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This page was generated on 2022-01-14 13:07:15 -0500 (Fri, 14 Jan 2022).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 20.04.4 LTS)x86_644.1.2 (2021-11-01) -- "Bird Hippie" 4327
tokay2Windows Server 2012 R2 Standardx644.1.2 (2021-11-01) -- "Bird Hippie" 4076
machv2macOS 10.14.6 Mojavex86_644.1.2 (2021-11-01) -- "Bird Hippie" 4137
Click on any hostname to see more info about the system (e.g. compilers)      (*) as reported by 'uname -p', except on Windows and Mac OS X

CHECK results for STATegRa on tokay2


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.
- Make sure to use the following settings in order to reproduce any error or warning you see on this page.

raw results

Package 1887/2083HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
STATegRa 1.30.0  (landing page)
David Gomez-Cabrero , NĂºria Planell
Snapshot Date: 2022-01-13 01:55:04 -0500 (Thu, 13 Jan 2022)
git_url: https://git.bioconductor.org/packages/STATegRa
git_branch: RELEASE_3_14
git_last_commit: 9124807
git_last_commit_date: 2021-10-26 12:17:04 -0500 (Tue, 26 Oct 2021)
nebbiolo2Linux (Ubuntu 20.04.4 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
tokay2Windows Server 2012 R2 Standard / x64  OK    OK    OK    OK  UNNEEDED, same version is already published
machv2macOS 10.14.6 Mojave / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published

Summary

Package: STATegRa
Version: 1.30.0
Command: C:\Users\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=C:\Users\biocbuild\bbs-3.14-bioc\R\library --no-vignettes --timings STATegRa_1.30.0.tar.gz
StartedAt: 2022-01-14 03:00:37 -0500 (Fri, 14 Jan 2022)
EndedAt: 2022-01-14 03:06:41 -0500 (Fri, 14 Jan 2022)
EllapsedTime: 364.1 seconds
RetCode: 0
Status:   OK  
CheckDir: STATegRa.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\Users\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=C:\Users\biocbuild\bbs-3.14-bioc\R\library --no-vignettes --timings STATegRa_1.30.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory 'C:/Users/biocbuild/bbs-3.14-bioc/meat/STATegRa.Rcheck'
* using R version 4.1.2 (2021-11-01)
* 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 '1.30.0'
* 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
  'C:/Users/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_1.30.0.tar.gz && rm -rf STATegRa.buildbin-libdir && mkdir STATegRa.buildbin-libdir && C:\Users\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=STATegRa.buildbin-libdir STATegRa_1.30.0.tar.gz && C:\Users\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD INSTALL STATegRa_1.30.0.zip && rm STATegRa_1.30.0.tar.gz STATegRa_1.30.0.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
 14 3177k   14  470k    0     0  2022k      0  0:00:01 --:--:--  0:00:01 2019k
100 3177k  100 3177k    0     0  4378k      0 --:--:-- --:--:-- --:--:-- 4376k

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_1.30.0.zip
* DONE (STATegRa)
* installing to library 'C:/Users/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 4.1.2 (2021-11-01) -- "Bird Hippie"
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 -- Fri Jan 14 03:04:40 2022 
*********************************************** 
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: In rownames(pData) == colnames(exprs) :
  longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
> 
> proc.time()
   user  system elapsed 
   3.31    0.28    3.57 

STATegRa.Rcheck/tests_x64/runTests.Rout


R version 4.1.2 (2021-11-01) -- "Bird Hippie"
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 -- Fri Jan 14 03:06:33 2022 
*********************************************** 
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: In rownames(pData) == colnames(exprs) :
  longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
> 
> proc.time()
   user  system elapsed 
   4.03    0.32    4.34 

STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsCLUST.Rout


R version 4.1.2 (2021-11-01) -- "Bird Hippie"
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 
  24.64    0.85   25.48 

STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsCLUST.Rout


R version 4.1.2 (2021-11-01) -- "Bird Hippie"
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 
  24.95    0.90   25.82 

STATegRa.Rcheck/tests_i386/STATegRa_Example.omicsNPC.Rout


R version 4.1.2 (2021-11-01) -- "Bird Hippie"
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 
  74.17    0.42   74.56 

STATegRa.Rcheck/tests_x64/STATegRa_Example.omicsNPC.Rout


R version 4.1.2 (2021-11-01) -- "Bird Hippie"
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 
  70.07    0.18   70.25 

STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsPCA.Rout


R version 4.1.2 (2021-11-01) -- "Bird Hippie"
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    0.0781575694 -0.0431551357
sample2   -0.1192221531  0.0294017813
sample3   -0.0531408504 -0.0746837477
sample4    0.0292971607 -0.0006038418
sample5    0.0202090695  0.0110454757
sample6    0.1226088415  0.1053494746
sample7    0.1078931510 -0.0322415682
sample8    0.1782890922  0.1449328397
sample9    0.0468697243 -0.0455171544
sample10  -0.0036032803  0.0420075779
sample11  -0.0035566360 -0.0566284653
sample12   0.1006129722  0.0641394854
sample13  -0.1174413043  0.0907474616
sample14   0.0981203574  0.0617765112
sample15   0.0085337416 -0.0086952633
sample16   0.0783146720  0.1581335901
sample17  -0.1483610705  0.0638580018
sample18  -0.0963084296  0.0556690864
sample19  -0.0217243019 -0.0720131213
sample20  -0.0635633802 -0.0779607577
sample21  -0.0201844188  0.1566381421
sample22   0.0218274114 -0.0764052815
sample23   0.0852038889 -0.0032769707
sample24  -0.1287182079  0.1924418986
sample25  -0.0430575720 -0.0456643124
sample26  -0.1453899938  0.0541455536
sample27  -0.0197483338 -0.1185588884
sample28  -0.1025339589  0.0650653483
sample29   0.0706022669 -0.0682929477
sample30  -0.1295622739 -0.0066671957
sample31   0.1147449321  0.1232729791
sample32  -0.0374308071  0.0380254046
sample33   0.0599521022  0.0136940425
sample34  -0.0984199226  0.0375367339
sample35  -0.0543096314 -0.0378030765
sample36   0.1403628187 -0.0343631645
sample37   0.0228947988 -0.0732679828
sample38  -0.0222072801 -0.0962565491
sample39  -0.0941739258  0.0215179379
sample40   0.0643807395 -0.0687710420
sample41  -0.0327634814 -0.1232187146
sample42  -0.0500431622 -0.0292516321
sample43  -0.0184497095  0.0233046480
sample44   0.1487888846  0.1171201117
sample45  -0.1050779040  0.1123136541
sample46  -0.1151191377 -0.1093993697
sample47  -0.0962591408 -0.0288415155
sample48   0.0004832362 -0.0310385774
sample49   0.1135203665  0.1213934300
sample50  -0.0123549753 -0.1740764142
sample51   0.0550527310  0.1258933569
sample52   0.0499118341  0.0728583256
sample53   0.1119772615  0.1588067478
sample54  -0.0360055719  0.0228586262
sample55   0.0210418820  0.0006751928
sample56  -0.0434171617  0.0633131598
sample57   0.0197820482  0.1150756515
sample58   0.0030440749  0.0326129425
sample59   0.0500256938  0.0129528124
sample60   0.0184280166  0.0136226500
sample61   0.0150298896  0.0635101447
sample62  -0.0304758473 -0.0201230859
sample63   0.1102249994  0.1285967847
sample64   0.1552586762  0.0971186326
sample65  -0.0058503860  0.0207101940
sample66  -0.0025607586  0.0424282329
sample67   0.1546638881 -0.0661569460
sample68   0.0536374524 -0.0923599057
sample69   0.0640333140  0.0082005183
sample70   0.0163521953 -0.0663227015
sample71  -0.0102536036 -0.1345967696
sample72  -0.0654191521 -0.0196030935
sample73  -0.1048553086  0.0221003972
sample74   0.0123800560  0.0586159535
sample75   0.0392079835 -0.0209724342
sample76   0.0648954641 -0.0524759182
sample77   0.1172922678 -0.0201201235
sample78  -0.1463072867  0.0708394930
sample79   0.0265208707 -0.1603432888
sample80   0.0279739329 -0.0214150052
sample81   0.0079212185 -0.0738498338
sample82  -0.1544234494 -0.0361449491
sample83  -0.0494205088 -0.0049931945
sample84  -0.0259039800 -0.0346594241
sample85   0.1116487614 -0.0031398248
sample86  -0.1306478836 -0.0377152220
sample87  -0.0554777844 -0.0459739054
sample88  -0.0301626673  0.0382207288
sample89  -0.1016866159  0.0694081057
sample90   0.0086821738 -0.0201324054
sample91   0.1578630017 -0.2097789887
sample92   0.0170933326 -0.1655944606
sample93  -0.0979805005 -0.0121499713
sample94   0.0131486334 -0.0114929288
sample95   0.0315682452 -0.0758920806
sample96   0.0024125852 -0.0470188321
sample97   0.0634545828  0.0270301922
sample98  -0.0359372441 -0.0135465105
sample99  -0.1009167848  0.1124708406
