -0.0437782161 -2.845072e-02
sample69  -0.0501199692  2.053469e-03
sample70  -0.0140278645 -2.027157e-02
sample71   0.0057489505 -4.085977e-02
sample72   0.0511212704 -5.522408e-03
sample73   0.0828141409  7.431582e-03
sample74  -0.0085959456  1.772951e-02
sample75  -0.0312180394 -6.636869e-03
sample76  -0.0519051781 -1.640191e-02
sample77  -0.0925924762 -6.907800e-03
sample78   0.1163971046  2.251122e-02
sample79  -0.0240906926 -4.887766e-02
sample80  -0.0221327065 -6.730703e-03
sample81  -0.0072114968 -2.254399e-02
sample82   0.1204416674 -9.907422e-03
sample83   0.0386739485 -1.171663e-03
sample84   0.0195988488 -1.033806e-02
sample85  -0.0877680171 -1.725057e-03
sample86   0.1023541048 -1.062501e-02
sample87   0.0425213089 -1.356865e-02
sample88   0.0244788514  1.180820e-02
sample89   0.0804276691  2.188588e-02
sample90  -0.0074639871 -6.140721e-03
sample91  -0.1278832404 -6.485140e-02
sample92  -0.0162199697 -5.048358e-02
sample93   0.0769344893 -3.045135e-03
sample94  -0.0104345587 -3.593172e-03
sample95  -0.0260058453 -2.330475e-02
sample96  -0.0025018700 -1.433516e-02
sample97  -0.0492358305  7.774183e-03
sample98   0.0279220220 -3.862141e-03
sample99   0.0813921923  3.487339e-02
sample100 -0.0428797405  7.112807e-03
sample101  0.0032855240 -4.940743e-02
sample102  0.0038439317  2.938008e-03
sample103  0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105  0.0314853405 -4.656633e-03
sample106  0.0726456731  1.192390e-02
sample107  0.0807342975  7.508627e-03
sample108  0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110  0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112  0.0599954082  8.820317e-03
sample113 -0.0195006577  1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131  6.937326e-03
sample121  0.0613899126  2.915307e-02
sample122  0.0218424338  2.241775e-02
sample123  0.0809008460 -1.051759e-02
sample124 -0.0472109313  1.239887e-02
sample125  0.0583180947  2.521167e-03
sample126 -0.0753941872  2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128  0.1000212216  2.908091e-02
sample129  0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877  4.091672e-03
sample132 -0.0566219024  2.179861e-02
sample133  0.0384172955 -1.372840e-02
sample134 -0.1280862736  2.077912e-02
sample135  0.0592633273  6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861  6.729304e-03
sample140  0.0468926306 -1.285498e-02
sample141 -0.0186248693  1.605439e-02
sample142  0.0328031246  9.887746e-03
sample143  0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310  3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147  0.0765025670 -7.714769e-03
sample148 -0.0276016641  2.420589e-02
sample149  0.0027545308 -4.653007e-02
sample150 -0.0792296010  1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152  0.0409796547 -1.907063e-02
sample153  0.0734301757  5.528780e-02
sample154  0.0557740684 -2.487723e-02
sample155  0.0689436560 -6.127635e-03
sample156  0.0212272938 -9.747423e-03
sample157  0.0911931194  6.355708e-03
sample158  0.0220840645 -3.016357e-03
sample159 -0.0513244242  1.304175e-02
sample160  0.0246213576  1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162  0.1078802043  4.337260e-02
sample163 -0.1017965082  5.047171e-02
sample164  0.0119430799  9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180  1.899222e-02
sample167 -0.0872832229  1.516582e-04
sample168  0.0540714512  1.397701e-02
sample169  0.0328432652  7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
                   [,1]          [,2]
sample1    0.0133684846  2.195848e-02
sample2    0.0254157197 -1.058416e-02
sample3   -0.0049551479 -4.840017e-03
sample4    0.0310390570 -1.063929e-02
sample5    0.0046941318 -6.488426e-03
sample6   -0.0107406753 -1.026702e-02
sample7   -0.0225157631  2.624712e-04
sample8    0.0141320952 -9.505821e-03
sample9    0.0029681280  2.078210e-02
sample10   0.0131729174 -2.275042e-03
sample11  -0.0004164298  1.994019e-02
sample12  -0.0095211620  3.759883e-02
sample13   0.0091018604 -7.953956e-03
