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This page was generated on 2021-10-20 12:05:28 -0400 (Wed, 20 Oct 2021).

CHECK results for STATegRa on riesling1

To the developers/maintainers of the STATegRa package:
- Please allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/STATegRa.git to
reflect on this report. See How and When does the builder pull? When will my changes propagate? here for more information.
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raw results

Package 1878/2072HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
STATegRa 1.29.0  (landing page)
David Gomez-Cabrero , NĂºria Planell
Snapshot Date: 2021-10-19 14:50:04 -0400 (Tue, 19 Oct 2021)
git_url: https://git.bioconductor.org/packages/STATegRa
git_branch: master
git_last_commit: 2771b94
git_last_commit_date: 2021-05-19 12:08:55 -0400 (Wed, 19 May 2021)
nebbiolo2Linux (Ubuntu 20.04.2 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
riesling1Windows Server 2019 Standard / x64  OK    OK    OK    OK  UNNEEDED, same version is already published
merida1macOS 10.14.6 Mojave / x86_64  OK    OK    OK    NA  

Summary

Package: STATegRa
Version: 1.29.0
Command: D:\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=D:\biocbuild\bbs-3.14-bioc\R\library --no-vignettes --timings STATegRa_1.29.0.tar.gz
StartedAt: 2021-10-20 04:01:25 -0400 (Wed, 20 Oct 2021)
EndedAt: 2021-10-20 04:07:14 -0400 (Wed, 20 Oct 2021)
EllapsedTime: 349.0 seconds
RetCode: 0
Status:   OK  
CheckDir: STATegRa.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   D:\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=D:\biocbuild\bbs-3.14-bioc\R\library --no-vignettes --timings STATegRa_1.29.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory 'D:/biocbuild/bbs-3.14-bioc/meat/STATegRa.Rcheck'
* using R version 4.1.1 (2021-08-10)
* 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 '1.29.0'
* 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
** running examples for arch 'x64' ... OK
* 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
  'D:/biocbuild/bbs-3.14-bioc/meat/STATegRa.Rcheck/00check.log'
for details.



Installation output

STATegRa.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\cygwin\bin\curl.exe -O http://155.52.207.166/BBS/3.14/bioc/src/contrib/STATegRa_1.29.0.tar.gz && rm -rf STATegRa.buildbin-libdir && mkdir STATegRa.buildbin-libdir && D:\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=STATegRa.buildbin-libdir STATegRa_1.29.0.tar.gz && D:\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD INSTALL STATegRa_1.29.0.zip && rm STATegRa_1.29.0.tar.gz STATegRa_1.29.0.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
 67 3177k   67 2135k    0     0  2696k      0  0:00:01 --:--:--  0:00:01 2696k
 94 3177k   94 3005k    0     0  1677k      0  0:00:01  0:00:01 --:--:-- 1677k
100 3177k  100 3177k    0     0  1702k      0  0:00:01  0:00:01 --:--:-- 1702k

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_1.29.0.zip
* DONE (STATegRa)
* installing to library 'D:/biocbuild/bbs-3.14-bioc/R/library'
package 'STATegRa' successfully unpacked and MD5 sums checked

Tests output

STATegRa.Rcheck/tests_i386/runTests.Rout


R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

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

Distinctive components
[[1]]
[1] 0

[[2]]
[1] 0

Common components
[1] 2

Distinctive components
[[1]]
[1] 1

[[2]]
[1] 1

Common components
[1] 2

Distinctive components
[[1]]
[1] 2

[[2]]
[1] 2



RUNIT TEST PROTOCOL -- Wed Oct 20 04:04:09 2021 
*********************************************** 
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: In rownames(pData) == colnames(exprs) :
  longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
> 
> proc.time()
   user  system elapsed 
   2.14    0.15    2.28 

STATegRa.Rcheck/tests_x64/runTests.Rout


R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

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

Distinctive components
[[1]]
[1] 0

[[2]]
[1] 0

Common components
[1] 2

Distinctive components
[[1]]
[1] 1

[[2]]
[1] 1

Common components
[1] 2

Distinctive components
[[1]]
[1] 2

[[2]]
[1] 2



RUNIT TEST PROTOCOL -- Wed Oct 20 04:05:26 2021 
*********************************************** 
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: In rownames(pData) == colnames(exprs) :
  longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
> 
> proc.time()
   user  system elapsed 
   2.18    0.17    2.37 

STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsCLUST.Rout


R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

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

Attaching package: 'BiocGenerics'

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

    IQR, mad, sd, var, xtabs

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

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

Welcome to Bioconductor

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

> 
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+                  distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+                  map.name = "id",
+                  map.metadata = list(),
+                  params = list())
> 
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
> 
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
> 
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+                        bioDistList = bioDistList,
+                        weights=weights)
> length(bioDistWList)
[1] 4
> 
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
> 
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+                listDistW = bioDistWList,
+                method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
4: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
5: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
6: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
7: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
> 
> #############################################
> ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
> #############################################
> 
> ## IDH1
> 
> IDH1.F<-bioDistFeature(Feature = "IDH1" ,
+                        listDistW = bioDistWList,
+                        threshold.cor=0.7)
> bioDistFeaturePlot(data=IDH1.F)
> 
> ## PDGFRA
> 
> #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
> #                       listDistW = bioDistWList,
> #                       threshold.cor=0.7)
> #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
> 
> ## EGFR
> #EGFR.F<-bioDistFeature(Feature = "EGFR" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.7)
> #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
> 
> ## MGMT
> #MGMT.F<-bioDistFeature(Feature = "MGMT" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.5)
> #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
  22.62    0.53   23.12 

STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsCLUST.Rout


R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

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

Attaching package: 'BiocGenerics'

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

    IQR, mad, sd, var, xtabs

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

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

Welcome to Bioconductor

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

> 
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+                  distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+                  map.name = "id",
+                  map.metadata = list(),
+                  params = list())
> 
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
> 
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
> 
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+                        bioDistList = bioDistList,
+                        weights=weights)
> length(bioDistWList)
[1] 4
> 
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
> 
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+                listDistW = bioDistWList,
+                method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
4: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
5: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
6: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
7: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
> 
> #############################################
> ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
> #############################################
> 
> ## IDH1
> 
> IDH1.F<-bioDistFeature(Feature = "IDH1" ,
+                        listDistW = bioDistWList,
+                        threshold.cor=0.7)
> bioDistFeaturePlot(data=IDH1.F)
> 
> ## PDGFRA
> 
> #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
> #                       listDistW = bioDistWList,
> #                       threshold.cor=0.7)
> #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
> 
> ## EGFR
> #EGFR.F<-bioDistFeature(Feature = "EGFR" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.7)
> #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
> 
> ## MGMT
> #MGMT.F<-bioDistFeature(Feature = "MGMT" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.5)
> #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
  17.64    0.43   18.06 

STATegRa.Rcheck/tests_i386/STATegRa_Example.omicsNPC.Rout


R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

> rm(list = ls())
> require("STATegRa")
Loading required package: STATegRa
> # Load the data
> data("TCGA_BRCA_Batch_93")
> # Setting dataTypes
> dataTypes <- c("count", "count", "continuous")
> # Setting methods to combine pvalues
> combMethods = c("Fisher", "Liptak", "Tippett")
> # Setting number of permutations
> numPerms = 1000
> # Setting number of cores
> numCores = 1
> # Setting holistOmics to print out the steps that it performs.
> verbose = TRUE
> # Run holistOmics analysis.
> output <- omicsNPC(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes, combMethods = combMethods, numPerms = numPerms, numCores = numCores, verbose = verbose)
Compute initial statistics on data
Building NULL distributions by permuting data
Compute pseudo p-values based on NULL distributions...
NPC p-values calculation...
> 
> proc.time()
   user  system elapsed 
  43.92    0.14   44.04 

STATegRa.Rcheck/tests_x64/STATegRa_Example.omicsNPC.Rout


R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

> rm(list = ls())
> require("STATegRa")
Loading required package: STATegRa
> # Load the data
> data("TCGA_BRCA_Batch_93")
> # Setting dataTypes
> dataTypes <- c("count", "count", "continuous")
> # Setting methods to combine pvalues
> combMethods = c("Fisher", "Liptak", "Tippett")
> # Setting number of permutations
> numPerms = 1000
> # Setting number of cores
> numCores = 1
> # Setting holistOmics to print out the steps that it performs.
> verbose = TRUE
> # Run holistOmics analysis.
> output <- omicsNPC(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes, combMethods = combMethods, numPerms = numPerms, numCores = numCores, verbose = verbose)
Compute initial statistics on data
Building NULL distributions by permuting data
Compute pseudo p-values based on NULL distributions...
NPC p-values calculation...
> 
> proc.time()
   user  system elapsed 
  47.87    0.20   48.06 

STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsPCA.Rout


R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

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

Distinctive components
[[1]]
[1] 2

[[2]]
[1] 2

> 
> 
> #########################
> ## PART 3. Component Analysis
> 
> ## 3.1 Component analysis of the three methods
> discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                               scale=TRUE,weight=TRUE)
> jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                              scale=TRUE,weight=TRUE)
> o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                               scale=TRUE,weight=TRUE)
> 
> ## 3.2 Exploring scores structures
> 
> # Exploring DISCO-SCA scores structure
> discoRes@scores$common ## Common scores
                      1             2
sample1    0.0781575658 -0.0431549111
sample2   -0.1192221395  0.0294020868
sample3   -0.0531408651 -0.0746837609
sample4    0.0292971766 -0.0006035019
sample5    0.0202090741  0.0110455109
sample6    0.1226088427  0.1053493508
sample7    0.1078931359 -0.0322418304
sample8    0.1782891099  0.1449329951
sample9    0.0468697291 -0.0455171586
sample10  -0.0036032701  0.0420077302
sample11  -0.0035566355 -0.0566284929
sample12   0.1006129694  0.0641394400
sample13  -0.1174412857  0.0907475200
sample14   0.0981203548  0.0617763907
sample15   0.0085337287 -0.0086955181
sample16   0.0783146789  0.1581334147
sample17  -0.1483610676  0.0638580097
sample18  -0.0963084394  0.0556688600
sample19  -0.0217243054 -0.0720129304
sample20  -0.0635633909 -0.0779609507
sample21  -0.0201844036  0.1566381806
sample22   0.0218273884 -0.0764055085
sample23   0.0852039037 -0.0032766143
sample24  -0.1287181587  0.1924424533
sample25  -0.0430575644 -0.0456639829
sample26  -0.1453899797  0.0541458104
sample27  -0.0197483573 -0.1185591878
sample28  -0.1025339447  0.0650654933
sample29   0.0706022503 -0.0682931865
sample30  -0.1295622965 -0.0066676324
sample31   0.1147449296  0.1232727888
sample32  -0.0374308205  0.0380250683
sample33   0.0599520793  0.0136937156
sample34  -0.0984199299  0.0375365333
sample35  -0.0543096417 -0.0378034079
sample36   0.1403628038 -0.0343637210
sample37   0.0228947689 -0.0732687284
sample38  -0.0222072979 -0.0962566686
sample39  -0.0941739220  0.0215180277
sample40   0.0643807093 -0.0687717600
sample41  -0.0327634941 -0.1232187197
sample42  -0.0500431627 -0.0292514508
sample43  -0.0184497175  0.0233044885
sample44   0.1487889276  0.1171207815
sample45  -0.1050778831  0.1123139387
sample46  -0.1151191560 -0.1093995244
sample47  -0.0962591510 -0.0288417244
sample48   0.0004832597 -0.0310380984
sample49   0.1135203825  0.1213935875
sample50  -0.0123549864 -0.1740763092
sample51   0.0550527405  0.1258931536
sample52   0.0499118454  0.0728581578
sample53   0.1119772642  0.1588065159
sample54  -0.0360055724  0.0228585730
sample55   0.0210418828  0.0006751051
sample56  -0.0434171521  0.0633131346
sample57   0.0197820645  0.1150754677
sample58   0.0030440702  0.0326128015
sample59   0.0500256752  0.0129523276
sample60   0.0184280083  0.0136220419
sample61   0.0150298906  0.0635098174
sample62  -0.0304758717 -0.0201234758
sample63   0.1102250104  0.1285968393
sample64   0.1552586818  0.0971185664
sample65  -0.0058503818  0.0207102581
sample66  -0.0025607493  0.0424283891
sample67   0.1546638679 -0.0661575968
sample68   0.0536374289 -0.0923602830
sample69   0.0640333022  0.0082004234
sample70   0.0163521783 -0.0663227115
sample71  -0.0102536088 -0.1345965689
sample72  -0.0654191730 -0.0196034897
sample73  -0.1048553222  0.0221001123
sample74   0.0123800506  0.0586157578
sample75   0.0392079756 -0.0209725665
sample76   0.0648954574 -0.0524759563
sample77   0.1172922640 -0.0201200782
sample78  -0.1463072664  0.0708398298
sample79   0.0265208841 -0.1603427425
sample80   0.0279739244 -0.0214152342
sample81   0.0079212167 -0.0738496287
sample82  -0.1544234588 -0.0361450359
sample83  -0.0494205377 -0.0049937129
sample84  -0.0259039734 -0.0346592322
sample85   0.1116487448 -0.0031402728
sample86  -0.1306479027 -0.0377155040
sample87  -0.0554777867 -0.0459739590
sample88  -0.0301626570  0.0382206746
sample89  -0.1016866196  0.0694078953
sample90   0.0086821654 -0.0201323973
sample91   0.1578629854 -0.2097791215
sample92   0.0170933504 -0.1655938566
sample93  -0.0979805078 -0.0121500213
sample94   0.0131486247 -0.0114929351
sample95   0.0315682475 -0.0758918086
sample96   0.0024125854 -0.0470186014
sample97   0.0634545818  0.0270303261
sample98  -0.0359372531 -0.0135466082
sample99  -0.1009167655  0.1124711542
sample100  0.0551754101  0.0246502339
sample101 -0.0080115936 -0.1627407725
sample102 -0.0046451229  0.0095470363
sample103 -0.0472520838 -0.0940383342
sample104  0.0198157449 -0.0591148095
sample105 -0.0400239004 -0.0160949841
sample106 -0.0923810159  0.0369003618
sample107 -0.1019372352  0.0224967538
sample108 -0.0877091507 -0.0128849960
sample109  0.0864820272 -0.0901083059
sample110 -0.1223116486 -0.0096108893
sample111  0.0257352443 -0.0936282854
sample112 -0.0765285922  0.0270379830
sample113  0.0258799826  0.0377437525
sample114  0.0021141125 -0.0882040559
sample115  0.0303455278 -0.0723737445
sample116  0.0780504437 -0.0685163821
sample117  0.0536894042 -0.0912027298
sample118  0.0666649885 -0.0236261521
sample119  0.1021872627 -0.2325004516
sample120  0.0750216334  0.0243345150
sample121 -0.0756937897  0.0942970880
sample122 -0.0259632097  0.0731920163
sample123 -0.1037844677 -0.0369178544
sample124  0.0611205156  0.0421645947
sample125 -0.0738472627  0.0066944046
sample126  0.0972919157  0.0762699896
sample127  0.0824699463 -0.0096644742
sample128 -0.1249411552  0.0929253041
sample129 -0.0734063668 -0.0434312951
sample130 -0.0003500237 -0.0309857349
sample131  0.0930184055  0.0155970619
sample132  0.0736220591  0.0732971300
sample133 -0.0498398359 -0.0462456271
sample134  0.1644872610  0.0720047632
sample135 -0.0752295027  0.0003870417
sample136  0.0227150014 -0.0495468863
sample137  0.0564721741 -0.0288859827
sample138  0.0255986483 -0.0610931689
sample139  0.0621218763  0.0235858366
sample140 -0.0604148907 -0.0435531366
sample141  0.0246743031  0.0532629993
sample142 -0.0409563901  0.0316233540
sample143 -0.0077356402 -0.0476909086
sample144  0.0173241012 -0.0156785974
sample145  0.0485467638  0.1202737464
sample146  0.0419650025 -0.0811240316
sample147 -0.0977304634 -0.0274770414
sample148  0.0368253247  0.0803969289
sample149 -0.0072864858 -0.1533017544
sample150  0.1020825501  0.0624823251
sample151  0.0305397154 -0.0289337917
sample152 -0.0533595195 -0.0638335266
sample153 -0.0891639536  0.1799451551
sample154 -0.0727554368 -0.0834128971
sample155 -0.0880665799 -0.0220769665
sample156 -0.0276558816 -0.0326601378
sample157 -0.1155031557  0.0183635845
sample158 -0.0281506692 -0.0104911536
sample159  0.0663233750  0.0443809230
sample160 -0.0302644016  0.0404301844
sample161  0.0114712937 -0.0591084028
sample162 -0.1337091042  0.1398131492
sample163  0.1330120709  0.1688769250
sample164 -0.0150338162  0.0028374740
sample165  0.0076518826 -0.0164146121
sample166  0.0367791467  0.0630613497
sample167  0.1111989829  0.0030066599
sample168 -0.0672983000  0.0446266456
sample169 -0.0413003633  0.0224447840
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
                      1             2
sample1    0.0420463004  0.0867866111
sample2    0.0820849524 -0.0410968958
sample3   -0.0155965777 -0.0195186223
sample4    0.1001342680 -0.0410776522
sample5    0.0153479575 -0.0253257772
sample6   -0.0340239751 -0.0408223449
sample7   -0.0722602254  0.0002324054
sample8    0.0457618619 -0.0370007179
sample9    0.0086216999  0.0820184511
sample10   0.0423630953 -0.0083917753
sample11  -0.0022593062  0.0787764170
sample12  -0.0322076195  0.1479823370
sample13   0.0293969672 -0.0306743043
sample14  -0.0337431298 -0.0367508386
sample15  -0.0815559917  0.1275613975
sample16  -0.0508332767  0.0540604229
sample17  -0.0062555490  0.0041024825
sample18  -0.0705601163 -0.0351053299
sample19   0.0476784024 -0.0509595413
sample20  -0.0523026616  0.0715514229
sample21   0.0119250119 -0.0376087284
sample22  -0.0724457574 -0.0095634710
sample23   0.0992529560  0.0134298820
sample24   0.1595264647  0.0728683763
sample25   0.0920661212 -0.0749749330
sample26   0.0595566956  0.0848973537
sample27  -0.0826576436 -0.0086747305
sample28   0.0384833390  0.0440972608
sample29  -0.0777740228  0.1735298683
sample30  -0.1229474404 -0.0819018289
sample31  -0.0579751230 -0.0238646921
sample32  -0.0970365956 -0.0111435045
sample33  -0.1017580255 -0.0630452458
sample34  -0.0637902753  0.0377936266
sample35  -0.0790003260 -0.0229732353
sample36  -0.1224933259 -0.1274968193
sample37  -0.1798847455 -0.1673447749
sample38  -0.0466393152  0.0888153351
sample39   0.0168694718  0.0421536011
sample40  -0.1756417943 -0.1526662086
sample41  -0.0042468654  0.0004924712
sample42   0.0447825606 -0.0651501395
sample43  -0.0482292170 -0.0253533494
sample44   0.1986818642 -0.0545754167
sample45   0.0741917524  0.0054714009
sample46  -0.0478861458 -0.0007080192
sample47  -0.0608216337  0.0481615553
sample48   0.1381464985  0.0578300870
sample49   0.0530629964 -0.1405523799
sample50   0.0173647817  0.1602386291
sample51  -0.0462457350  0.0303472936
sample52  -0.0279996491  0.0280387794
sample53  -0.0667499561  0.0237700046
sample54  -0.0121811915 -0.0521354911
sample55  -0.0182392226  0.0221326645
sample56   0.0001308519  0.0030909220
sample57  -0.0316575434  0.0530190535
sample58  -0.0393891248 -0.0297801776
sample59  -0.1278271598 -0.0546540465
sample60  -0.1486964619  0.1069141982
sample61  -0.0793067865  0.0569790416
sample62  -0.1172821742 -0.0149211002
sample63   0.0028812169  0.1300523989
sample64  -0.0237296647  0.1073288302
sample65   0.0126543632  0.0589810324
sample66   0.0468233483 -0.0771066707
sample67  -0.1494285882 -0.0769877188
sample68  -0.0978023095 -0.0577363622
sample69  -0.0403090454  0.0156038299
sample70  -0.0221597451  0.0315436735
sample71   0.0546330807 -0.0272394883
sample72  -0.1107500991 -0.0537331224
sample73  -0.0906756645  0.0579957979
sample74  -0.0586513732  0.0121417468
sample75  -0.0390512476  0.0349278277
sample76   0.0022939537 -0.1676560052
sample77   0.0232101187 -0.2067300955
sample78   0.0929809367 -0.0434928119
sample79   0.1619381681 -0.0378102521
sample80  -0.0680392416  0.1424656014
sample81   0.0530725954 -0.0358347655
sample82  -0.0266850263 -0.0577449013
sample83  -0.1517242047 -0.0448569910
sample84   0.0570943355 -0.0273808492
sample85  -0.1086271801 -0.1228130317
sample86  -0.0833891433 -0.0442924662
sample87  -0.0022040696 -0.0943908460
sample88   0.0078276309 -0.1140504605
sample89  -0.0611006292 -0.0094589384
sample90  -0.0022941628 -0.0936254836
sample91  -0.0433771430  0.3205972378
sample92   0.1815218616 -0.0334666746
sample93  -0.0267653810  0.0614425860
sample94  -0.0181901239  0.0605088210
sample95   0.0720314776 -0.0013040576
sample96   0.0559672978 -0.0118787149
sample97   0.0217420563  0.0195417162
sample98  -0.0379199047  0.0588352824
sample99   0.0792507613 -0.0151262568
sample100 -0.0222100462 -0.0023322929
sample101  0.0387085438  0.1224225326
sample102  0.2094625867 -0.0516421125
sample103 -0.0138557370  0.0301047747
sample104  0.0807948288 -0.0162712440
sample105  0.0520491733 -0.1229660398
sample106  0.0192642749 -0.0185235222
sample107 -0.0319014344  0.0405120611
sample108  0.0140674322  0.0163422345
sample109  0.1831857531  0.0613023539
sample110  0.0292782674 -0.0199846500
sample111  0.1423174357  0.0327352007
sample112 -0.0426313402 -0.0029087026
sample113  0.0771931970  0.0268742604
sample114  0.0241568400 -0.0184080582
sample115  0.1958956541  0.0460148421
sample116  0.1394437587 -0.0530793616
sample117  0.1672311898 -0.1386522082
sample118  0.0448331661 -0.0117618027
sample119  0.0910194169  0.2217435865
sample120  0.0331404865 -0.0057270388
sample121 -0.0307516558  0.1392506145
sample122  0.0839837607 -0.0291983784
sample123 -0.0239675336 -0.0642167328
sample124  0.0909176341  0.0130429981
sample125  0.0065362247 -0.1092631023
sample126 -0.0935273175  0.1368276869
sample127 -0.0035405670  0.0292755031
sample128  0.0660350359  0.1018575633
sample129 -0.0693671215 -0.0695430121
sample130 -0.0008517212 -0.0669705347
sample131 -0.0431011926  0.0174061034
sample132  0.0637089063  0.0029383368
sample133  0.0289464535 -0.0390817289
sample134 -0.0446141901  0.0456332259
sample135 -0.0712343724  0.0521627686
sample136 -0.0596318553  0.0197291817
sample137 -0.0793175616 -0.0380637168
sample138  0.0973505447 -0.0454210177
sample139 -0.0539866176 -0.1534332080
sample140 -0.0850872112  0.0955804579
sample141  0.0192723901 -0.0554446540
sample142  0.0672294441 -0.0461313189
sample143  0.0303707015 -0.0519258543
sample144  0.0089350538  0.0145815373
sample145  0.0638877219  0.0122268512
sample146 -0.0585922792  0.0063075044
sample147 -0.0894146961 -0.1124625658
sample148  0.0216439774 -0.0615962520
sample149  0.0515316060 -0.0839902774
sample150 -0.0568228627 -0.0124472749
sample151  0.0789513571 -0.0261824008
sample152  0.0330693309  0.1306445022
sample153  0.1752065933  0.1497755117
sample154 -0.0421489970 -0.0037016983
sample155 -0.0680198870  0.0095703613
sample156 -0.0388950240  0.1057558030
sample157 -0.0314765003  0.0561364674
sample158 -0.0329630249  0.0353943662
sample159  0.0398461656 -0.1007368378
sample160 -0.0424905658  0.0108493053
sample161  0.0888340071 -0.0679692876
sample162  0.0027571344  0.1237848153
sample163  0.0126229939  0.0725440699
sample164  0.0566787170 -0.0458318358
sample165  0.0315331510 -0.0236359608
sample166  0.0612109401 -0.0425224956
sample167 -0.0142729547  0.0179307001
sample168  0.0169543075 -0.0769614925
sample169 -0.0675063309  0.0131499122
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
                      1             2
sample1   -0.0012331672  1.635716e-01
sample2   -0.0724353243  6.022139e-03
sample3   -0.0188459937  1.080029e-01
sample4    0.0390143106 -3.106457e-04
sample5    0.1774810621  2.996429e-02
sample6   -0.0451446471  3.455899e-02
sample7   -0.0226463458  7.019201e-03
sample8   -0.1033684588  9.857960e-03
sample9    0.1350014237 -8.979114e-02
sample10   0.1259884397  5.097938e-02
sample11   0.0979790943 -7.086567e-02
sample12  -0.0863020987  8.620322e-02
sample13  -0.1381401846 -1.827998e-01
sample14  -0.0615074741  2.642808e-02
sample15   0.0381600609  3.101601e-02
sample16  -0.0048779406 -1.271016e-03
sample17  -0.0788483244  1.547607e-02
sample18  -0.0884189500  3.795477e-02
sample19   0.0703043510  1.084003e-01
sample20  -0.0025581320 -7.975971e-02
sample21   0.0941596599  4.126895e-02
sample22  -0.0550270837  7.806616e-02
sample23   0.0679492798  4.102078e-02
sample24  -0.1310969504 -1.649282e-01
sample25   0.0113583545  4.426900e-02
sample26  -0.1402948935 -2.016458e-02
sample27   0.0261566087 -1.589974e-03
sample28  -0.0724200804 -5.850509e-02
sample29  -0.0330054728 -2.062092e-03
sample30  -0.0228750313  2.015344e-02
sample31  -0.0635070421  6.670369e-02
sample32   0.0685100012  4.955246e-02
sample33  -0.0777764917  1.272070e-01
sample34   0.0157842087  3.024311e-02
sample35  -0.0529627897 -1.500981e-01
sample36   0.0070908033 -2.025321e-01
sample37  -0.0442411738 -1.802109e-01
sample38  -0.0781508348  3.676297e-02
sample39   0.0120330055  3.388884e-02
sample40  -0.0473283775 -1.471582e-01
sample41   0.0228192213  2.673457e-02
sample42  -0.0245361876  7.960878e-02
sample43   0.1036362011  8.229577e-02
sample44  -0.1012234838 -7.049239e-02
sample45   0.0013726649  2.451070e-02
sample46  -0.0558506424 -2.948592e-03
sample47  -0.0380478694 -4.554237e-02
sample48   0.0784340444 -4.888891e-02
sample49  -0.0605168109  1.162472e-02
sample50   0.0530082950  2.737812e-02
sample51   0.1514645346 -5.678260e-02
sample52   0.1860936017 -1.246711e-01
sample53  -0.0064179719  2.701060e-02