sample100  0.0551754148  0.0246502916
sample101 -0.0080115836 -0.1627409664
sample102 -0.0046451560  0.0095462975
sample103 -0.0472520742 -0.0940382908
sample104  0.0198157345 -0.0591150681
sample105 -0.0400239056 -0.0160951441
sample106 -0.0923810233  0.0369002993
sample107 -0.1019372282  0.0224968128
sample108 -0.0877091506 -0.0128850695
sample109  0.0864820052 -0.0901089658
sample110 -0.1223116530 -0.0096109932
sample111  0.0257352313 -0.0936288128
sample112 -0.0765285883  0.0270381331
sample113  0.0258799663  0.0377434750
sample114  0.0021141212 -0.0882041719
sample115  0.0303455023 -0.0723744504
sample116  0.0780504245 -0.0685168291
sample117  0.0536893847 -0.0912032645
sample118  0.0666649824 -0.0236262936
sample119  0.1021872623 -0.2325007903
sample120  0.0750216320  0.0243343578
sample121 -0.0756937964  0.0942971676
sample122 -0.0259632281  0.0731917114
sample123 -0.1037844600 -0.0369177604
sample124  0.0611205020  0.0421642339
sample125 -0.0738472615  0.0066943897
sample126  0.0972919280  0.0762702505
sample127  0.0824699537 -0.0096645127
sample128 -0.1249411737  0.0929250190
sample129 -0.0734063485 -0.0434310564
sample130 -0.0003500157 -0.0309857492
sample131  0.0930184116  0.0155972132
sample132  0.0736220485  0.0732968592
sample133 -0.0498398380 -0.0462457084
sample134  0.1644872581  0.0720049497
sample135 -0.0752294926  0.0003872802
sample136  0.0227150202 -0.0495467169
sample137  0.0564721944 -0.0288857214
sample138  0.0255986397 -0.0610935090
sample139  0.0621218832  0.0235860851
sample140 -0.0604148747 -0.0435528598
sample141  0.0246742993  0.0532629197
sample142 -0.0409564062  0.0316231461
sample143 -0.0077356454 -0.0476909619
sample144  0.0173241014 -0.0156786353
sample145  0.0485467336  0.1202735917
sample146  0.0419650220 -0.0811238439
sample147 -0.0977304447 -0.0274767076
sample148  0.0368253126  0.0803968880
sample149 -0.0072864825 -0.1533018916
sample150  0.1020825525  0.0624825491
sample151  0.0305397058 -0.0289340644
sample152 -0.0533595232 -0.0638336614
sample153 -0.0891640081  0.1799445528
sample154 -0.0727554230 -0.0834127489
sample155 -0.0880665670 -0.0220767353
sample156 -0.0276558727 -0.0326600367
sample157 -0.1155031528  0.0183636707
sample158 -0.0281506655 -0.0104910309
sample159  0.0663233671  0.0443808028
sample160 -0.0302644014  0.0404303530
sample161  0.0114712812 -0.0591086663
sample162 -0.1337091215  0.1398131180
sample163  0.1330120546  0.1688768596
sample164 -0.0150338258  0.0028372889
sample165  0.0076518759 -0.0164146901
sample166  0.0367791337  0.0630611293
sample167  0.1111989871  0.0030066971
sample168 -0.0672983054  0.0446265950
sample169 -0.0413003563  0.0224450304
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
                      1             2
sample1    0.0420460525  0.0867866245
sample2    0.0820850525 -0.0410968427
sample3   -0.0155968800 -0.0195186514
sample4    0.1001342989 -0.0410775932
sample5    0.0153480242 -0.0253257649
sample6   -0.0340235722 -0.0408223384
sample7   -0.0722603283  0.0002323566
sample8    0.0457624149 -0.0370006543
sample9    0.0086215419  0.0820184474
sample10   0.0423632462 -0.0083917405
sample11  -0.0022595205  0.0787764032
sample12  -0.0322074997  0.1479823336
sample13   0.0293973373 -0.0306742653
sample14  -0.0337428877 -0.0367508427
sample15  -0.0815561024  0.1275613476
sample16  -0.0508327313  0.0540604322
sample17  -0.0062553608  0.0041024917
sample18  -0.0705599348 -0.0351053600
sample19   0.0476781427 -0.0509595319
sample20  -0.0523029653  0.0715513736
sample21   0.0119255863 -0.0376086841
sample22  -0.0724460535 -0.0095635326
sample23   0.0992529435  0.0134299403
sample24   0.1595271302  0.0728685157
sample25   0.0920659815 -0.0749748911
sample26   0.0595568077  0.0848973993
sample27  -0.0826580616 -0.0086748076
sample28   0.0384835411  0.0440972980
sample29  -0.0777743599  0.1735298071
sample30  -0.1229474461 -0.0819019046
sample31  -0.0579746801 -0.0238646958
sample32  -0.0970364686 -0.0111435530