sample14  -0.0106557524 -9.181659e-03
sample15  -0.0249924121  3.262724e-02
sample16  -0.0156216400  1.375700e-02
sample17  -0.0019382446  1.073994e-03
sample18  -0.0221072481 -8.703592e-03
sample19   0.0146917619 -1.311712e-02
sample20  -0.0160353760  1.826290e-02
sample21   0.0035947899 -9.616341e-03
sample22  -0.0225060762 -2.532589e-03
sample23   0.0310000683  3.033060e-03
sample24   0.0499544372  1.809450e-02
sample25   0.0284442301 -1.932558e-02
sample26   0.0188220043  2.146985e-02
sample27  -0.0257763219 -1.999228e-03
sample28   0.0120888648  1.125834e-02
sample29  -0.0236482520  4.426726e-02
sample30  -0.0385486305 -2.055935e-02
sample31  -0.0181539336 -5.877838e-03
sample32  -0.0302630460 -2.607192e-03
sample33  -0.0319565715 -1.562628e-02
sample34  -0.0197970124  9.906813e-03
sample35  -0.0247412713 -5.434440e-03
sample36  -0.0386259060 -3.190394e-02
sample37  -0.0566199273 -4.192574e-02
sample38  -0.0142060273  2.259644e-02
sample39   0.0053589035  1.076485e-02
sample40  -0.0552546493 -3.819896e-02
sample41  -0.0013089975  9.278818e-05
sample42   0.0137252142 -1.664652e-02
sample43  -0.0151259626 -6.290953e-03
sample44   0.0617391754 -1.442883e-02
sample45   0.0231410886  1.163143e-03
sample46  -0.0148898209 -1.384176e-04
sample47  -0.0187252536  1.221690e-02
sample48   0.0432839432  1.416671e-02
sample49   0.0160818605 -3.588745e-02
sample50   0.0059333545  4.067003e-02
sample51  -0.0142914866  7.776270e-03
sample52  -0.0086339952  7.208917e-03
sample53  -0.0207386980  6.272432e-03
sample54  -0.0039856719 -1.316934e-02
sample55  -0.0056217017  5.692315e-03
sample56   0.0000123292  8.978290e-04
sample57  -0.0095805555  1.324253e-02
sample58  -0.0124160295 -7.326376e-03
sample59  -0.0400195442 -1.349736e-02
sample60  -0.0460063358  2.770091e-02
sample61  -0.0245266456  1.470710e-02
sample62  -0.0366022783 -3.437352e-03
sample63   0.0013742171  3.288796e-02
sample64  -0.0070599859  2.739588e-02
sample65   0.0041201911  1.498268e-02
sample66   0.0143173351 -1.968812e-02
sample67  -0.0467477531 -1.929938e-02
sample68  -0.0306751978 -1.436184e-02
sample69  -0.0125317217  4.130407e-03
sample70  -0.0068071487  8.080857e-03
sample71   0.0169170264 -7.027348e-03
sample72  -0.0346909749 -1.333770e-02
sample73  -0.0280506153  1.493843e-02
sample74  -0.0182611498  3.294697e-03
sample75  -0.0120563964  8.974612e-03
sample76   0.0001437236 -4.253184e-02
sample77   0.0065330299 -5.252886e-02
sample78   0.0288278141 -1.127782e-02
sample79   0.0503961481 -1.023318e-02
sample80  -0.0207693429  3.648391e-02
sample81   0.0163562768 -9.074596e-03
sample82  -0.0084317129 -1.478976e-02
sample83  -0.0474097918 -1.103126e-02
sample84   0.0177181395 -7.191197e-03
sample85  -0.0342718548 -3.082360e-02
sample86  -0.0261671791 -1.089491e-02
sample87  -0.0009486358 -2.411514e-02
sample88   0.0020528931 -2.894615e-02
sample89  -0.0189361111 -2.638639e-03
sample90  -0.0009863658 -2.390075e-02
sample91  -0.0124352695  8.153234e-02
sample92   0.0564264106 -8.909537e-03
sample93  -0.0081461774  1.570851e-02
sample94  -0.0054896581  1.547251e-02
sample95   0.0224073150 -4.374348e-04
sample96   0.0173528924 -3.050441e-03
sample97   0.0067948115  5.008237e-03
sample98  -0.0116030825  1.498764e-02
sample99   0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101  0.0124923691  3.091503e-02
sample102  0.0650835386 -1.367400e-02
sample103 -0.0042741828  7.855985e-03
sample104  0.0250591040 -4.171938e-03
sample105  0.0157516368 -3.121990e-02
sample106  0.0060593853 -5.101693e-03
sample107 -0.0098329626  1.044506e-02
sample108  0.0044269853  4.142036e-03
sample109  0.0572473486  1.517542e-02
sample110  0.0090474827 -5.119868e-03
sample111  0.0444263015  7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113  0.0241047399  6.706740e-03
sample114  0.0074558775 -4.728652e-03
sample115  0.0611851433  1.117210e-02
sample116  0.0432646951 -1.380556e-02
sample117  0.0516750066 -3.575617e-02
sample118  0.0139942100 -3.279138e-03