sample54   0.0697037556  2.308413e-02
sample55   0.1633577712 -1.366433e-02
sample56   0.1011483987 -4.682133e-02
sample57   0.1730374406 -1.609594e-01
sample58  -0.0071384889  1.666951e-02
sample59  -0.0030458445 -3.005376e-02
sample60   0.0215842250 -2.665887e-01
sample61   0.1510585351 -1.002384e-01
sample62  -0.0925531560  4.845727e-02
sample63  -0.0596315459  4.137109e-02
sample64  -0.0449227286  2.600961e-03
sample65   0.0939382240  4.406950e-02
sample66   0.1063397710  5.710079e-02
sample67  -0.0201580744 -2.361746e-01
sample68   0.0037208432 -2.418544e-02
sample69  -0.0645161995  1.155618e-01
sample70  -0.1013439738  1.351780e-01
sample71  -0.0016466094  2.976773e-02
sample72   0.0328895429  2.835771e-02
sample73   0.0275080399  5.148152e-02
sample74   0.1341718364  7.895303e-02
sample75   0.0951576657  3.943147e-02
sample76  -0.0864719938 -3.035054e-02
sample77  -0.1035749511  2.545325e-02
sample78  -0.1575647908 -4.939474e-02
sample79   0.0189138375 -4.874690e-02
sample80   0.1384142797 -4.315768e-05
sample81  -0.0118846668  6.357908e-02
sample82  -0.1675306631 -3.533969e-02
sample83  -0.0065671129  7.812497e-02
sample84   0.1486890649  3.109096e-02
sample85  -0.0532720314 -7.417989e-02
sample86  -0.1138474897  1.819808e-05
sample87   0.0432865958 -6.080499e-02
sample88   0.0433451193 -1.402486e-01
sample89   0.0331204797  1.395428e-02
sample90  -0.0607413482  8.610386e-02
sample91  -0.0566263673 -1.303770e-01
sample92  -0.0359580698 -1.061605e-01
sample93  -0.0433646453  4.443610e-02
sample94  -0.0477292128  1.059571e-01
sample95  -0.0249595927  3.980509e-02
sample96   0.0035217570  9.293931e-02
sample97  -0.0066052020  1.527234e-01
sample98   0.0020367072  5.579515e-02
sample99  -0.0886621773  3.728375e-02
sample100 -0.1091259603  3.560401e-02
sample101 -0.0739723811  4.317884e-02
sample102  0.0574455661  2.784087e-02
sample103  0.0142733770 -9.706358e-03
sample104  0.0710395579 -4.068331e-02
sample105  0.0980829926  3.452997e-02
sample106 -0.0254260506 -3.628933e-02
sample107 -0.0160655046  9.173398e-02
sample108 -0.0200988316  2.379699e-02
sample109 -0.0389781956 -1.692311e-02
sample110 -0.0326305258 -2.988087e-02
sample111  0.0676935923  6.038249e-02
sample112  0.0167883513 -5.336923e-03
sample113  0.0969213933  2.757704e-02
sample114 -0.0026397972  9.209102e-02
sample115 -0.0308049588 -1.603744e-02
sample116 -0.1240306400 -1.272998e-01
sample117  0.0334728634 -5.392662e-02
sample118 -0.1037152156 -6.252440e-02
sample119 -0.1064170545 -1.196217e-01
sample120 -0.0771357723  1.004935e-01
sample121 -0.0129352320 -3.181914e-02
sample122  0.0847487525  5.568464e-02
sample123 -0.0041335512 -7.693553e-03
sample124 -0.0583462231  8.396476e-02
sample125  0.0634843240  5.232568e-02
sample126 -0.0662582091  1.091730e-01
sample127 -0.0865025616  1.094172e-01
sample128 -0.0627822060  1.471094e-02
sample129 -0.0336274576  4.007774e-02
sample130 -0.0293518116  8.046086e-02
sample131 -0.0469196783  2.209382e-03
sample132 -0.0241745663  1.248608e-01
sample133  0.0907303799 -1.466698e-02
sample134 -0.0350841223 -7.539660e-02
sample135  0.0001334895 -9.185821e-03
sample136 -0.0335874791  9.860181e-02
sample137 -0.0640147255  7.554371e-02
sample138  0.0060964038  1.742783e-02
sample139 -0.0592082755 -5.615006e-02
sample140  0.0427988628  1.099465e-02
sample141  0.0618793254  9.301102e-02
sample142  0.0898552491 -3.573323e-02
sample143  0.0817391069 -8.880528e-02
sample144  0.0787754475  3.821395e-02
sample145  0.1085819491 -1.569461e-01
sample146 -0.0589554980  4.373236e-02
sample147 -0.0495327898 -7.278061e-03
sample148  0.1161590470 -9.078132e-03
sample149 -0.0121575489 -7.788462e-02
sample150 -0.0314511972 -3.520220e-02
sample151  0.0575380935  1.945393e-02
sample152 -0.0494540348 -7.025566e-02
sample153 -0.0941338582 -2.153270e-01
sample154 -0.0335928811 -2.078825e-02
sample155  0.0690459056  2.780361e-02
sample156  0.1039902315  6.292488e-02
sample157 -0.0408645840 -8.065529e-03
sample158  0.1018106351 -7.817023e-03
sample159 -0.0281732544  1.207260e-02
sample160  0.1643052865 -2.977810e-03
sample161  0.0374330078 -8.524589e-02
sample162 -0.0804538283 -8.349635e-02
sample163 -0.0743232416  1.406347e-02
sample164  0.1208804267  2.139524e-02
sample165  0.1608115952 -2.025159e-02
sample166 -0.0425947954  2.660801e-02
sample167 -0.0226849503  4.464258e-02
sample168 -0.0180737382  7.471588e-04
sample169  0.0190780217 -2.645427e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
                   [,1]          [,2]
sample1   -0.0572060227 -1.729087e-02
sample2    0.0875245208  1.112588e-02
sample3    0.0403482602 -3.168994e-02
sample4   -0.0218345996  4.052760e-06
sample5   -0.0150905011  4.795041e-03
sample6   -0.0924362933  4.511003e-02
sample7   -0.0793066751 -1.243823e-02
sample8   -0.1342997187  6.215220e-02
sample9   -0.0338886944 -1.854401e-02
sample10   0.0020547173  1.749421e-02
sample11   0.0037275602 -2.364116e-02
sample12  -0.0753094533  2.772698e-02
sample13   0.0856160091  3.679963e-02
sample14  -0.0737457307  2.668452e-02
sample15  -0.0062111746 -3.554864e-03
sample16  -0.0602355268  6.675115e-02
sample17   0.1086768843  2.524534e-02
sample18   0.0702999472  2.231671e-02
sample19   0.0173785882 -3.024846e-02
sample20   0.0484173812 -3.310904e-02
sample21   0.0124657042  6.517144e-02
sample22  -0.0140989936 -3.159137e-02
sample23  -0.0627028403 -5.393710e-04
sample24   0.0919972100  7.909297e-02
sample25   0.0326998483 -1.945206e-02
sample26   0.1064741246  2.120849e-02
sample27   0.0166058995 -4.964993e-02
sample28   0.0743504770  2.614211e-02
sample29  -0.0511008491 -2.782647e-02
sample30   0.0962250842 -3.974893e-03
sample31  -0.0869563008  5.250819e-02
sample32   0.0271858919  1.552005e-02
sample33  -0.0448364581  6.243160e-03
sample34   0.0718415218  1.469396e-02
sample35   0.0403086451 -1.632629e-02
sample36  -0.1036402827 -1.304320e-02
sample37  -0.0159385744 -3.036525e-02
sample38   0.0182198369 -4.034805e-02
sample39   0.0690363619  8.058350e-03
sample40  -0.0467312750 -2.810325e-02
sample41   0.0263674438 -5.171216e-02
sample42   0.0374578960 -1.268634e-02
sample43   0.0132336869  9.536642e-03
sample44  -0.1119154428  5.028683e-02
sample45   0.0759639367  4.587903e-02
sample46   0.0871885519 -4.670385e-02
sample47   0.0721490571 -1.288540e-02
sample48   0.0005086144 -1.290565e-02
sample49  -0.0858177028  5.173760e-02
sample50   0.0118992665 -7.276215e-02
sample51  -0.0426446855  5.306205e-02
sample52  -0.0381605826  3.086785e-02
sample53  -0.0855757630  6.730043e-02
sample54   0.0261723092  9.184260e-03
sample55  -0.0156418304  4.682404e-04
sample56   0.0307831193  2.597550e-02
sample57  -0.0157242103  4.829381e-02
sample58  -0.0031174404  1.359898e-02
sample59  -0.0373001859  5.868397e-03
sample60  -0.0142609099  5.831654e-03
sample61  -0.0122255144  2.663579e-02
sample62   0.0228002942 -8.692265e-03
sample63  -0.0833127581  5.473229e-02
sample64  -0.1166548159  4.196500e-02
sample65   0.0038808902  8.568590e-03
sample66   0.0011561811  1.766612e-02
sample67  -0.1129311062 -2.608702e-02
sample68  -0.0382526429 -3.804045e-02
sample69  -0.0476502440  4.003241e-03
sample70  -0.0110329882 -2.752719e-02
sample71   0.0096850282 -5.627056e-02
sample72   0.0487124704 -8.800131e-03
sample73   0.0773058132  8.239864e-03
sample74  -0.0102488176  2.454957e-02
sample75  -0.0286613976 -8.387293e-03
sample76  -0.0472655595 -2.129315e-02
sample77  -0.0865043074 -7.296820e-03
sample78   0.1070293698  2.818346e-02
sample79  -0.0165060681 -6.659721e-02
sample80  -0.0206765949 -8.712112e-03
sample81  -0.0050943615 -3.079175e-02
sample82   0.1153622361 -1.647054e-02
sample83   0.0367979217 -2.538114e-03
sample84   0.0199463070 -1.468961e-02
sample85  -0.0827122185 -2.709824e-04
sample86   0.0969487314 -1.699897e-02
sample87   0.0421957457 -1.965953e-02
sample88   0.0215934743  1.566050e-02
sample89   0.0751559502  2.811652e-02
sample90  -0.0057328000 -8.283795e-03
sample91  -0.1134005268 -8.603522e-02
sample92  -0.0101689918 -6.894992e-02
sample93   0.0725967502 -6.003176e-03
sample94  -0.0096878852 -4.693081e-03
sample95  -0.0223502239 -3.139636e-02
sample96  -0.0013232863 -1.963604e-02
sample97  -0.0476541710  1.183660e-02
sample98   0.0269546160 -5.978398e-03
sample99   0.0728179461  4.597884e-02
sample100 -0.0413398038  1.079347e-02
sample101  0.0087536994 -6.796076e-02
sample102  0.0032509529  3.932612e-03
sample103  0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105  0.0294940465 -7.140722e-03
sample106  0.0686472054  1.462895e-02
sample107  0.0748635927  8.401339e-03
sample108  0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110  0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112  0.0570870472  1.066018e-02
sample113 -0.0200110554  1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586  1.089843e-02
sample121  0.0542443861  3.861344e-02
sample122  0.0178575357  3.027138e-02
sample123  0.0775020581 -1.636852e-02
sample124 -0.0460701050  1.814758e-02
sample125  0.0543846585  2.075898e-03
sample126 -0.0729417144  3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128  0.0908136899  3.758801e-02
sample129  0.0552445878 -1.879062e-02
sample130  0.0007128089 -1.294308e-02
sample131 -0.0693311345  7.357082e-03
sample132 -0.0556565156  3.126995e-02
sample133  0.0375870104 -1.977240e-02
sample134 -0.1229130924  3.159495e-02
sample135  0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201  1.040683e-02
sample140  0.0452288969 -1.876279e-02
sample141 -0.0189142561  2.247042e-02
sample142  0.0297545566  1.280524e-02
sample143  0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491  5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147  0.0726083535 -1.239968e-02
sample148 -0.0284795794  3.389732e-02
sample149  0.0082261455 -6.399305e-02
sample150 -0.0765013197  2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152  0.0403422737 -2.714879e-02
sample153  0.0629117719  7.425085e-02
sample154  0.0551622927 -3.548984e-02
sample155  0.0654439133 -1.005306e-02
sample156  0.0209310714 -1.390213e-02
sample157  0.0851522597  6.577150e-03
sample158  0.0208354599 -4.663078e-03
sample159 -0.0498794349  1.913257e-02
sample160  0.0216074437  1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162  0.0963663017  5.705881e-02
sample163 -0.1009542191  7.174224e-02
sample164  0.0109881996  1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357  2.673084e-02
sample167 -0.0825048036  2.278863e-03
sample168  0.0486147429  1.793843e-02
sample169  0.0302506727  8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
                   [,1]          [,2]
sample1   -0.0621842115 -1.364509e-02
sample2    0.0944623785  9.720892e-03
sample3    0.0406196267 -2.236338e-02
sample4   -0.0229316496 -3.932487e-04
sample5   -0.0157330047  3.231033e-03
sample6   -0.0945794025  3.120720e-02
sample7   -0.0854427118 -1.052880e-02
sample8   -0.1376625920  4.286608e-02
sample9   -0.0377115311 -1.415134e-02
sample10   0.0035244506  1.280825e-02
sample11   0.0016639987 -1.717895e-02
sample12  -0.0781403168  1.884368e-02
sample13   0.0938400516  2.838858e-02
sample14  -0.0759839772  1.810989e-02
sample15  -0.0068340837 -2.705361e-03
sample16  -0.0590150849  4.757848e-02
sample17   0.1178805097  2.040526e-02
sample18   0.0767858320  1.756604e-02
sample19   0.0157112113 -2.172867e-02
sample20   0.0485318300 -2.327033e-02
sample21   0.0185928176  4.777095e-02
sample22  -0.0191358702 -2.329775e-02
sample23  -0.0672994194 -1.535656e-03
sample24   0.1047476642  5.935707e-02
sample25   0.0329844953 -1.358036e-02
sample26   0.1154952052  1.741529e-02
sample27   0.0133849853 -3.590922e-02
sample28   0.0821554039  2.042376e-02
sample29  -0.0567643690 -2.123848e-02
sample30   0.1016073931 -1.134728e-03
sample31  -0.0880396372  3.670548e-02
sample32   0.0300363338  1.182406e-02
sample33  -0.0467252272  3.739254e-03
sample34   0.0783666394  1.203777e-02
sample35   0.0424227097 -1.118559e-02
sample36  -0.1107646166 -1.143464e-02
sample37  -0.0191667664 -2.246060e-02
sample38   0.0155968095 -2.909621e-02
sample39   0.0746847148  7.148218e-03
sample40  -0.0517028178 -2.137267e-02
sample41   0.0234979494 -3.723018e-02
sample42   0.0388797356 -8.557228e-03
sample43   0.0149555568  7.210002e-03
sample44  -0.1150305613  3.461805e-02
sample45   0.0846146236  3.486020e-02
sample46   0.0884426404 -3.246853e-02
sample47   0.0748644971 -8.083045e-03
sample48  -0.0012033198 -9.403647e-03
sample49  -0.0872662737  3.616245e-02
sample50   0.0066941314 -5.284863e-02
sample51  -0.0411777630  3.791830e-02
sample52  -0.0379355780  2.180834e-02
sample53  -0.0851639886  4.751761e-02
sample54   0.0288006248  7.184424e-03
sample55  -0.0164920835  5.919925e-05
sample56   0.0355115616  1.951043e-02
sample57  -0.0141146068  3.492409e-02
sample58  -0.0015636132  9.862883e-03
sample59  -0.0390656483  3.590929e-03
sample60  -0.0139454780  3.963030e-03
sample61  -0.0106410274  1.919705e-02
sample62   0.0236748439 -5.922677e-03
sample63  -0.0846790877  3.839102e-02
sample64  -0.1202581015  2.846469e-02
sample65   0.0050548584  6.328644e-03
sample66   0.0028013072  1.291807e-02
sample67  -0.1231623009 -2.112565e-02
sample68  -0.0437782161 -2.845072e-02
sample69  -0.0501199692  2.053469e-03
sample70  -0.0140278645 -2.027157e-02
sample71   0.0057489505 -4.085977e-02
sample72   0.0511212704 -5.522408e-03
sample73   0.0828141409  7.431582e-03
sample74  -0.0085959456  1.772951e-02
sample75  -0.0312180394 -6.636869e-03
sample76  -0.0519051781 -1.640191e-02
sample77  -0.0925924762 -6.907800e-03
sample78   0.1163971046  2.251122e-02
sample79  -0.0240906926 -4.887766e-02
sample80  -0.0221327065 -6.730703e-03
sample81  -0.0072114968 -2.254399e-02
sample82   0.1204416674 -9.907422e-03
sample83   0.0386739485 -1.171663e-03
sample84   0.0195988488 -1.033806e-02
sample85  -0.0877680171 -1.725057e-03
sample86   0.1023541048 -1.062501e-02
sample87   0.0425213089 -1.356865e-02
sample88   0.0244788514  1.180820e-02
sample89   0.0804276691  2.188588e-02
sample90  -0.0074639871 -6.140721e-03
sample91  -0.1278832404 -6.485140e-02
sample92  -0.0162199697 -5.048358e-02
sample93   0.0769344893 -3.045135e-03
sample94  -0.0104345587 -3.593172e-03
sample95  -0.0260058453 -2.330475e-02
sample96  -0.0025018700 -1.433516e-02
sample97  -0.0492358305  7.774183e-03
sample98   0.0279220220 -3.862141e-03
sample99   0.0813921923  3.487339e-02
sample100 -0.0428797405  7.112807e-03
sample101  0.0032855240 -4.940743e-02
sample102  0.0038439317  2.938008e-03
sample103  0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105  0.0314853405 -4.656633e-03
sample106  0.0726456731  1.192390e-02
sample107  0.0807342975  7.508627e-03
sample108  0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110  0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112  0.0599954082  8.820317e-03
sample113 -0.0195006577  1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131  6.937326e-03
sample121  0.0613899126  2.915307e-02
sample122  0.0218424338  2.241775e-02
sample123  0.0809008460 -1.051759e-02
sample124 -0.0472109313  1.239887e-02
sample125  0.0583180947  2.521167e-03
sample126 -0.0753941872  2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128  0.1000212216  2.908091e-02
sample129  0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877  4.091672e-03
sample132 -0.0566219024  2.179861e-02
sample133  0.0384172955 -1.372840e-02
sample134 -0.1280862736  2.077912e-02
sample135  0.0592633273  6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861  6.729304e-03
sample140  0.0468926306 -1.285498e-02
sample141 -0.0186248693  1.605439e-02
sample142  0.0328031246  9.887746e-03
sample143  0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310  3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147  0.0765025670 -7.714769e-03
sample148 -0.0276016641  2.420589e-02
sample149  0.0027545308 -4.653007e-02