sample33  -0.1017579816 -0.0630453030
sample34  -0.0637901875  0.0377935967
sample35  -0.0790004112 -0.0229732899
sample36  -0.1224932823 -0.1274968951
sample37  -0.1798848494 -0.1673448957
sample38  -0.0466397354  0.0888152836
sample39   0.0168694988  0.0421536145
sample40  -0.1756418956 -0.1526663256
sample41  -0.0042473174  0.0004924384
sample42   0.0447824567 -0.0651501220
sample43  -0.0482291384 -0.0253533728
sample44   0.1986823508 -0.0545752698
sample45   0.0741921254  0.0054714697
sample46  -0.0478865594 -0.0007080753
sample47  -0.0608217689  0.0481615118
sample48   0.1381463790  0.0578301613
sample49   0.0530635198 -0.1405523184
sample50   0.0173640609  0.1602385971
sample51  -0.0462452588  0.0303472988
sample52  -0.0279993350  0.0280387834
sample53  -0.0667493949  0.0237700049
sample54  -0.0121810853 -0.0521354932
sample55  -0.0182392074  0.0221326550
sample56   0.0001310962  0.0030909377
sample57  -0.0316570871  0.0530190655
sample58  -0.0393889968 -0.0297801930
sample59  -0.1278270638 -0.0546541172
sample60  -0.1486963804  0.1069141178
sample61  -0.0793065403  0.0569790124
sample62  -0.1172822687 -0.0149211750
sample63   0.0028815970  0.1300524323
sample64  -0.0237293618  0.1073288416
sample65   0.0126543971  0.0589810445
sample66   0.0468235368 -0.0771066333
sample67  -0.1494286771 -0.0769878175
sample68  -0.0978025884 -0.0577364407
sample69  -0.0403090612  0.0156038076
sample70  -0.0221600546  0.0315436428
sample71   0.0546326041 -0.0272394892
sample72  -0.1107501541 -0.0537331931
sample73  -0.0906756506  0.0579957482
sample74  -0.0586511811  0.0121417263
sample75  -0.0390513397  0.0349278002
sample76   0.0022938757 -0.1676560157
sample77   0.0232101666 -0.2067300859
sample78   0.0929811913 -0.0434927424
sample79   0.1619376405 -0.0378101949
sample80  -0.0680393819  0.1424655573
sample81   0.0530723311 -0.0358347529
sample82  -0.0266851524 -0.0577449280
sample83  -0.1517242312 -0.0448570826
sample84   0.0570942294 -0.0273808243
sample85  -0.1086270824 -0.1228130939
sample86  -0.0833892853 -0.0442925263
sample87  -0.0022041621 -0.0943908583
sample88   0.0078278816 -0.1140504453
sample89  -0.0611003931 -0.0094589588
sample90  -0.0022942143 -0.0936254909
sample91  -0.0433780167  0.3205971653
sample92   0.1815213246 -0.0334666069
sample93  -0.0267654957  0.0614425654
sample94  -0.0181902393  0.0605088064
sample95   0.0720311962 -0.0013040338
sample96   0.0559671062 -0.0118786945
sample97   0.0217420991  0.0195417345
sample98  -0.0379200119  0.0588352558
sample99   0.0792511334 -0.0151261855
sample100 -0.0222099719 -0.0023323001
sample101  0.0387078671  0.1224225152
sample102  0.2094626471 -0.0516419877
sample103 -0.0138560931  0.0301047435
sample104  0.0807946518 -0.0162712099
sample105  0.0520491804 -0.1229660138
sample106  0.0192644123 -0.0185235030
sample107 -0.0319014268  0.0405120453
sample108  0.0140673525  0.0163422381
sample109  0.1831854106  0.0613024407
sample110  0.0292782319 -0.0199846366
sample111  0.1423170732  0.0327352614
sample112 -0.0426312504 -0.0029087219
sample113  0.0771933213  0.0268743151
sample114  0.0241565047 -0.0184080664
sample115  0.1958953756  0.0460149398
sample116  0.1394435871 -0.0530792944
sample117  0.1672309758 -0.1386521308
sample118  0.0448331091 -0.0117617809
sample119  0.0910185020  0.2217435861
sample120  0.0331405463 -0.0057270140
sample121 -0.0307514033  0.1392506185
sample122  0.0839840255 -0.0291983123
sample123 -0.0239676421 -0.0642167571
sample124  0.0909177520  0.0130430611
sample125  0.0065362901 -0.1092630982
sample126 -0.0935271540  0.1368276503
sample127 -0.0035406519  0.0292754982
sample128  0.0660352763  0.1018576222
sample129 -0.0693672647 -0.0695430647
sample130 -0.0008518230 -0.0669705437
sample131 -0.0431011382  0.0174060830
sample132  0.0637091332  0.0029383912
sample133  0.0289463182 -0.0390817233
sample134 -0.0446139119  0.0456332201
sample135 -0.0712344104  0.0521627261