sample119  0.0291722987  5.587946e-02
sample120  0.0103515853 -1.690016e-03
sample121 -0.0091396331  3.552116e-02
sample122  0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124  0.0283466326  3.127845e-03
sample125  0.0016472378 -2.770692e-02
sample126 -0.0286529417  3.489336e-02
sample127 -0.0010224500  7.483214e-03
sample128  0.0209049296  2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968  4.637390e-03
sample132  0.0198526786  5.723983e-04
sample133  0.0088812957 -9.988115e-03
sample134 -0.0137484514  1.172591e-02
sample135 -0.0220314568  1.347465e-02
sample136 -0.0185173353  5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138  0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762  2.456863e-02
sample141  0.0058369763 -1.420854e-02
sample142  0.0207886071 -1.188764e-02
sample143  0.0092832598 -1.324238e-02
sample144  0.0028442140  3.627979e-03
sample145  0.0199749569  2.862202e-03
sample146 -0.0182236697  1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148  0.0065435868 -1.572917e-02
sample149  0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151  0.0245166984 -6.888241e-03
sample152  0.0107259913  3.314630e-02
sample153  0.0550963965  3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574  2.642246e-03
sample156 -0.0117803505  2.698265e-02
sample157 -0.0096167165  1.433840e-02
sample158 -0.0101754772  9.137620e-03
sample159  0.0120662931 -2.565236e-02
sample160 -0.0132238202  2.916023e-03
sample161  0.0274491966 -1.748284e-02
sample162  0.0012482909  3.152261e-02
sample163  0.0042031315  1.830701e-02
sample164  0.0174896157 -1.175915e-02
sample165  0.0097517662 -6.119019e-03
sample166  0.0190134679 -1.121582e-02
sample167 -0.0044140836  4.665585e-03
sample168  0.0049689168 -1.941822e-02
sample169 -0.0209802098  3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
                   [,1]          [,2]
sample1   -0.0515543627 -0.0305856787
sample2   -0.0144993256  0.0236342950
sample3   -0.0371833108 -0.0140263348
sample4    0.0068945388 -0.0132539692
sample5    0.0215035333 -0.0663338101
sample6   -0.0187055152  0.0088773016
sample7   -0.0061521552  0.0064029054
sample8   -0.0210874459  0.0334652901
sample9    0.0516865043 -0.0291142799
sample10   0.0059440366 -0.0527217447
sample11   0.0393010793 -0.0200624712
sample12  -0.0420837100  0.0131331362
sample13   0.0333252565  0.0818552509
sample14  -0.0190062644  0.0160202175
sample15  -0.0030968049 -0.0189230681
sample16  -0.0004452158  0.0018880102
sample17  -0.0185848615  0.0240170131
sample18  -0.0273093598  0.0230213640
sample19  -0.0217761111 -0.0445894441
sample20   0.0245820821  0.0159812738
sample21   0.0034527644 -0.0400016054
sample22  -0.0340789054  0.0039289109
sample23  -0.0010344929 -0.0310161212
sample24   0.0289468503  0.0760962436
sample25  -0.0119098496 -0.0122798760
sample26  -0.0181001057  0.0517892852
sample27   0.0050465417 -0.0086515844
sample28   0.0057491502  0.0358830107
sample29  -0.0051104246  0.0116605117
sample30  -0.0103085904  0.0039678538
sample31  -0.0319929858  0.0090606113
sample32  -0.0036232521 -0.0328202010
sample33  -0.0534742153  0.0024751837
sample34  -0.0067495749 -0.0111000311
sample35   0.0378745721  0.0465929296
sample36   0.0647886800  0.0359987924
sample37   0.0488441236  0.0492906912
sample38  -0.0251514062  0.0197110110
sample39  -0.0085428066 -0.0105117852
sample40   0.0379324087  0.0440810741
sample41  -0.0044199152 -0.0128820644
sample42  -0.0292553573 -0.0067045265
sample43  -0.0077829155 -0.0510178219
sample44   0.0045122248  0.0479660309
sample45  -0.0074444298 -0.0051116726
sample46  -0.0088025512  0.0196186661
sample47   0.0076696301  0.0215947965
sample48   0.0290108585 -0.0175568376
sample49  -0.0141754858  0.0184717099
sample50   0.0006282201 -0.0233054373
sample51   0.0441995177 -0.0410022921
sample52   0.0715329391 -0.0399499475
sample53  -0.0095954087 -0.0029140909
sample54   0.0048933768 -0.0281884386
sample55   0.0327325487 -0.0532290012