sample150 -0.0792296010  1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152  0.0409796547 -1.907063e-02
sample153  0.0734301757  5.528780e-02
sample154  0.0557740684 -2.487723e-02
sample155  0.0689436560 -6.127635e-03
sample156  0.0212272938 -9.747423e-03
sample157  0.0911931194  6.355708e-03
sample158  0.0220840645 -3.016357e-03
sample159 -0.0513244242  1.304175e-02
sample160  0.0246213576  1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162  0.1078802043  4.337260e-02
sample163 -0.1017965082  5.047171e-02
sample164  0.0119430799  9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180  1.899222e-02
sample167 -0.0872832229  1.516582e-04
sample168  0.0540714512  1.397701e-02
sample169  0.0328432652  7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
                   [,1]          [,2]
sample1    0.0133684846  2.195848e-02
sample2    0.0254157197 -1.058416e-02
sample3   -0.0049551479 -4.840017e-03
sample4    0.0310390570 -1.063929e-02
sample5    0.0046941318 -6.488426e-03
sample6   -0.0107406753 -1.026702e-02
sample7   -0.0225157631  2.624712e-04
sample8    0.0141320952 -9.505821e-03
sample9    0.0029681280  2.078210e-02
sample10   0.0131729174 -2.275042e-03
sample11  -0.0004164298  1.994019e-02
sample12  -0.0095211620  3.759883e-02
sample13   0.0091018604 -7.953956e-03
sample14  -0.0106557524 -9.181659e-03
sample15  -0.0249924121  3.262724e-02
sample16  -0.0156216400  1.375700e-02
sample17  -0.0019382446  1.073994e-03
sample18  -0.0221072481 -8.703592e-03
sample19   0.0146917619 -1.311712e-02
sample20  -0.0160353760  1.826290e-02
sample21   0.0035947899 -9.616341e-03
sample22  -0.0225060762 -2.532589e-03
sample23   0.0310000683  3.033060e-03
sample24   0.0499544372  1.809450e-02
sample25   0.0284442301 -1.932558e-02
sample26   0.0188220043  2.146985e-02
sample27  -0.0257763219 -1.999228e-03
sample28   0.0120888648  1.125834e-02
sample29  -0.0236482520  4.426726e-02
sample30  -0.0385486305 -2.055935e-02
sample31  -0.0181539336 -5.877838e-03
sample32  -0.0302630460 -2.607192e-03
sample33  -0.0319565715 -1.562628e-02
sample34  -0.0197970124  9.906813e-03
sample35  -0.0247412713 -5.434440e-03
sample36  -0.0386259060 -3.190394e-02
sample37  -0.0566199273 -4.192574e-02
sample38  -0.0142060273  2.259644e-02
sample39   0.0053589035  1.076485e-02
sample40  -0.0552546493 -3.819896e-02
sample41  -0.0013089975  9.278818e-05
sample42   0.0137252142 -1.664652e-02
sample43  -0.0151259626 -6.290953e-03
sample44   0.0617391754 -1.442883e-02
sample45   0.0231410886  1.163143e-03
sample46  -0.0148898209 -1.384176e-04
sample47  -0.0187252536  1.221690e-02
sample48   0.0432839432  1.416671e-02
sample49   0.0160818605 -3.588745e-02
sample50   0.0059333545  4.067003e-02
sample51  -0.0142914866  7.776270e-03
sample52  -0.0086339952  7.208917e-03
sample53  -0.0207386980  6.272432e-03
sample54  -0.0039856719 -1.316934e-02
sample55  -0.0056217017  5.692315e-03
sample56   0.0000123292  8.978290e-04
sample57  -0.0095805555  1.324253e-02
sample58  -0.0124160295 -7.326376e-03
sample59  -0.0400195442 -1.349736e-02
sample60  -0.0460063358  2.770091e-02
sample61  -0.0245266456  1.470710e-02
sample62  -0.0366022783 -3.437352e-03
sample63   0.0013742171  3.288796e-02
sample64  -0.0070599859  2.739588e-02
sample65   0.0041201911  1.498268e-02
sample66   0.0143173351 -1.968812e-02
sample67  -0.0467477531 -1.929938e-02
sample68  -0.0306751978 -1.436184e-02
sample69  -0.0125317217  4.130407e-03
sample70  -0.0068071487  8.080857e-03
sample71   0.0169170264 -7.027348e-03
sample72  -0.0346909749 -1.333770e-02
sample73  -0.0280506153  1.493843e-02
sample74  -0.0182611498  3.294697e-03
sample75  -0.0120563964  8.974612e-03
sample76   0.0001437236 -4.253184e-02
sample77   0.0065330299 -5.252886e-02
sample78   0.0288278141 -1.127782e-02
sample79   0.0503961481 -1.023318e-02
sample80  -0.0207693429  3.648391e-02
sample81   0.0163562768 -9.074596e-03
sample82  -0.0084317129 -1.478976e-02
sample83  -0.0474097918 -1.103126e-02
sample84   0.0177181395 -7.191197e-03
sample85  -0.0342718548 -3.082360e-02
sample86  -0.0261671791 -1.089491e-02
sample87  -0.0009486358 -2.411514e-02
sample88   0.0020528931 -2.894615e-02
sample89  -0.0189361111 -2.638639e-03
sample90  -0.0009863658 -2.390075e-02
sample91  -0.0124352695  8.153234e-02
sample92   0.0564264106 -8.909537e-03
sample93  -0.0081461774  1.570851e-02
sample94  -0.0054896581  1.547251e-02
sample95   0.0224073150 -4.374348e-04
sample96   0.0173528924 -3.050441e-03
sample97   0.0067948115  5.008237e-03
sample98  -0.0116030825  1.498764e-02
sample99   0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101  0.0124923691  3.091503e-02
sample102  0.0650835386 -1.367400e-02
sample103 -0.0042741828  7.855985e-03
sample104  0.0250591040 -4.171938e-03
sample105  0.0157516368 -3.121990e-02
sample106  0.0060593853 -5.101693e-03
sample107 -0.0098329626  1.044506e-02
sample108  0.0044269853  4.142036e-03
sample109  0.0572473486  1.517542e-02
sample110  0.0090474827 -5.119868e-03
sample111  0.0444263015  7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113  0.0241047399  6.706740e-03
sample114  0.0074558775 -4.728652e-03
sample115  0.0611851433  1.117210e-02
sample116  0.0432646951 -1.380556e-02
sample117  0.0516750066 -3.575617e-02
sample118  0.0139942100 -3.279138e-03
sample119  0.0291722987  5.587946e-02
sample120  0.0103515853 -1.690016e-03
sample121 -0.0091396331  3.552116e-02
sample122  0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124  0.0283466326  3.127845e-03
sample125  0.0016472378 -2.770692e-02
sample126 -0.0286529417  3.489336e-02
sample127 -0.0010224500  7.483214e-03
sample128  0.0209049296  2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968  4.637390e-03
sample132  0.0198526786  5.723983e-04
sample133  0.0088812957 -9.988115e-03
sample134 -0.0137484514  1.172591e-02
sample135 -0.0220314568  1.347465e-02
sample136 -0.0185173353  5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138  0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762  2.456863e-02
sample141  0.0058369763 -1.420854e-02
sample142  0.0207886071 -1.188764e-02
sample143  0.0092832598 -1.324238e-02
sample144  0.0028442140  3.627979e-03
sample145  0.0199749569  2.862202e-03
sample146 -0.0182236697  1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148  0.0065435868 -1.572917e-02
sample149  0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151  0.0245166984 -6.888241e-03
sample152  0.0107259913  3.314630e-02
sample153  0.0550963965  3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574  2.642246e-03
sample156 -0.0117803505  2.698265e-02
sample157 -0.0096167165  1.433840e-02
sample158 -0.0101754772  9.137620e-03
sample159  0.0120662931 -2.565236e-02
sample160 -0.0132238202  2.916023e-03
sample161  0.0274491966 -1.748284e-02
sample162  0.0012482909  3.152261e-02
sample163  0.0042031315  1.830701e-02
sample164  0.0174896157 -1.175915e-02
sample165  0.0097517662 -6.119019e-03
sample166  0.0190134679 -1.121582e-02
sample167 -0.0044140836  4.665585e-03
sample168  0.0049689168 -1.941822e-02
sample169 -0.0209802098  3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
                   [,1]          [,2]
sample1   -0.0515543627 -0.0305856787
sample2   -0.0144993256  0.0236342950
sample3   -0.0371833108 -0.0140263348
sample4    0.0068945388 -0.0132539692
sample5    0.0215035333 -0.0663338101
sample6   -0.0187055152  0.0088773016
sample7   -0.0061521552  0.0064029054
sample8   -0.0210874459  0.0334652901
sample9    0.0516865043 -0.0291142799
sample10   0.0059440366 -0.0527217447
sample11   0.0393010793 -0.0200624712
sample12  -0.0420837100  0.0131331362
sample13   0.0333252565  0.0818552509
sample14  -0.0190062644  0.0160202175
sample15  -0.0030968049 -0.0189230681
sample16  -0.0004452158  0.0018880102
sample17  -0.0185848615  0.0240170131
sample18  -0.0273093598  0.0230213640
sample19  -0.0217761111 -0.0445894441
sample20   0.0245820821  0.0159812738
sample21   0.0034527644 -0.0400016054
sample22  -0.0340789054  0.0039289109
sample23  -0.0010344929 -0.0310161212
sample24   0.0289468503  0.0760962436
sample25  -0.0119098496 -0.0122798760
sample26  -0.0181001057  0.0517892852
sample27   0.0050465417 -0.0086515844
sample28   0.0057491502  0.0358830107
sample29  -0.0051104246  0.0116605117
sample30  -0.0103085904  0.0039678538
sample31  -0.0319929858  0.0090606113
sample32  -0.0036232521 -0.0328202010
sample33  -0.0534742153  0.0024751837
sample34  -0.0067495749 -0.0111000311
sample35   0.0378745721  0.0465929296
sample36   0.0647886800  0.0359987924
sample37   0.0488441236  0.0492906912
sample38  -0.0251514062  0.0197110110
sample39  -0.0085428066 -0.0105117852
sample40   0.0379324087  0.0440810741
sample41  -0.0044199152 -0.0128820644
sample42  -0.0292553573 -0.0067045265
sample43  -0.0077829155 -0.0510178219
sample44   0.0045122248  0.0479660309
sample45  -0.0074444298 -0.0051116726
sample46  -0.0088025512  0.0196186661
sample47   0.0076696301  0.0215947965
sample48   0.0290108585 -0.0175568376
sample49  -0.0141754858  0.0184717099
sample50   0.0006282201 -0.0233054373
sample51   0.0441995177 -0.0410022921
sample52   0.0715329391 -0.0399499475
sample53  -0.0095954087 -0.0029140909
sample54   0.0048933768 -0.0281884386
sample55   0.0327325487 -0.0532290012
sample56   0.0323068984 -0.0256595538
sample57   0.0806603122 -0.0286748097
sample58  -0.0064792049 -0.0006945349
sample59   0.0088958941  0.0067389649
sample60   0.0874124612  0.0431964341
sample61   0.0577604571 -0.0326112099
sample62  -0.0313318464  0.0224391756
sample63  -0.0233625220  0.0125110562
sample64  -0.0086426068  0.0148770341
sample65   0.0025256193 -0.0404466327
sample66   0.0006014071 -0.0471576264
sample67   0.0706087042  0.0516228406
sample68   0.0082301011  0.0033109509
sample69  -0.0475076743  0.0001452708
sample70  -0.0600773716  0.0089986962
sample71  -0.0096321627 -0.0050761187
sample72  -0.0031773546 -0.0166221542
sample73  -0.0113700517 -0.0191726684
sample74  -0.0014179662 -0.0608101325
sample75   0.0041911740 -0.0399981269
sample76  -0.0055326449  0.0353114263
sample77  -0.0260214459  0.0305731380
sample78  -0.0119267436  0.0632236007
sample79   0.0186017239  0.0027402910
sample80   0.0241047889 -0.0472697181
sample81  -0.0220288317 -0.0079577210
sample82  -0.0180751258  0.0639051029
sample83  -0.0256671713 -0.0125898269
sample84   0.0161392598 -0.0567222449
sample85   0.0139988188  0.0322763454
sample86  -0.0198382995  0.0389225776
sample87   0.0266270281 -0.0032979996
sample88   0.0515677078  0.0117902495
sample89   0.0014022125 -0.0140510488
sample90  -0.0375949749  0.0044004551
sample91   0.0310397965  0.0440610926
sample92   0.0270570567  0.0324380452
sample93  -0.0215009202  0.0063993941
sample94  -0.0415702912 -0.0037692077
sample95  -0.0168416047  0.0010019120
sample96  -0.0285582661 -0.0187991000
sample97  -0.0490843868 -0.0266760748
sample98  -0.0171579033 -0.0112897471
sample99  -0.0271316525  0.0232395583
sample100 -0.0301789816  0.0305498693
sample101 -0.0264371151  0.0170723968
sample102  0.0012767734 -0.0248949597
sample103  0.0055214687 -0.0030040587
sample104  0.0251346074 -0.0165212671
sample105  0.0062424215 -0.0400309901
sample106  0.0069768684  0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679  0.0023637162
sample109 -0.0014762845  0.0165583675
sample110  0.0036971063  0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112  0.0046098120 -0.0048009350
sample113  0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453  0.0135805368
sample116  0.0183575759  0.0665377581
sample117  0.0227640036 -0.0012287760
sample118  0.0015695248  0.0472617382
sample119  0.0190084932  0.0590034062
sample120 -0.0449645755  0.0072755697
sample121  0.0077307184  0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123  0.0016959300  0.0028593594
sample124 -0.0365091615  0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408  0.0019165544
sample127 -0.0494064872  0.0088209044
sample128 -0.0155454766  0.0186819802
sample129 -0.0184340400  0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422  0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133  0.0204029276 -0.0282209049
sample134  0.0175513332  0.0262883962
sample135  0.0029009925  0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323  0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139  0.0073103935  0.0308956174
sample140  0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142  0.0268670676 -0.0239229634
sample143  0.0421049126 -0.0110888235
sample144  0.0017253664 -0.0341766012
sample145  0.0681741320 -0.0073526377
sample146 -0.0239965222  0.0118396767
sample147 -0.0063453522  0.0183130585
sample148  0.0230825251 -0.0379753037
sample149  0.0223298673  0.0188909118
sample150  0.0055709108  0.0174179009
sample151  0.0039177786 -0.0233533275
sample152  0.0134325667  0.0302344591
sample153  0.0511990309  0.0730230140
sample154  0.0006698324  0.0154177486
sample155  0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599  0.0154934573
sample158  0.0201775524 -0.0332982124
sample159 -0.0086909001  0.0073496711
sample160  0.0295437331 -0.0555734536
sample161  0.0332754288  0.0033779619
sample162  0.0121954537  0.0433540412
sample163 -0.0173490933  0.0227219128
sample164  0.0143374783 -0.0453542590
sample165  0.0343612593 -0.0511194536
sample166 -0.0157536004  0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919  0.0060747155
sample169  0.0116231468 -0.0015112800
> 
> ## 3.3 Plotting VAF
> 
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
> 
> # JIVE plotVAF
> plotVAF(jiveRes)
> 
> 
> #########################
> ## PART 4. Plot Results
> 
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+              combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+              background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+              axisSize=NULL,titleSize=NULL)
> 
> # Scores for common part. JIVE
> plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common",
+              combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+              background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+              axisSize=NULL,titleSize=NULL)
> 
> # Scores for common part. O2PLS.
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for common part. O2PLS.
> plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common",
+              combined=TRUE,block=NULL,color="classname",shape=NULL,
+              labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+              labelSize=NULL,axisSize=NULL,titleSize=NULL)
> 
> 
> # Scores for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for distinctive part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual",
+              combined=TRUE,block=NULL,color="classname",shape=NULL,
+              labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+              labelSize=NULL,axisSize=NULL,titleSize=NULL)
> 
> # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block)
> p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for common and distinctive part. DISCO  (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Loadings for common part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> # Loadings for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> # Combined plot for loadings from common and distinctive part  (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> 
> ## Plot scores and loadings togheter: Common components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+         combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+         background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+         axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> ## Plot scores and loadings togheter:  Common components O2PLS
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> ## Plot scores and loadings togheter: Distintive components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
   8.06    0.26    8.32 

STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsPCA.Rout


R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

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

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

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

Distinctive components
[[1]]
[1] 2

[[2]]
[1] 2

> 
> 
> #########################
> ## PART 3. Component Analysis
> 
> ## 3.1 Component analysis of the three methods
> discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                               scale=TRUE,weight=TRUE)
> jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                              scale=TRUE,weight=TRUE)
> o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                               scale=TRUE,weight=TRUE)
> 
> ## 3.2 Exploring scores structures
> 
> # Exploring DISCO-SCA scores structure
> discoRes@scores$common ## Common scores
                      1             2
sample1    0.0781574244  0.0431500450
sample2   -0.1192218277 -0.0294091083
sample3   -0.0531412245  0.0746839900
sample4    0.0292975257  0.0005957027
sample5    0.0202091749 -0.0110464311
sample6    0.1226089111 -0.1053466073
sample7    0.1078928002  0.0322477990
sample8    0.1782895500 -0.1449365192
sample9    0.0468698106  0.0455174355
sample10  -0.0036030469 -0.0420112478
sample11  -0.0035566515  0.0566292868
sample12   0.1006128895 -0.0641379952
sample13  -0.1174408122 -0.0907488743
sample14   0.0981203280 -0.0617737112
sample15   0.0085334184  0.0087015842
sample16   0.0783148749 -0.1581292878
sample17  -0.1483609862 -0.0638581910
sample18  -0.0963086279 -0.0556638258
sample19  -0.0217244163  0.0720084367
sample20  -0.0635636504  0.0779654896
sample21  -0.0201840212 -0.1566391894
sample22   0.0218268555  0.0764106361
sample23   0.0852042095  0.0032685771
sample24  -0.1287170192 -0.1924547862
sample25  -0.0430574107  0.0456563193
sample26  -0.1453896699 -0.0541513945
sample27  -0.0197489013  0.1185659259
sample28  -0.1025336168 -0.0650686565
sample29   0.0706018335  0.0682990538
sample30  -0.1295627672  0.0066772790
sample31   0.1147449155 -0.1232684984
sample32  -0.0374310980 -0.0380175280
sample33   0.0599515852 -0.0136864494
sample34  -0.0984200871 -0.0375319345
sample35  -0.0543098415  0.0378108733
sample36   0.1403625376  0.0343760565
sample37   0.0228941714  0.0732852266
sample38  -0.0222077408  0.0962596112
sample39  -0.0941738473 -0.0215199894
sample40   0.0643800981  0.0687876790
sample41  -0.0327638160  0.1232188165
sample42  -0.0500431845  0.0292471704
sample43  -0.0184498906 -0.0233009943
sample44   0.1487899163 -0.1171359885
sample45  -0.1050773999 -0.1123203957
sample46  -0.1151195864  0.1094030120
sample47  -0.0962593800  0.0288465640
sample48   0.0004837457  0.0310273831
sample49   0.1135207974 -0.1213974509
sample50  -0.0123553302  0.1740743070
sample51   0.0550529938 -0.1258885104
sample52   0.0499121293 -0.0728543287
sample53   0.1119773717 -0.1588011803
sample54  -0.0360055685 -0.0228575362
sample55   0.0210418955 -0.0006731152
sample56  -0.0434169164 -0.0633125981
sample57   0.0197824733 -0.1150712110
sample58   0.0030439874 -0.0326096840
sample59   0.0500253007 -0.0129414758
sample60   0.0184278619 -0.0136079808
sample61   0.0150299405 -0.0635023059
sample62  -0.0304764057  0.0201322770
sample63   0.1102252582 -0.1285977051
sample64   0.1552588152 -0.0971167445
sample65  -0.0058503061 -0.0207116026
sample66  -0.0025605291 -0.0424321641
sample67   0.1546634710  0.0661722086
sample68   0.0536369088  0.0923686935
sample69   0.0640330284 -0.0081982118
sample70   0.0163517603  0.0663230389
sample71  -0.0102537710  0.1345919541
sample72  -0.0654196205  0.0196122882
sample73  -0.1048556256 -0.0220935665
sample74   0.0123799408 -0.0586113547
sample75   0.0392077836  0.0209756166
sample76   0.0648953372  0.0524764524
sample77   0.1172922153  0.0201186028
sample78  -0.1463067864 -0.0708475332
sample79   0.0265211249  0.1603302938
sample80   0.0279737021  0.0214207290
sample81   0.0079211456  0.0738448939
sample82  -0.1544236528  0.0361468788
sample83  -0.0494211617  0.0050053089
sample84  -0.0259038474  0.0346547557
sample85   0.1116484275  0.0031501633
sample86  -0.1306483150  0.0377217798
sample87  -0.0554778218  0.0459749148
sample88  -0.0301623726 -0.0382197374
sample89  -0.1016866730 -0.0694031991
sample90   0.0086819826  0.0201319922
sample91   0.1578625127  0.2097830066
sample92   0.0170936945  0.1655801389
sample93  -0.0979806885  0.0121512882
sample94   0.0131484011  0.0114932342
sample95   0.0315682621  0.0758856635
sample96   0.0024125588  0.0470133394
sample97   0.0634545387 -0.0270333086
sample98  -0.0359374731  0.0135489463
sample99  -0.1009163118 -0.1124782879
sample100  0.0551753126 -0.0246488864
sample101 -0.0080119033  0.1627367091
sample102 -0.0046444064 -0.0095639076
sample103 -0.0472523291  0.0940393744
sample104  0.0198159534  0.0591089024
sample105 -0.0400237775  0.0160910064
sample106 -0.0923808331 -0.0369018330
sample107 -0.1019374012 -0.0224953515
sample108 -0.0877091660  0.0128833555
sample109  0.0864824531  0.0900935683
sample110 -0.1223115491  0.0096084713
sample111  0.0257354668  0.0936164093
sample112 -0.0765286629 -0.0270346183
sample113  0.0258803346 -0.0377499956
sample114  0.0021138813  0.0882013738
sample115  0.0303460382  0.0723579091
sample116  0.0780508619  0.0685062175
sample117  0.0536898255  0.0911902951
sample118  0.0666651233  0.0236229848
sample119  0.1021871569  0.2324934232
sample120  0.0750216580 -0.0243380534
sample121 -0.0756936330 -0.0942949513
sample122 -0.0259627943 -0.0731990318
sample123 -0.1037846317  0.0369197957
sample124  0.0611208047 -0.0421727000
sample125 -0.0738472732 -0.0066950630
sample126  0.0972916345 -0.0762637093
sample127  0.0824697570  0.0096637134
sample128 -0.1249407471 -0.0929315129
sample129 -0.0734067663  0.0434365089
sample130 -0.0003502073  0.0309852418
sample131  0.0930182779 -0.0155935550
sample132  0.0736222906 -0.0733032386
sample133 -0.0498397994  0.0462436449
sample134  0.1644873553 -0.0720003627
sample135 -0.0752297289 -0.0003815355
sample136  0.0227145596  0.0495507745
sample137  0.0564717251  0.0288918281
sample138  0.0255988163  0.0610853641
sample139  0.0621217808 -0.0235805528
sample140 -0.0604152710  0.0435596053
sample141  0.0246743993 -0.0532649681
sample142 -0.0409560202 -0.0316282108
sample143 -0.0077355195  0.0476895534
sample144  0.0173240786  0.0156777487
sample145  0.0485474793 -0.1202772210
sample146  0.0419645458  0.0811283212
sample147 -0.0977308476  0.0274843074
sample148  0.0368256288 -0.0803980369
sample149 -0.0072865839  0.1532984524
sample150  0.1020825306 -0.0624772430
sample151  0.0305399116  0.0289275422
sample152 -0.0533594788  0.0638308168
sample153 -0.0891627121 -0.1799583681
sample154 -0.0727557597  0.0834162363
sample155 -0.0880668701  0.0220821706
sample156 -0.0276561190  0.0326626350
sample157 -0.1155032203 -0.0183615091
sample158 -0.0281507589  0.0104939736
sample159  0.0663235811 -0.0443838664
sample160 -0.0302643926 -0.0404264084
sample161  0.0114715662  0.0591022724
sample162 -0.1337086925 -0.1398135410
sample163  0.1330124672 -0.1688781609
sample164 -0.0150336047 -0.0028418259
sample165  0.0076520283  0.0164127343
sample166  0.0367794511 -0.0630664194
sample167  0.1111988829 -0.0030057405
sample168 -0.0672981526 -0.0446279958
sample169 -0.0413005025 -0.0224391904
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
                      1             2
sample1   -0.0420516927  0.0867862958
sample2   -0.0820827675 -0.0410978279
sample3    0.0155896888 -0.0195182336
sample4   -0.1001336959 -0.0410786973
sample5   -0.0153465525 -0.0253259737
sample6    0.0340329570 -0.0408223095
sample7    0.0722578776  0.0002332459
sample8   -0.0457494766 -0.0370016350
sample9   -0.0086250668  0.0820184888
sample10  -0.0423597279 -0.0083923419
sample11   0.0022546721  0.0787766057
sample12   0.0322107344  0.1479824779
sample13  -0.0293886225 -0.0306748694
sample14   0.0337484905 -0.0367506756
sample15   0.0815538368  0.1275622726
sample16   0.0508457535  0.0540604802
sample17   0.0062598593  0.0041023723
sample18   0.0705641545 -0.0351047492
sample19  -0.0476844374 -0.0509598240
sample20   0.0522960111  0.0715522024
sample21  -0.0119121336 -0.0376093142
sample22   0.0724390274 -0.0095624902
sample23  -0.0992532304  0.0134288497
sample24  -0.1595111428  0.0728661554
sample25  -0.0920694904 -0.0749757544
sample26  -0.0595538692  0.0848965862
sample27   0.0826481608 -0.0086735178
sample28  -0.0384786060  0.0440966760
sample29   0.0777668750  0.1735308756
sample30   0.1229471064 -0.0819005167
sample31   0.0579850585 -0.0238644586
sample32   0.0970394325 -0.0111426025
sample33   0.1017588295 -0.0630442273
sample34   0.0637923824  0.0377941888
sample35   0.0789983633 -0.0229722968
sample36   0.1224939361 -0.1274954543
sample37   0.1798819374 -0.1673426865
sample38   0.0466300717  0.0888161087
sample39  -0.0168687264  0.0421533693
sample40   0.1756390749 -0.1526641805
sample41   0.0042366401  0.0004928816
sample42  -0.0447850863 -0.0651505149
sample43   0.0482309058 -0.0253529144
sample44  -0.1986710167 -0.0545778447
sample45  -0.0741832781  0.0054703045
sample46   0.0478768167 -0.0007071873
sample47   0.0608187378  0.0481622821
sample48  -0.1381490476  0.0578287365
sample49  -0.0530515676 -0.1405532991
sample50  -0.0173806470  0.1602389622
sample51   0.0462565664  0.0303473971
sample52   0.0280068070  0.0280388483
sample53   0.0667626953  0.0237702236
sample54   0.0121834428 -0.0521354295
sample55   0.0182396019  0.0221328484
sample56  -0.0001253203  0.0030907352
sample57   0.0316679994  0.0530190381
sample58   0.0393919351 -0.0297798631
sample59   0.1278291707 -0.0546527562
sample60   0.1486986193  0.1069157034
sample61   0.0793124987  0.0569796765
sample62   0.1172800112 -0.0149198137
sample63  -0.0028722665  0.1300519822
sample64   0.0237367953  0.1073287796
sample65  -0.0126534475  0.0589808389
sample66  -0.0468193196 -0.0771072848
sample67   0.1494263483 -0.0769859802
sample68   0.0977958267 -0.0577350746
sample69   0.0403087204  0.0156042233
sample70   0.0221528342  0.0315441020
sample71  -0.0546439395 -0.0272396596
sample72   0.1107487101 -0.0537319049
sample73   0.0906761550  0.0579966864
sample74   0.0586557252  0.0121421833
sample75   0.0390492380  0.0349282927
sample76  -0.0022961910 -0.1676558785
sample77  -0.0232096271 -0.2067302855
sample78  -0.0929752401 -0.0434939771
sample79  -0.1619502175 -0.0378114703
sample80   0.0680364420  0.1424663697
sample81  -0.0530786799 -0.0358351007
sample82   0.0266820682 -0.0577445023
sample83   0.1517234818 -0.0448553895
sample84  -0.0570968337 -0.0273813420
sample85   0.1086290352 -0.1228118987
sample86   0.0833858500 -0.0442914736
sample87   0.0022017210 -0.0943906844
sample88  -0.0078222725 -0.1140506547
sample89   0.0611059588 -0.0094584985
sample90   0.0022927360 -0.0936253990
sample91   0.0433582638  0.3205982959
sample92  -0.1815340961 -0.0334680806
sample93   0.0267629821  0.0614429097
sample94   0.0181876799  0.0605090455
sample95  -0.0720378534 -0.0013045864
sample96  -0.0559716741 -0.0118791589
sample97  -0.0217410557  0.0195414067
sample98   0.0379176484  0.0588357212
sample99  -0.0792423527 -0.0151274053
sample100  0.0222117214 -0.0023321364
sample101 -0.0387235005  0.1224226114
sample102 -0.2094613740 -0.0516443247
sample103  0.0138477730  0.0301052000
sample104 -0.0807988887 -0.0162719150
sample105 -0.0520493668 -0.1229665329
sample106 -0.0192611928 -0.0185238248
sample107  0.0319017374  0.0405123366
sample108 -0.0140691719  0.0163421333
sample109 -0.1831933375  0.0613007035
sample110 -0.0292790957 -0.0199849172
sample111 -0.1423255632  0.0327339908
sample112  0.0426333750 -0.0029083322
sample113 -0.0771903280  0.0268733411
sample114 -0.0241644846 -0.0184080489
sample115 -0.1959018249  0.0460130111
sample116 -0.1394477794 -0.0530806190
sample117 -0.1672364357 -0.1386536880
sample118 -0.0448344831 -0.0117622048
sample119 -0.0910394883  0.2217433156
sample120 -0.0331391609 -0.0057274597
sample121  0.0307577812  0.1392506627
sample122 -0.0839778646 -0.0291994686
sample123  0.0239649176 -0.0642163659
sample124 -0.0909149394  0.0130419228
sample125 -0.0065350526 -0.1092631847
sample126  0.0935313863  0.1368284316
sample127  0.0035387153  0.0292755646
sample128 -0.0660292734  0.1018566115
sample129  0.0693637005 -0.0695421540
sample130  0.0008492252 -0.0669704330
sample131  0.0431024613  0.0174065002
sample132 -0.0637037853  0.0029374523
sample133 -0.0289496280 -0.0390818919
sample134  0.0446205915  0.0456334654
sample135  0.0712336753  0.0521635160
sample136  0.0596268840  0.0197299494
sample137  0.0793150843 -0.0380628072
sample138 -0.0973550403 -0.0454218543
sample139  0.0539906302 -0.1534327194
sample140  0.0850825047  0.0955814777
sample141 -0.0192680000 -0.0554450138
sample142 -0.0672260628 -0.0461321116
sample143 -0.0303731640 -0.0519260318
sample144 -0.0089365263  0.0145814884
sample145 -0.0638765030  0.0122258230
sample146  0.0585853271  0.0063083527
sample147  0.0894132579 -0.1124615442
sample148 -0.0216363752 -0.0615967195
sample149 -0.0515425671 -0.0839903625
sample150  0.0568285862 -0.0124468748
sample151 -0.0789533348 -0.0261831416
sample152 -0.0330756033  0.1306443499
sample153 -0.1751924736  0.1497731600
sample154  0.0421421319 -0.0037010068
sample155  0.0680176494  0.0095711418
sample156  0.0388909435  0.1057563063
sample157  0.0314769739  0.0561367509
sample158  0.0329620089  0.0353947415
sample159 -0.0398414628 -0.1007373888
sample160  0.0424940200  0.0108496290
sample161 -0.0888372895 -0.0679700418
sample162 -0.0027471246  0.1237843848
sample163 -0.0126099352  0.0725434309
sample164 -0.0566779460 -0.0458324355
sample165 -0.0315336755 -0.0236362444
sample166 -0.0612055810 -0.0425233218
sample167  0.0142729909  0.0179308321
sample168 -0.0169501758 -0.0769617954
sample169  0.0675081152  0.0131505525
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
                      1             2
sample1    0.0012329651 -1.635717e-01
sample2    0.0724350103 -6.021223e-03
sample3    0.0188460443 -1.080036e-01
sample4   -0.0390145302  3.114366e-04
sample5   -0.1774811626 -2.996384e-02
sample6    0.0451444486 -3.455856e-02
sample7    0.0226466219 -7.020193e-03
sample8    0.1033680294 -9.856735e-03
sample9   -0.1350011811  8.979097e-02
sample10  -0.1259887210 -5.097851e-02
sample11  -0.0979788430  7.086532e-02
sample12   0.0863019151 -8.620316e-02
sample13   0.1381401136  1.828007e-01
sample14   0.0615073897 -2.642802e-02
sample15  -0.0381598942 -3.101666e-02
sample16   0.0048776824  1.271869e-03
sample17   0.0788481028 -1.547551e-02
sample18   0.0884188836 -3.795486e-02
sample19  -0.0703044433 -1.084004e-01
sample20   0.0025585466  7.975871e-02
sample21  -0.0941601541 -4.126736e-02
sample22   0.0550273396 -7.806747e-02
sample23  -0.0679495329 -4.102004e-02
sample24   0.1310962867  1.649310e-01
sample25  -0.0113585298 -4.426862e-02
sample26   0.1402945962  2.016547e-02
sample27  -0.0261561187  1.588390e-03
sample28   0.0724198755  5.850596e-02
sample29   0.0330058518  2.060782e-03
sample30   0.0228752610 -2.015433e-02
sample31   0.0635068032 -6.670332e-02
sample32  -0.0685099586 -4.955274e-02
sample33   0.0777765272 -1.272079e-01
sample34  -0.0157842350 -3.024314e-02
sample35   0.0529632714  1.500972e-01
sample36  -0.0070900850  2.025307e-01
sample37   0.0442420523  1.802088e-01
sample38   0.0781511258 -3.676424e-02
sample39  -0.0120331818 -3.388840e-02
sample40   0.0473292001  1.471561e-01
sample41  -0.0228189473 -2.673558e-02
sample42   0.0245360256 -7.960866e-02
sample43  -0.1036362754 -8.229578e-02
sample44   0.1012228792  7.049459e-02
sample45  -0.0013731952 -2.450905e-02
sample46   0.0558509978  2.947346e-03
sample47   0.0380481174  4.554171e-02
sample48  -0.0784342143  4.888983e-02
sample49   0.0605164017 -1.162351e-02
sample50  -0.0530079377 -2.737937e-02
sample51  -0.1514646472  5.678347e-02