sample136 -0.0596320772  0.0197291339
sample137 -0.0793176660 -0.0380637705
sample138  0.0973503520 -0.0454209753
sample139 -0.0539864211 -0.1534332327
sample140 -0.0850874314  0.0955803968
sample141  0.0192725920 -0.0554446304
sample142  0.0672295994 -0.0461312717
sample143  0.0303705993 -0.0519258467
sample144  0.0089349877  0.0145815389
sample145  0.0638882169  0.0122269204
sample146 -0.0585925872  0.0063074504
sample147 -0.0894147469 -0.1124626263
sample148  0.0216443221 -0.0615962189
sample149  0.0515311316 -0.0839902834
sample150 -0.0568226075 -0.0124472915
sample151  0.0789512737 -0.0261823611
sample152  0.0330690364  0.1306445061
sample153  0.1752071965  0.1497756585
sample154 -0.0421493009 -0.0037017439
sample155 -0.0680199873  0.0095703149
sample156 -0.0388952169  0.1057557717
sample157 -0.0314764877  0.0561364517
sample158 -0.0329630738  0.0353943445
sample159  0.0398463865 -0.1007368031
sample160 -0.0424904137  0.0108492905
sample161  0.0888338706 -0.0679692485
sample162  0.0027575593  0.1237848494
sample163  0.0126235627  0.0725441193
sample164  0.0566787575 -0.0458318019
sample165  0.0315331318 -0.0236359453
sample166  0.0612111824 -0.0425224446
sample167 -0.0142729543  0.0179306933
sample168  0.0169544990 -0.0769614729
sample169 -0.0675062540  0.0131498779
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
                      1             2
sample1   -0.0012331777 -1.635716e-01
sample2   -0.0724353392 -6.022177e-03
sample3   -0.0188459914 -1.080029e-01
sample4    0.0390143005  3.106109e-04
sample5    0.1774810571 -2.996431e-02
sample6   -0.0451446574 -3.455900e-02
sample7   -0.0226463326 -7.019158e-03
sample8   -0.1033684801 -9.858012e-03
sample9    0.1350014359  8.979115e-02
sample10   0.1259884258 -5.097942e-02
sample11   0.0979791069  7.086569e-02
sample12  -0.0863021089 -8.620322e-02
sample13  -0.1381401874  1.827998e-01
sample14  -0.0615074786 -2.642808e-02
sample15   0.0381600682 -3.101598e-02
sample16  -0.0048779542  1.270978e-03
sample17  -0.0788483355 -1.547609e-02
sample18  -0.0884189538 -3.795476e-02
sample19   0.0703043467 -1.084003e-01
sample20  -0.0025581116  7.975975e-02
sample21   0.0941596351 -4.126902e-02
sample22  -0.0550270715 -7.806610e-02
sample23   0.0679492676 -4.102081e-02
sample24  -0.1310969825  1.649281e-01
sample25   0.0113583466 -4.426902e-02
sample26  -0.1402949081  2.016455e-02
sample27   0.0261566328  1.590043e-03
sample28  -0.0724200904  5.850506e-02
sample29  -0.0330054550  2.062148e-03
sample30  -0.0228750204 -2.015340e-02
sample31  -0.0635070547 -6.670371e-02
sample32   0.0685100026 -4.955245e-02
sample33  -0.0777764908 -1.272070e-01
sample34   0.0157842068 -3.024311e-02
sample35  -0.0529627657  1.500982e-01
sample36   0.0070908392  2.025321e-01
sample37  -0.0442411301  1.802110e-01
sample38  -0.0781508208 -3.676292e-02
sample39   0.0120329966 -3.388886e-02
sample40  -0.0473283367  1.471583e-01
sample41   0.0228192350 -2.673453e-02
sample42  -0.0245361952 -7.960878e-02
sample43   0.1036361969 -8.229578e-02
sample44  -0.1012235126  7.049229e-02
sample45   0.0013726388 -2.451078e-02
sample46  -0.0558506247  2.948648e-03
sample47  -0.0380478574  4.554240e-02
sample48   0.0784340368  4.888887e-02
sample49  -0.0605168308 -1.162478e-02
sample50   0.0530083126 -2.737807e-02
sample51   0.1514645285  5.678256e-02
sample52   0.1860936055  1.246711e-01
sample53  -0.0064179853 -2.701064e-02
sample54   0.0697037518 -2.308414e-02
sample55   0.1633577744  1.366432e-02
sample56   0.1011483933  4.682130e-02
sample57   0.1730374415  1.609593e-01
sample58  -0.0071384899 -1.666951e-02
sample59  -0.0030458290  3.005380e-02
sample60   0.0215842594  2.665888e-01
sample61   0.1510585431  1.002384e-01
sample62  -0.0925531447 -4.845722e-02
sample63  -0.0596315646 -4.137113e-02
sample64  -0.0449227366 -2.600980e-03
sample65   0.0939382163 -4.406952e-02
sample66   0.1063397564 -5.710082e-02
sample67  -0.0201580289  2.361747e-01