sample56   0.0323068984 -0.0256595538
sample57   0.0806603122 -0.0286748097
sample58  -0.0064792049 -0.0006945349
sample59   0.0088958941  0.0067389649
sample60   0.0874124612  0.0431964341
sample61   0.0577604571 -0.0326112099
sample62  -0.0313318464  0.0224391756
sample63  -0.0233625220  0.0125110562
sample64  -0.0086426068  0.0148770341
sample65   0.0025256193 -0.0404466327
sample66   0.0006014071 -0.0471576264
sample67   0.0706087042  0.0516228406
sample68   0.0082301011  0.0033109509
sample69  -0.0475076743  0.0001452708
sample70  -0.0600773716  0.0089986962
sample71  -0.0096321627 -0.0050761187
sample72  -0.0031773546 -0.0166221542
sample73  -0.0113700517 -0.0191726684
sample74  -0.0014179662 -0.0608101325
sample75   0.0041911740 -0.0399981269
sample76  -0.0055326449  0.0353114263
sample77  -0.0260214459  0.0305731380
sample78  -0.0119267436  0.0632236007
sample79   0.0186017239  0.0027402910
sample80   0.0241047889 -0.0472697181
sample81  -0.0220288317 -0.0079577210
sample82  -0.0180751258  0.0639051029
sample83  -0.0256671713 -0.0125898269
sample84   0.0161392598 -0.0567222449
sample85   0.0139988188  0.0322763454
sample86  -0.0198382995  0.0389225776
sample87   0.0266270281 -0.0032979996
sample88   0.0515677078  0.0117902495
sample89   0.0014022125 -0.0140510488
sample90  -0.0375949749  0.0044004551
sample91   0.0310397965  0.0440610926
sample92   0.0270570567  0.0324380452
sample93  -0.0215009202  0.0063993941
sample94  -0.0415702912 -0.0037692077
sample95  -0.0168416047  0.0010019120
sample96  -0.0285582661 -0.0187991000
sample97  -0.0490843868 -0.0266760748
sample98  -0.0171579033 -0.0112897471
sample99  -0.0271316525  0.0232395583
sample100 -0.0301789816  0.0305498693
sample101 -0.0264371151  0.0170723968
sample102  0.0012767734 -0.0248949597
sample103  0.0055214687 -0.0030040587
sample104  0.0251346074 -0.0165212671
sample105  0.0062424215 -0.0400309901
sample106  0.0069768684  0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679  0.0023637162
sample109 -0.0014762845  0.0165583675
sample110  0.0036971063  0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112  0.0046098120 -0.0048009350
sample113  0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453  0.0135805368
sample116  0.0183575759  0.0665377581
sample117  0.0227640036 -0.0012287760
sample118  0.0015695248  0.0472617382
sample119  0.0190084932  0.0590034062
sample120 -0.0449645755  0.0072755697
sample121  0.0077307184  0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123  0.0016959300  0.0028593594
sample124 -0.0365091615  0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408  0.0019165544
sample127 -0.0494064872  0.0088209044
sample128 -0.0155454766  0.0186819802
sample129 -0.0184340400  0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422  0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133  0.0204029276 -0.0282209049
sample134  0.0175513332  0.0262883962
sample135  0.0029009925  0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323  0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139  0.0073103935  0.0308956174
sample140  0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142  0.0268670676 -0.0239229634
sample143  0.0421049126 -0.0110888235
sample144  0.0017253664 -0.0341766012
sample145  0.0681741320 -0.0073526377
sample146 -0.0239965222  0.0118396767
sample147 -0.0063453522  0.0183130585
sample148  0.0230825251 -0.0379753037
sample149  0.0223298673  0.0188909118
sample150  0.0055709108  0.0174179009
sample151  0.0039177786 -0.0233533275
sample152  0.0134325667  0.0302344591
sample153  0.0511990309  0.0730230140
sample154  0.0006698324  0.0154177486
sample155  0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599  0.0154934573
sample158  0.0201775524 -0.0332982124
sample159 -0.0086909001  0.0073496711
sample160  0.0295437331 -0.0555734536
sample161  0.0332754288  0.0033779619
sample162  0.0121954537  0.0433540412
sample163 -0.0173490933  0.0227219128
sample164  0.0143374783 -0.0453542590