sample52  -0.1860935233  1.246717e-01
sample53   0.0064177193 -2.700991e-02
sample54  -0.0697038310 -2.308388e-02
sample55  -0.1633577038  1.366441e-02
sample56  -0.1011485067  4.682207e-02
sample57  -0.1730374189  1.609603e-01
sample58   0.0071384740 -1.666955e-02
sample59   0.0030461696  3.005282e-02
sample60  -0.0215835192  2.665877e-01
sample61  -0.1510583620  1.002385e-01
sample62   0.0925533980 -4.845845e-02
sample63   0.0596311858 -4.137019e-02
sample64   0.0449225830 -2.600566e-03
sample65  -0.0939383737 -4.406908e-02
sample66  -0.1063400713 -5.709990e-02
sample67   0.0201589897  2.361727e-01
sample68  -0.0037203262  2.418384e-02
sample69   0.0645161240 -1.155622e-01
sample70   0.1013440012 -1.351789e-01
sample71   0.0016467795 -2.976844e-02
sample72  -0.0328892992 -2.835861e-02
sample73  -0.0275079981 -5.148187e-02
sample74  -0.1341719619 -7.895280e-02
sample75  -0.0951575667 -3.943186e-02
sample76   0.0864721914  3.034990e-02
sample77   0.1035749541 -2.545353e-02
sample78   0.1575644180  4.939600e-02
sample79  -0.0189137224  4.874679e-02
sample80  -0.1384140599  4.262213e-05
sample81   0.0118846422 -6.357932e-02
sample82   0.1675308175  3.533911e-02
sample83   0.0065673466 -7.812613e-02
sample84  -0.1486891628 -3.109057e-02
sample85   0.0532724397  7.417881e-02
sample86   0.1138477355 -1.917764e-05
sample87  -0.0432864016  6.080471e-02
sample88  -0.0433450377  1.402491e-01
sample89  -0.0331205700 -1.395400e-02
sample90   0.0607412829 -8.610414e-02
sample91   0.0566272435  1.303746e-01
sample92   0.0359582338  1.061604e-01
sample93   0.0433646390 -4.443635e-02
sample94   0.0477291324 -1.059574e-01
sample95   0.0249595718 -3.980526e-02
sample96  -0.0035219021 -9.293928e-02
sample97   0.0066048798 -1.527231e-01
sample98  -0.0020366796 -5.579551e-02
sample99   0.0886616178 -3.728220e-02
sample100  0.1091259154 -3.560420e-02
sample101  0.0739726388 -4.318002e-02
sample102 -0.0574461176 -2.783908e-02
sample103 -0.0142731071  9.705528e-03
sample104 -0.0710395276  4.068351e-02
sample105 -0.0980831346 -3.452952e-02
sample106  0.0254259339  3.628986e-02
sample107  0.0160653513 -9.173394e-02
sample108  0.0200987670 -2.379692e-02
sample109  0.0389780543  1.692361e-02
sample110  0.0326304851  2.988111e-02
sample111 -0.0676937635 -6.038212e-02
sample112 -0.0167883396  5.336938e-03
sample113 -0.0969217006 -2.757601e-02
sample114  0.0026398322 -9.209159e-02
sample115  0.0308047227  1.603826e-02
sample116  0.1240307058  1.273000e-01
sample117 -0.0334729188  5.392712e-02
sample118  0.1037152866  6.252431e-02
sample119  0.1064176439  1.196202e-01
sample120  0.0771355115 -1.004932e-01
sample121  0.0129350816  3.181978e-02
sample122 -0.0847492240 -5.568322e-02
sample123  0.0041336780  7.693168e-03
sample124  0.0583458021 -8.396386e-02
sample125 -0.0634844565 -5.232539e-02
sample126  0.0662581020 -1.091733e-01
sample127  0.0865024615 -1.094176e-01
sample128  0.0627817502 -1.470958e-02
sample129  0.0336276465 -4.007861e-02
sample130  0.0293517754 -8.046118e-02
sample131  0.0469197665 -2.209760e-03
sample132  0.0241740732 -1.248598e-01
sample133 -0.0907303238  1.466700e-02
sample134  0.0350842077  7.539662e-02
sample135 -0.0001333382  9.185363e-03
sample136  0.0335876065 -9.860277e-02
sample137  0.0640148919 -7.554473e-02
sample138 -0.0060964897 -1.742762e-02
sample139  0.0592084462  5.614968e-02
sample140 -0.0427985925 -1.099554e-02
sample141 -0.0618796333 -9.301036e-02
sample142 -0.0898554464  3.573420e-02
sample143 -0.0817389272  8.880523e-02
sample144 -0.0787754786 -3.821392e-02
sample145 -0.1085821576  1.569477e-01
sample146  0.0589557908 -4.373364e-02
sample147  0.0495330463  7.277177e-03
sample148 -0.1161592756  9.079109e-03
sample149  0.0121579338  7.788371e-02
sample150  0.0314512559  3.520212e-02
sample151 -0.0575382204 -1.945351e-02
sample152  0.0494542041  7.025536e-02
sample153  0.0941332764  2.153298e-01
sample154  0.0335931975  2.078725e-02
sample155 -0.0690457625 -2.780412e-02
sample156 -0.1039901613 -6.292527e-02
sample157  0.0408645819  8.065518e-03
sample158 -0.1018105305  7.816862e-03
sample159  0.0281730551 -1.207204e-02
sample160 -0.1643052973  2.978102e-03
sample161 -0.0374329329  8.524611e-02
sample162  0.0804535404  8.349760e-02
sample163  0.0743228047 -1.406220e-02
sample164 -0.1208806011 -2.139459e-02
sample165 -0.1608115937  2.025192e-02
sample166  0.0425944667 -2.660711e-02
sample167  0.0226849476 -4.464282e-02
sample168  0.0180735622 -7.465974e-04
sample169 -0.0190778979  2.645401e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
                   [,1]          [,2]
sample1   -0.0572060227 -1.729087e-02
sample2    0.0875245208  1.112588e-02
sample3    0.0403482602 -3.168994e-02
sample4   -0.0218345996  4.052760e-06
sample5   -0.0150905011  4.795041e-03
sample6   -0.0924362933  4.511003e-02
sample7   -0.0793066751 -1.243823e-02
sample8   -0.1342997187  6.215220e-02
sample9   -0.0338886944 -1.854401e-02
sample10   0.0020547173  1.749421e-02
sample11   0.0037275602 -2.364116e-02
sample12  -0.0753094533  2.772698e-02
sample13   0.0856160091  3.679963e-02
sample14  -0.0737457307  2.668452e-02
sample15  -0.0062111746 -3.554864e-03
sample16  -0.0602355268  6.675115e-02
sample17   0.1086768843  2.524534e-02
sample18   0.0702999472  2.231671e-02
sample19   0.0173785882 -3.024846e-02
sample20   0.0484173812 -3.310904e-02
sample21   0.0124657042  6.517144e-02
sample22  -0.0140989936 -3.159137e-02
sample23  -0.0627028403 -5.393710e-04
sample24   0.0919972100  7.909297e-02
sample25   0.0326998483 -1.945206e-02
sample26   0.1064741246  2.120849e-02
sample27   0.0166058995 -4.964993e-02
sample28   0.0743504770  2.614211e-02
sample29  -0.0511008491 -2.782647e-02
sample30   0.0962250842 -3.974893e-03
sample31  -0.0869563008  5.250819e-02
sample32   0.0271858919  1.552005e-02
sample33  -0.0448364581  6.243160e-03
sample34   0.0718415218  1.469396e-02
sample35   0.0403086451 -1.632629e-02
sample36  -0.1036402827 -1.304320e-02
sample37  -0.0159385744 -3.036525e-02
sample38   0.0182198369 -4.034805e-02
sample39   0.0690363619  8.058350e-03
sample40  -0.0467312750 -2.810325e-02
sample41   0.0263674438 -5.171216e-02
sample42   0.0374578960 -1.268634e-02
sample43   0.0132336869  9.536642e-03
sample44  -0.1119154428  5.028683e-02
sample45   0.0759639367  4.587903e-02
sample46   0.0871885519 -4.670385e-02
sample47   0.0721490571 -1.288540e-02
sample48   0.0005086144 -1.290565e-02
sample49  -0.0858177028  5.173760e-02
sample50   0.0118992665 -7.276215e-02
sample51  -0.0426446855  5.306205e-02
sample52  -0.0381605826  3.086785e-02
sample53  -0.0855757630  6.730043e-02
sample54   0.0261723092  9.184260e-03
sample55  -0.0156418304  4.682404e-04
sample56   0.0307831193  2.597550e-02
sample57  -0.0157242103  4.829381e-02
sample58  -0.0031174404  1.359898e-02
sample59  -0.0373001859  5.868397e-03
sample60  -0.0142609099  5.831654e-03
sample61  -0.0122255144  2.663579e-02
sample62   0.0228002942 -8.692265e-03
sample63  -0.0833127581  5.473229e-02
sample64  -0.1166548159  4.196500e-02
sample65   0.0038808902  8.568590e-03
sample66   0.0011561811  1.766612e-02
sample67  -0.1129311062 -2.608702e-02
sample68  -0.0382526429 -3.804045e-02
sample69  -0.0476502440  4.003241e-03
sample70  -0.0110329882 -2.752719e-02
sample71   0.0096850282 -5.627056e-02
sample72   0.0487124704 -8.800131e-03
sample73   0.0773058132  8.239864e-03
sample74  -0.0102488176  2.454957e-02
sample75  -0.0286613976 -8.387293e-03
sample76  -0.0472655595 -2.129315e-02
sample77  -0.0865043074 -7.296820e-03
sample78   0.1070293698  2.818346e-02
sample79  -0.0165060681 -6.659721e-02
sample80  -0.0206765949 -8.712112e-03
sample81  -0.0050943615 -3.079175e-02
sample82   0.1153622361 -1.647054e-02
sample83   0.0367979217 -2.538114e-03
sample84   0.0199463070 -1.468961e-02
sample85  -0.0827122185 -2.709824e-04
sample86   0.0969487314 -1.699897e-02
sample87   0.0421957457 -1.965953e-02
sample88   0.0215934743  1.566050e-02
sample89   0.0751559502  2.811652e-02
sample90  -0.0057328000 -8.283795e-03
sample91  -0.1134005268 -8.603522e-02
sample92  -0.0101689918 -6.894992e-02
sample93   0.0725967502 -6.003176e-03
sample94  -0.0096878852 -4.693081e-03
sample95  -0.0223502239 -3.139636e-02
sample96  -0.0013232863 -1.963604e-02
sample97  -0.0476541710  1.183660e-02
sample98   0.0269546160 -5.978398e-03
sample99   0.0728179461  4.597884e-02
sample100 -0.0413398038  1.079347e-02
sample101  0.0087536994 -6.796076e-02
sample102  0.0032509529  3.932612e-03
sample103  0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105  0.0294940465 -7.140722e-03
sample106  0.0686472054  1.462895e-02
sample107  0.0748635927  8.401339e-03
sample108  0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110  0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112  0.0570870472  1.066018e-02
sample113 -0.0200110554  1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586  1.089843e-02
sample121  0.0542443861  3.861344e-02
sample122  0.0178575357  3.027138e-02
sample123  0.0775020581 -1.636852e-02
sample124 -0.0460701050  1.814758e-02
sample125  0.0543846585  2.075898e-03
sample126 -0.0729417144  3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128  0.0908136899  3.758801e-02
sample129  0.0552445878 -1.879062e-02
sample130  0.0007128089 -1.294308e-02
sample131 -0.0693311345  7.357082e-03
sample132 -0.0556565156  3.126995e-02
sample133  0.0375870104 -1.977240e-02
sample134 -0.1229130924  3.159495e-02
sample135  0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201  1.040683e-02
sample140  0.0452288969 -1.876279e-02
sample141 -0.0189142561  2.247042e-02
sample142  0.0297545566  1.280524e-02
sample143  0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491  5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147  0.0726083535 -1.239968e-02
sample148 -0.0284795794  3.389732e-02
sample149  0.0082261455 -6.399305e-02
sample150 -0.0765013197  2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152  0.0403422737 -2.714879e-02
sample153  0.0629117719  7.425085e-02
sample154  0.0551622927 -3.548984e-02
sample155  0.0654439133 -1.005306e-02
sample156  0.0209310714 -1.390213e-02
sample157  0.0851522597  6.577150e-03
sample158  0.0208354599 -4.663078e-03
sample159 -0.0498794349  1.913257e-02
sample160  0.0216074437  1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162  0.0963663017  5.705881e-02
sample163 -0.1009542191  7.174224e-02
sample164  0.0109881996  1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357  2.673084e-02
sample167 -0.0825048036  2.278863e-03
sample168  0.0486147429  1.793843e-02
sample169  0.0302506727  8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
                   [,1]          [,2]
sample1   -0.0621842115 -1.364509e-02
sample2    0.0944623785  9.720892e-03
sample3    0.0406196267 -2.236338e-02
sample4   -0.0229316496 -3.932487e-04
sample5   -0.0157330047  3.231033e-03
sample6   -0.0945794025  3.120720e-02
sample7   -0.0854427118 -1.052880e-02
sample8   -0.1376625920  4.286608e-02
sample9   -0.0377115311 -1.415134e-02
sample10   0.0035244506  1.280825e-02
sample11   0.0016639987 -1.717895e-02
sample12  -0.0781403168  1.884368e-02
sample13   0.0938400516  2.838858e-02
sample14  -0.0759839772  1.810989e-02
sample15  -0.0068340837 -2.705361e-03
sample16  -0.0590150849  4.757848e-02
sample17   0.1178805097  2.040526e-02
sample18   0.0767858320  1.756604e-02
sample19   0.0157112113 -2.172867e-02
sample20   0.0485318300 -2.327033e-02
sample21   0.0185928176  4.777095e-02
sample22  -0.0191358702 -2.329775e-02
sample23  -0.0672994194 -1.535656e-03
sample24   0.1047476642  5.935707e-02
sample25   0.0329844953 -1.358036e-02
sample26   0.1154952052  1.741529e-02
sample27   0.0133849853 -3.590922e-02
sample28   0.0821554039  2.042376e-02
sample29  -0.0567643690 -2.123848e-02
sample30   0.1016073931 -1.134728e-03
sample31  -0.0880396372  3.670548e-02
sample32   0.0300363338  1.182406e-02
sample33  -0.0467252272  3.739254e-03
sample34   0.0783666394  1.203777e-02
sample35   0.0424227097 -1.118559e-02
sample36  -0.1107646166 -1.143464e-02
sample37  -0.0191667664 -2.246060e-02
sample38   0.0155968095 -2.909621e-02
sample39   0.0746847148  7.148218e-03
sample40  -0.0517028178 -2.137267e-02
sample41   0.0234979494 -3.723018e-02
sample42   0.0388797356 -8.557228e-03
sample43   0.0149555568  7.210002e-03
sample44  -0.1150305613  3.461805e-02
sample45   0.0846146236  3.486020e-02
sample46   0.0884426404 -3.246853e-02
sample47   0.0748644971 -8.083045e-03
sample48  -0.0012033198 -9.403647e-03
sample49  -0.0872662737  3.616245e-02
sample50   0.0066941314 -5.284863e-02
sample51  -0.0411777630  3.791830e-02
sample52  -0.0379355780  2.180834e-02
sample53  -0.0851639886  4.751761e-02
sample54   0.0288006248  7.184424e-03
sample55  -0.0164920835  5.919925e-05
sample56   0.0355115616  1.951043e-02
sample57  -0.0141146068  3.492409e-02
sample58  -0.0015636132  9.862883e-03
sample59  -0.0390656483  3.590929e-03
sample60  -0.0139454780  3.963030e-03
sample61  -0.0106410274  1.919705e-02
sample62   0.0236748439 -5.922677e-03
sample63  -0.0846790877  3.839102e-02
sample64  -0.1202581015  2.846469e-02
sample65   0.0050548584  6.328644e-03
sample66   0.0028013072  1.291807e-02
sample67  -0.1231623009 -2.112565e-02
sample68  -0.0437782161 -2.845072e-02
sample69  -0.0501199692  2.053469e-03
sample70  -0.0140278645 -2.027157e-02
sample71   0.0057489505 -4.085977e-02
sample72   0.0511212704 -5.522408e-03
sample73   0.0828141409  7.431582e-03
sample74  -0.0085959456  1.772951e-02
sample75  -0.0312180394 -6.636869e-03
sample76  -0.0519051781 -1.640191e-02
sample77  -0.0925924762 -6.907800e-03
sample78   0.1163971046  2.251122e-02
sample79  -0.0240906926 -4.887766e-02
sample80  -0.0221327065 -6.730703e-03
sample81  -0.0072114968 -2.254399e-02
sample82   0.1204416674 -9.907422e-03
sample83   0.0386739485 -1.171663e-03
sample84   0.0195988488 -1.033806e-02
sample85  -0.0877680171 -1.725057e-03
sample86   0.1023541048 -1.062501e-02
sample87   0.0425213089 -1.356865e-02
sample88   0.0244788514  1.180820e-02
sample89   0.0804276691  2.188588e-02
sample90  -0.0074639871 -6.140721e-03
sample91  -0.1278832404 -6.485140e-02
sample92  -0.0162199697 -5.048358e-02
sample93   0.0769344893 -3.045135e-03
sample94  -0.0104345587 -3.593172e-03
sample95  -0.0260058453 -2.330475e-02
sample96  -0.0025018700 -1.433516e-02
sample97  -0.0492358305  7.774183e-03
sample98   0.0279220220 -3.862141e-03
sample99   0.0813921923  3.487339e-02
sample100 -0.0428797405  7.112807e-03
sample101  0.0032855240 -4.940743e-02
sample102  0.0038439317  2.938008e-03
sample103  0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105  0.0314853405 -4.656633e-03
sample106  0.0726456731  1.192390e-02
sample107  0.0807342975  7.508627e-03
sample108  0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110  0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112  0.0599954082  8.820317e-03
sample113 -0.0195006577  1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131  6.937326e-03
sample121  0.0613899126  2.915307e-02
sample122  0.0218424338  2.241775e-02
sample123  0.0809008460 -1.051759e-02
sample124 -0.0472109313  1.239887e-02
sample125  0.0583180947  2.521167e-03
sample126 -0.0753941872  2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128  0.1000212216  2.908091e-02
sample129  0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877  4.091672e-03
sample132 -0.0566219024  2.179861e-02
sample133  0.0384172955 -1.372840e-02
sample134 -0.1280862736  2.077912e-02
sample135  0.0592633273  6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861  6.729304e-03
sample140  0.0468926306 -1.285498e-02
sample141 -0.0186248693  1.605439e-02
sample142  0.0328031246  9.887746e-03
sample143  0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310  3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147  0.0765025670 -7.714769e-03
sample148 -0.0276016641  2.420589e-02
sample149  0.0027545308 -4.653007e-02