sample68   0.0037208686  2.418551e-02
sample69  -0.0645162039 -1.155618e-01
sample70  -0.1013439730 -1.351780e-01
sample71  -0.0016466003 -2.976770e-02
sample72   0.0328895544 -2.835767e-02
sample73   0.0275080411 -5.148151e-02
sample74   0.1341718295 -7.895304e-02
sample75   0.0951576702 -3.943146e-02
sample76  -0.0864719833  3.035057e-02
sample77  -0.1035749502 -2.545324e-02
sample78  -0.1575648086  4.939469e-02
sample79   0.0189138447  4.874690e-02
sample80   0.1384142897  4.317780e-05
sample81  -0.0118846676 -6.357907e-02
sample82  -0.1675306551  3.533972e-02
sample83  -0.0065671023 -7.812492e-02
sample84   0.1486890605 -3.109098e-02
sample85  -0.0532720112  7.417994e-02
sample86  -0.1138474777 -1.815287e-05
sample87   0.0432866060  6.080501e-02
sample88   0.0433451241  1.402486e-01
sample89   0.0331204747 -1.395430e-02
sample90  -0.0607413514 -8.610384e-02
sample91  -0.0566263245  1.303771e-01
sample92  -0.0359580598  1.061605e-01
sample93  -0.0433646460 -4.443608e-02
sample94  -0.0477292173 -1.059570e-01
sample95  -0.0249595933 -3.980509e-02
sample96   0.0035217500 -9.293931e-02
sample97  -0.0066052185 -1.527234e-01
sample98   0.0020367080 -5.579513e-02
sample99  -0.0886622048 -3.728382e-02
sample100 -0.1091259628 -3.560400e-02
sample101 -0.0739723682 -4.317879e-02
sample102  0.0574455400 -2.784095e-02
sample103  0.0142733904  9.706394e-03
sample104  0.0710395602  4.068330e-02
sample105  0.0980829862 -3.452999e-02
sample106 -0.0254260561  3.628930e-02
sample107 -0.0160655127 -9.173398e-02
sample108 -0.0200988348 -2.379699e-02
sample109 -0.0389782015  1.692309e-02
sample110 -0.0326305274  2.988086e-02
sample111  0.0676935846 -6.038251e-02
sample112  0.0167883516  5.336923e-03
sample113  0.0969213783 -2.757708e-02
sample114 -0.0026397953 -9.209099e-02
sample115 -0.0308049694  1.603740e-02
sample116 -0.1240306353  1.272998e-01
sample117  0.0334728624  5.392660e-02
sample118 -0.1037152116  6.252440e-02
sample119 -0.1064170247  1.196218e-01
sample120 -0.0771357854 -1.004935e-01
sample121 -0.0129352402  3.181911e-02
sample122  0.0847487294 -5.568471e-02
sample123 -0.0041335447  7.693571e-03
sample124 -0.0583462438 -8.396480e-02
sample125  0.0634843177 -5.232569e-02
sample126 -0.0662582159 -1.091730e-01
sample127 -0.0865025670 -1.094172e-01
sample128 -0.0627822287 -1.471100e-02
sample129 -0.0336274484 -4.007770e-02
sample130 -0.0293518133 -8.046085e-02
sample131 -0.0469196744 -2.209366e-03
sample132 -0.0241745909 -1.248608e-01
sample133  0.0907303832  1.466698e-02
sample134 -0.0350841185  7.539660e-02
sample135  0.0001334965  9.185841e-03
sample136 -0.0335874734 -9.860177e-02
sample137 -0.0640147177 -7.554367e-02
sample138  0.0060964003 -1.742784e-02
sample139 -0.0592082668  5.615008e-02
sample140  0.0427988755 -1.099461e-02
sample141  0.0618793100 -9.301105e-02
sample142  0.0898552399  3.573319e-02
sample143  0.0817391166  8.880528e-02
sample144  0.0787754458 -3.821395e-02
sample145  0.1085819393  1.569460e-01
sample146 -0.0589554838 -4.373231e-02
sample147 -0.0495327771  7.278102e-03
sample148  0.1161590358  9.078088e-03
sample149 -0.0121575286  7.788466e-02
sample150 -0.0314511946  3.520220e-02
sample151  0.0575380877 -1.945394e-02
sample152 -0.0494540263  7.025567e-02
sample153 -0.0941338863  2.153268e-01
sample154 -0.0335928654  2.078830e-02
sample155  0.0690459123 -2.780359e-02
sample156  0.1039902343 -6.292486e-02
sample157 -0.0408645844  8.065530e-03
sample158  0.1018106400  7.817029e-03
sample159 -0.0281732639 -1.207263e-02
sample160  0.1643052856  2.977796e-03
sample161  0.0374330126  8.524588e-02
sample162 -0.0804538430  8.349629e-02
sample163 -0.0743232639 -1.406352e-02
sample164  0.1208804185 -2.139527e-02
sample165  0.1608115956  2.025157e-02
sample166 -0.0425948114 -2.660805e-02
sample167 -0.0226849507 -4.464257e-02
sample168 -0.0180737466 -7.471819e-04
sample169  0.0190780274  2.645428e-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 
   9.81    0.26   10.06 

STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsPCA.Rout


R version 4.1.2 (2021-11-01) -- "Bird Hippie"
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.

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    0.0781577679 -0.0431502026
sample2   -0.1192221667  0.0294078416
sample3   -0.0531411097 -0.0746839761
sample4    0.0292977141 -0.0005990225
sample5    0.0202099201  0.0110426612
sample6    0.1226085130  0.1053477142
sample7    0.1078927161 -0.0322456757
sample8    0.1782888738  0.1449371065
sample9    0.0468704656 -0.0455185813
sample10  -0.0036025144  0.0420078901
sample11  -0.0035561276 -0.0566297363
sample12   0.1006126389  0.0641419554
sample13  -0.1174417347  0.0907505786
sample14   0.0981199291  0.0617751291
sample15   0.0085337071 -0.0086989429
sample16   0.0783146174  0.1581312901
sample17  -0.1483614101  0.0638594499
sample18  -0.0963091425  0.0556661765
sample19  -0.0217239197 -0.0720116829
sample20  -0.0635635669 -0.0779632780
sample21  -0.0201838731  0.1566366781
sample22   0.0218267641 -0.0764084238
sample23   0.0852046225 -0.0032717223
sample24  -0.1287178636  0.1924547656
sample25  -0.0430572647 -0.0456596622
sample26  -0.1453902413  0.0541534057
sample27  -0.0197486615 -0.1185647148
sample28  -0.1025340132  0.0650696262
sample29   0.0706019392 -0.0682944900
sample30  -0.1295630041 -0.0066755089
sample31   0.1147444432  0.1232705488
sample32  -0.0374308996  0.0380181105
sample33   0.0599512190  0.0136889387
sample34  -0.0984200548  0.0375332744
sample35  -0.0543102245 -0.0378081752
sample36   0.1403622117 -0.0343742860
sample37   0.0228936321 -0.0732820804
sample38  -0.0222078138 -0.0962562770
sample39  -0.0941737426  0.0215196892
sample40   0.0643795885 -0.0687844168
sample41  -0.0327634935 -0.1232192444
sample42  -0.0500432046 -0.0292488568
sample43  -0.0184494708  0.0232996645
sample44   0.1487893011  0.1171331775
sample45  -0.1050775000  0.1123185809
sample46  -0.1151196786 -0.1094011955
sample47  -0.0962595258 -0.0288440028
sample48   0.0004842366 -0.0310308727
sample49   0.1135202364  0.1213957277
sample50  -0.0123546090 -0.1740736886
sample51   0.0550533760  0.1258875424
sample52   0.0499126902  0.0728525285
sample53   0.1119770904  0.1588031473
sample54  -0.0360053531  0.0228558166
sample55   0.0210425850  0.0006710460
sample56  -0.0434166334  0.0633109553
sample57   0.0197828993  0.1150700869
sample58   0.0030438611  0.0326102465
sample59   0.0500250937  0.0129437953
sample60   0.0184276925  0.0136130036
sample61   0.0150303888  0.0635024612
sample62  -0.0304768126 -0.0201285072
sample63   0.1102249759  0.1286003641
sample64   0.1552585676  0.0971196318
sample65  -0.0058498281  0.0207103074
sample66  -0.0025601459  0.0424281691
sample67   0.1546630862 -0.0661686247
sample68   0.0536369259 -0.0923671945
sample69   0.0640328421  0.0082001633
sample70   0.0163515884 -0.0663207831
sample71  -0.0102535212 -0.1345935617
sample72  -0.0654195536 -0.0196113109
sample73  -0.1048554941  0.0220954698
sample74   0.0123804622  0.0586102563
sample75   0.0392082704 -0.0209760218
sample76   0.0648948490 -0.0524769195
sample77   0.1172916266 -0.0201197990
sample78  -0.1463076024  0.0708475871
sample79   0.0265214892 -0.1603344329
sample80   0.0279744318 -0.0214198013
sample81   0.0079212741 -0.0738464686
sample82  -0.1544244168 -0.0361442819
sample83  -0.0494212561 -0.0050027677
sample84  -0.0259031224 -0.0346590424
sample85   0.1116479314 -0.0031480196
sample86  -0.1306488385 -0.0377187712
sample87  -0.0554777242 -0.0459767113
sample88  -0.0301625036  0.0382177505
sample89  -0.1016866844  0.0694036306