sample165  0.0343612593 -0.0511194536
sample166 -0.0157536004  0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919  0.0060747155
sample169  0.0116231468 -0.0015112800
> 
> ## 3.3 Plotting VAF
> 
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
> 
> # JIVE plotVAF
> plotVAF(jiveRes)
> 
> 
> #########################
> ## PART 4. Plot Results
> 
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+              combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+              background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+              axisSize=NULL,titleSize=NULL)
> 
> # Scores for common part. JIVE
> plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common",
+              combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+              background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+              axisSize=NULL,titleSize=NULL)
> 
> # Scores for common part. O2PLS.
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for common part. O2PLS.
> plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common",
+              combined=TRUE,block=NULL,color="classname",shape=NULL,
+              labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+              labelSize=NULL,axisSize=NULL,titleSize=NULL)
> 
> 
> # Scores for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for distinctive part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual",
+              combined=TRUE,block=NULL,color="classname",shape=NULL,
+              labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+              labelSize=NULL,axisSize=NULL,titleSize=NULL)
> 
> # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block)
> p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for common and distinctive part. DISCO  (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Loadings for common part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> # Loadings for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> # Combined plot for loadings from common and distinctive part  (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> 
> ## Plot scores and loadings togheter: Common components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+         combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+         background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+         axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> ## Plot scores and loadings togheter:  Common components O2PLS
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> ## Plot scores and loadings togheter: Distintive components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
  15.95    0.45   16.39 

Example timings

STATegRa.Rcheck/examples_i386/STATegRa-Ex.timings

nameusersystemelapsed
STATegRaUsersGuide000
STATegRa_data0.350.040.41
STATegRa_data_TCGA_BRCA0.020.000.01
bioDist0.610.050.66
bioDistFeature0.480.020.50
bioDistFeaturePlot0.410.010.42
bioDistW0.480.000.49
bioDistWPlot0.380.030.40
bioMap000
combiningMappings0.030.000.03
createOmicsExpressionSet0.240.020.25
getInitialData0.710.250.97
getLoadings0.630.160.78
getMethodInfo0.760.251.02
getPreprocessing1.270.341.61
getScores0.80.21.0
getVAF0.670.130.80
holistOmics0.000.010.01
modelSelection2.560.584.02
omicsCompAnalysis6.270.246.50
omicsNPC0.010.000.01
plotRes7.110.147.25
plotVAF5.740.005.74

STATegRa.Rcheck/examples_x64/STATegRa-Ex.timings

nameusersystemelapsed
STATegRaUsersGuide000
STATegRa_data0.300.010.31
STATegRa_data_TCGA_BRCA000
bioDist0.710.080.82
bioDistFeature0.470.020.49
bioDistFeaturePlot0.460.040.51
bioDistW0.540.020.55
bioDistWPlot0.500.030.53
bioMap0.010.000.02
combiningMappings0.020.000.01
createOmicsExpressionSet0.180.020.21
getInitialData0.720.120.84
getLoadings0.890.131.02
getMethodInfo0.460.150.61
getPreprocessing0.860.281.14
getScores0.670.100.76
getVAF0.530.080.61
holistOmics000
modelSelection1.720.402.13
omicsCompAnalysis3.450.113.56
omicsNPC000
plotRes5.390.055.53
plotVAF4.670.114.78