sample150 -0.0792296010  1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152  0.0409796547 -1.907063e-02
sample153  0.0734301757  5.528780e-02
sample154  0.0557740684 -2.487723e-02
sample155  0.0689436560 -6.127635e-03
sample156  0.0212272938 -9.747423e-03
sample157  0.0911931194  6.355708e-03
sample158  0.0220840645 -3.016357e-03
sample159 -0.0513244242  1.304175e-02
sample160  0.0246213576  1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162  0.1078802043  4.337260e-02
sample163 -0.1017965082  5.047171e-02
sample164  0.0119430799  9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180  1.899222e-02
sample167 -0.0872832229  1.516582e-04
sample168  0.0540714512  1.397701e-02
sample169  0.0328432652  7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
                   [,1]          [,2]
sample1    0.0133684846  2.195848e-02
sample2    0.0254157197 -1.058416e-02
sample3   -0.0049551479 -4.840017e-03
sample4    0.0310390570 -1.063929e-02
sample5    0.0046941318 -6.488426e-03
sample6   -0.0107406753 -1.026702e-02
sample7   -0.0225157631  2.624712e-04
sample8    0.0141320952 -9.505821e-03
sample9    0.0029681280  2.078210e-02
sample10   0.0131729174 -2.275042e-03
sample11  -0.0004164298  1.994019e-02
sample12  -0.0095211620  3.759883e-02
sample13   0.0091018604 -7.953956e-03
sample14  -0.0106557524 -9.181659e-03
sample15  -0.0249924121  3.262724e-02
sample16  -0.0156216400  1.375700e-02
sample17  -0.0019382446  1.073994e-03
sample18  -0.0221072481 -8.703592e-03
sample19   0.0146917619 -1.311712e-02
sample20  -0.0160353760  1.826290e-02
sample21   0.0035947899 -9.616341e-03
sample22  -0.0225060762 -2.532589e-03
sample23   0.0310000683  3.033060e-03
sample24   0.0499544372  1.809450e-02
sample25   0.0284442301 -1.932558e-02
sample26   0.0188220043  2.146985e-02
sample27  -0.0257763219 -1.999228e-03
sample28   0.0120888648  1.125834e-02
sample29  -0.0236482520  4.426726e-02
sample30  -0.0385486305 -2.055935e-02
sample31  -0.0181539336 -5.877838e-03
sample32  -0.0302630460 -2.607192e-03
sample33  -0.0319565715 -1.562628e-02
sample34  -0.0197970124  9.906813e-03
sample35  -0.0247412713 -5.434440e-03
sample36  -0.0386259060 -3.190394e-02
sample37  -0.0566199273 -4.192574e-02
sample38  -0.0142060273  2.259644e-02
sample39   0.0053589035  1.076485e-02
sample40  -0.0552546493 -3.819896e-02
sample41  -0.0013089975  9.278818e-05
sample42   0.0137252142 -1.664652e-02
sample43  -0.0151259626 -6.290953e-03
sample44   0.0617391754 -1.442883e-02
sample45   0.0231410886  1.163143e-03
sample46  -0.0148898209 -1.384176e-04
sample47  -0.0187252536  1.221690e-02
sample48   0.0432839432  1.416671e-02
sample49   0.0160818605 -3.588745e-02
sample50   0.0059333545  4.067003e-02
sample51  -0.0142914866  7.776270e-03
sample52  -0.0086339952  7.208917e-03
sample53  -0.0207386980  6.272432e-03
sample54  -0.0039856719 -1.316934e-02
sample55  -0.0056217017  5.692315e-03
sample56   0.0000123292  8.978290e-04
sample57  -0.0095805555  1.324253e-02
sample58  -0.0124160295 -7.326376e-03
sample59  -0.0400195442 -1.349736e-02
sample60  -0.0460063358  2.770091e-02
sample61  -0.0245266456  1.470710e-02
sample62  -0.0366022783 -3.437352e-03
sample63   0.0013742171  3.288796e-02
sample64  -0.0070599859  2.739588e-02
sample65   0.0041201911  1.498268e-02
sample66   0.0143173351 -1.968812e-02
sample67  -0.0467477531 -1.929938e-02
sample68  -0.0306751978 -1.436184e-02
sample69  -0.0125317217  4.130407e-03
sample70  -0.0068071487  8.080857e-03
sample71   0.0169170264 -7.027348e-03
sample72  -0.0346909749 -1.333770e-02
sample73  -0.0280506153  1.493843e-02
sample74  -0.0182611498  3.294697e-03
sample75  -0.0120563964  8.974612e-03
sample76   0.0001437236 -4.253184e-02
sample77   0.0065330299 -5.252886e-02
sample78   0.0288278141 -1.127782e-02
sample79   0.0503961481 -1.023318e-02
sample80  -0.0207693429  3.648391e-02
sample81   0.0163562768 -9.074596e-03
sample82  -0.0084317129 -1.478976e-02
sample83  -0.0474097918 -1.103126e-02
sample84   0.0177181395 -7.191197e-03
sample85  -0.0342718548 -3.082360e-02
sample86  -0.0261671791 -1.089491e-02
sample87  -0.0009486358 -2.411514e-02
sample88   0.0020528931 -2.894615e-02
sample89  -0.0189361111 -2.638639e-03
sample90  -0.0009863658 -2.390075e-02
sample91  -0.0124352695  8.153234e-02
sample92   0.0564264106 -8.909537e-03
sample93  -0.0081461774  1.570851e-02
sample94  -0.0054896581  1.547251e-02
sample95   0.0224073150 -4.374348e-04
sample96   0.0173528924 -3.050441e-03
sample97   0.0067948115  5.008237e-03
sample98  -0.0116030825  1.498764e-02
sample99   0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101  0.0124923691  3.091503e-02
sample102  0.0650835386 -1.367400e-02
sample103 -0.0042741828  7.855985e-03
sample104  0.0250591040 -4.171938e-03
sample105  0.0157516368 -3.121990e-02
sample106  0.0060593853 -5.101693e-03
sample107 -0.0098329626  1.044506e-02
sample108  0.0044269853  4.142036e-03
sample109  0.0572473486  1.517542e-02
sample110  0.0090474827 -5.119868e-03
sample111  0.0444263015  7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113  0.0241047399  6.706740e-03
sample114  0.0074558775 -4.728652e-03
sample115  0.0611851433  1.117210e-02
sample116  0.0432646951 -1.380556e-02
sample117  0.0516750066 -3.575617e-02
sample118  0.0139942100 -3.279138e-03
sample119  0.0291722987  5.587946e-02
sample120  0.0103515853 -1.690016e-03
sample121 -0.0091396331  3.552116e-02
sample122  0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124  0.0283466326  3.127845e-03
sample125  0.0016472378 -2.770692e-02
sample126 -0.0286529417  3.489336e-02
sample127 -0.0010224500  7.483214e-03
sample128  0.0209049296  2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968  4.637390e-03
sample132  0.0198526786  5.723983e-04
sample133  0.0088812957 -9.988115e-03
sample134 -0.0137484514  1.172591e-02
sample135 -0.0220314568  1.347465e-02
sample136 -0.0185173353  5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138  0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762  2.456863e-02
sample141  0.0058369763 -1.420854e-02
sample142  0.0207886071 -1.188764e-02
sample143  0.0092832598 -1.324238e-02
sample144  0.0028442140  3.627979e-03
sample145  0.0199749569  2.862202e-03
sample146 -0.0182236697  1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148  0.0065435868 -1.572917e-02
sample149  0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151  0.0245166984 -6.888241e-03
sample152  0.0107259913  3.314630e-02
sample153  0.0550963965  3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574  2.642246e-03
sample156 -0.0117803505  2.698265e-02
sample157 -0.0096167165  1.433840e-02
sample158 -0.0101754772  9.137620e-03
sample159  0.0120662931 -2.565236e-02
sample160 -0.0132238202  2.916023e-03
sample161  0.0274491966 -1.748284e-02
sample162  0.0012482909  3.152261e-02
sample163  0.0042031315  1.830701e-02
sample164  0.0174896157 -1.175915e-02
sample165  0.0097517662 -6.119019e-03
sample166  0.0190134679 -1.121582e-02
sample167 -0.0044140836  4.665585e-03
sample168  0.0049689168 -1.941822e-02
sample169 -0.0209802098  3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
                   [,1]          [,2]
sample1   -0.0515543627 -0.0305856787
sample2   -0.0144993256  0.0236342950
sample3   -0.0371833108 -0.0140263348
sample4    0.0068945388 -0.0132539692
sample5    0.0215035333 -0.0663338101
sample6   -0.0187055152  0.0088773016
sample7   -0.0061521552  0.0064029054
sample8   -0.0210874459  0.0334652901
sample9    0.0516865043 -0.0291142799
sample10   0.0059440366 -0.0527217447
sample11   0.0393010793 -0.0200624712
sample12  -0.0420837100  0.0131331362
sample13   0.0333252565  0.0818552509
sample14  -0.0190062644  0.0160202175
sample15  -0.0030968049 -0.0189230681
sample16  -0.0004452158  0.0018880102
sample17  -0.0185848615  0.0240170131
sample18  -0.0273093598  0.0230213640
sample19  -0.0217761111 -0.0445894441
sample20   0.0245820821  0.0159812738
sample21   0.0034527644 -0.0400016054
sample22  -0.0340789054  0.0039289109
sample23  -0.0010344929 -0.0310161212
sample24   0.0289468503  0.0760962436
sample25  -0.0119098496 -0.0122798760
sample26  -0.0181001057  0.0517892852
sample27   0.0050465417 -0.0086515844
sample28   0.0057491502  0.0358830107
sample29  -0.0051104246  0.0116605117
sample30  -0.0103085904  0.0039678538
sample31  -0.0319929858  0.0090606113
sample32  -0.0036232521 -0.0328202010
sample33  -0.0534742153  0.0024751837
sample34  -0.0067495749 -0.0111000311
sample35   0.0378745721  0.0465929296
sample36   0.0647886800  0.0359987924
sample37   0.0488441236  0.0492906912
sample38  -0.0251514062  0.0197110110
sample39  -0.0085428066 -0.0105117852
sample40   0.0379324087  0.0440810741
sample41  -0.0044199152 -0.0128820644
sample42  -0.0292553573 -0.0067045265
sample43  -0.0077829155 -0.0510178219
sample44   0.0045122248  0.0479660309
sample45  -0.0074444298 -0.0051116726
sample46  -0.0088025512  0.0196186661
sample47   0.0076696301  0.0215947965
sample48   0.0290108585 -0.0175568376
sample49  -0.0141754858  0.0184717099
sample50   0.0006282201 -0.0233054373
sample51   0.0441995177 -0.0410022921
sample52   0.0715329391 -0.0399499475
sample53  -0.0095954087 -0.0029140909
sample54   0.0048933768 -0.0281884386
sample55   0.0327325487 -0.0532290012
sample56   0.0323068984 -0.0256595538
sample57   0.0806603122 -0.0286748097
sample58  -0.0064792049 -0.0006945349
sample59   0.0088958941  0.0067389649
sample60   0.0874124612  0.0431964341
sample61   0.0577604571 -0.0326112099
sample62  -0.0313318464  0.0224391756
sample63  -0.0233625220  0.0125110562
sample64  -0.0086426068  0.0148770341
sample65   0.0025256193 -0.0404466327
sample66   0.0006014071 -0.0471576264
sample67   0.0706087042  0.0516228406
sample68   0.0082301011  0.0033109509
sample69  -0.0475076743  0.0001452708
sample70  -0.0600773716  0.0089986962
sample71  -0.0096321627 -0.0050761187
sample72  -0.0031773546 -0.0166221542
sample73  -0.0113700517 -0.0191726684
sample74  -0.0014179662 -0.0608101325
sample75   0.0041911740 -0.0399981269
sample76  -0.0055326449  0.0353114263
sample77  -0.0260214459  0.0305731380
sample78  -0.0119267436  0.0632236007
sample79   0.0186017239  0.0027402910
sample80   0.0241047889 -0.0472697181
sample81  -0.0220288317 -0.0079577210
sample82  -0.0180751258  0.0639051029
sample83  -0.0256671713 -0.0125898269
sample84   0.0161392598 -0.0567222449
sample85   0.0139988188  0.0322763454
sample86  -0.0198382995  0.0389225776
sample87   0.0266270281 -0.0032979996
sample88   0.0515677078  0.0117902495
sample89   0.0014022125 -0.0140510488
sample90  -0.0375949749  0.0044004551
sample91   0.0310397965  0.0440610926
sample92   0.0270570567  0.0324380452
sample93  -0.0215009202  0.0063993941
sample94  -0.0415702912 -0.0037692077
sample95  -0.0168416047  0.0010019120
sample96  -0.0285582661 -0.0187991000
sample97  -0.0490843868 -0.0266760748
sample98  -0.0171579033 -0.0112897471
sample99  -0.0271316525  0.0232395583
sample100 -0.0301789816  0.0305498693
sample101 -0.0264371151  0.0170723968
sample102  0.0012767734 -0.0248949597
sample103  0.0055214687 -0.0030040587
sample104  0.0251346074 -0.0165212671
sample105  0.0062424215 -0.0400309901
sample106  0.0069768684  0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679  0.0023637162
sample109 -0.0014762845  0.0165583675
sample110  0.0036971063  0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112  0.0046098120 -0.0048009350
sample113  0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453  0.0135805368
sample116  0.0183575759  0.0665377581
sample117  0.0227640036 -0.0012287760
sample118  0.0015695248  0.0472617382
sample119  0.0190084932  0.0590034062
sample120 -0.0449645755  0.0072755697
sample121  0.0077307184  0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123  0.0016959300  0.0028593594
sample124 -0.0365091615  0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408  0.0019165544
sample127 -0.0494064872  0.0088209044
sample128 -0.0155454766  0.0186819802
sample129 -0.0184340400  0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422  0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133  0.0204029276 -0.0282209049
sample134  0.0175513332  0.0262883962
sample135  0.0029009925  0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323  0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139  0.0073103935  0.0308956174
sample140  0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142  0.0268670676 -0.0239229634
sample143  0.0421049126 -0.0110888235
sample144  0.0017253664 -0.0341766012
sample145  0.0681741320 -0.0073526377
sample146 -0.0239965222  0.0118396767
sample147 -0.0063453522  0.0183130585
sample148  0.0230825251 -0.0379753037
sample149  0.0223298673  0.0188909118
sample150  0.0055709108  0.0174179009
sample151  0.0039177786 -0.0233533275
sample152  0.0134325667  0.0302344591
sample153  0.0511990309  0.0730230140
sample154  0.0006698324  0.0154177486
sample155  0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599  0.0154934573
sample158  0.0201775524 -0.0332982124
sample159 -0.0086909001  0.0073496711
sample160  0.0295437331 -0.0555734536
sample161  0.0332754288  0.0033779619
sample162  0.0121954537  0.0433540412
sample163 -0.0173490933  0.0227219128
sample164  0.0143374783 -0.0453542590
sample165  0.0343612593 -0.0511194536
sample166 -0.0157536004  0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919  0.0060747155
sample169  0.0116231468 -0.0015112800
> 
> ## 3.3 Plotting VAF
> 
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
> 
> # JIVE plotVAF
> plotVAF(jiveRes)
> 
> 
> #########################
> ## PART 4. Plot Results
> 
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+              combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+              background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+              axisSize=NULL,titleSize=NULL)
> 
> # Scores for common part. JIVE
> plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common",
+              combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+              background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+              axisSize=NULL,titleSize=NULL)
> 
> # Scores for common part. O2PLS.
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for common part. O2PLS.
> plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common",
+              combined=TRUE,block=NULL,color="classname",shape=NULL,
+              labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+              labelSize=NULL,axisSize=NULL,titleSize=NULL)
> 
> 
> # Scores for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for distinctive part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual",
+              combined=TRUE,block=NULL,color="classname",shape=NULL,
+              labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+              labelSize=NULL,axisSize=NULL,titleSize=NULL)
> 
> # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block)
> p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for common and distinctive part. DISCO  (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Loadings for common part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> # Loadings for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> # Combined plot for loadings from common and distinctive part  (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> 
> ## Plot scores and loadings togheter: Common components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+         combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+         background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+         axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> ## Plot scores and loadings togheter:  Common components O2PLS
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> ## Plot scores and loadings togheter: Distintive components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
   8.26    0.28    8.53 

Example timings

STATegRa.Rcheck/examples_i386/STATegRa-Ex.timings

nameusersystemelapsed
STATegRaUsersGuide000
STATegRa_data0.200.020.22
STATegRa_data_TCGA_BRCA000
bioDist0.330.030.36
bioDistFeature0.280.000.28
bioDistFeaturePlot0.250.030.28
bioDistW0.270.000.27
bioDistWPlot0.260.020.28
bioMap000
combiningMappings000
createOmicsExpressionSet0.100.030.13
getInitialData0.420.060.48
getLoadings0.410.110.52
getMethodInfo0.430.130.56
getPreprocessing0.680.220.89
getScores0.420.090.52
getVAF0.500.030.53
holistOmics0.000.020.01
modelSelection1.260.431.71
omicsCompAnalysis2.580.052.62
omicsNPC000
plotRes3.030.103.13
plotVAF2.670.042.72

STATegRa.Rcheck/examples_x64/STATegRa-Ex.timings

nameusersystemelapsed
STATegRaUsersGuide000
STATegRa_data0.160.010.17
STATegRa_data_TCGA_BRCA000
bioDist0.330.000.32
bioDistFeature0.240.020.25
bioDistFeaturePlot0.250.000.25
bioDistW0.230.020.25
bioDistWPlot0.250.000.25
bioMap0.000.010.02
combiningMappings000
createOmicsExpressionSet0.110.000.11
getInitialData0.390.130.51
getLoadings0.480.070.57
getMethodInfo0.500.070.56
getPreprocessing0.710.180.89
getScores0.430.100.53
getVAF0.520.040.56
holistOmics000
modelSelection1.060.521.58
omicsCompAnalysis2.860.082.94
omicsNPC000
plotRes3.390.013.41
plotVAF2.960.022.96