sample90   0.0086817382 -0.0201323093
sample91   0.1578628099 -0.2097764156
sample92   0.0170937978 -0.1655835492
sample93  -0.0979807614 -0.0121494777
sample94   0.0131483722 -0.0114915453
sample95   0.0315683658 -0.0758868826
sample96   0.0024127643 -0.0470150375
sample97   0.0634546478  0.0270328491
sample98  -0.0359373507 -0.0135476407
sample99  -0.1009168002  0.1124776272
sample100  0.0551748266  0.0246512096
sample101 -0.0080117501 -0.1627348784
sample102 -0.0046440684  0.0095576186
sample103 -0.0472521001 -0.0940389903
sample104  0.0198163489 -0.0591119389
sample105 -0.0400234005 -0.0160955438
sample106 -0.0923810433  0.0369015749
sample107 -0.1019373962  0.0224963940
sample108 -0.0877091802 -0.0128833240
sample109  0.0864826081 -0.0900959215
sample110 -0.1223117021 -0.0096089096
sample111  0.0257360952 -0.0936204000
sample112 -0.0765286729  0.0270351099
sample113  0.0258807898  0.0377469416
sample114  0.0021140993 -0.0882023532
sample115  0.0303461915 -0.0723609668
sample116  0.0780503632 -0.0685072758
sample117  0.0536900276 -0.0911960071
sample118  0.0666646803 -0.0236220248
sample119  0.1021872738 -0.2324902632
sample120  0.0750214032  0.0243384185
sample121 -0.0756937436  0.0942975621
sample122 -0.0259624780  0.0731951679
sample123 -0.1037846810 -0.0369201571
sample124  0.0611206431  0.0421717447
sample125 -0.0738470788  0.0066921775
sample126  0.0972914180  0.0762684452
sample127  0.0824695402 -0.0096619628
sample128 -0.1249409980  0.0929321893
sample129 -0.0734069202 -0.0434355308
sample130 -0.0003502765 -0.0309857954
sample131  0.0930180457  0.0155956680
sample132  0.0736222326  0.0733020591
sample133 -0.0498393759 -0.0462463703
sample134  0.1644870357  0.0720030051
sample135 -0.0752297290  0.0003836422
sample136  0.0227145745 -0.0495489024
sample137  0.0564714797 -0.0288896046
sample138  0.0255989751 -0.0610881681
sample139  0.0621212362  0.0235810905
sample140 -0.0604149612 -0.0435574232
sample141  0.0246746158  0.0532625511
sample142 -0.0409557307  0.0316246555
sample143 -0.0077352155 -0.0476920282
sample144  0.0173244962 -0.0156792037
sample145  0.0485476473  0.1202746525
sample146  0.0419644391 -0.0811260092
sample147 -0.0977311999 -0.0274830579
sample148  0.0368259280  0.0803948752
sample149 -0.0072865140 -0.1533002017
sample150  0.1020822098  0.0624791380
sample151  0.0305402459 -0.0289305896
sample152 -0.0533594875 -0.0638289235
sample153 -0.0891633342  0.1799585476
sample154 -0.0727558168 -0.0834148440
sample155 -0.0880665498 -0.0220819881
sample156 -0.0276554766 -0.0326624857
sample157 -0.1155033917  0.0183634383
sample158 -0.0281503017 -0.0104945918
sample159  0.0663233183  0.0443822937
sample160 -0.0302637826  0.0404245933
sample161  0.0114717263 -0.0591054356
sample162 -0.1337092076  0.1398164502
sample163  0.1330119748  0.1688802571
sample164 -0.0150330886  0.0028378428
sample165  0.0076527126 -0.0164164204
sample166  0.0367791873  0.0630652702
sample167  0.1111988344  0.0030067191
sample168 -0.0672983735  0.0446268594
sample169 -0.0413005146  0.0224405059
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
                      1             2
sample1    0.0420514074  0.0867864360
sample2    0.0820834131 -0.0410991533
sample3   -0.0155916722 -0.0195169109
sample4    0.1001328941 -0.0410799642
sample5    0.0153452520 -0.0253261631
sample6   -0.0340310603 -0.0408233193
sample7   -0.0722585212  0.0002344443
sample8    0.0457526787 -0.0370042880
sample9    0.0086244447  0.0820188773
sample10   0.0423596088 -0.0083932459
sample11  -0.0022553540  0.0787773249
sample12  -0.0322071898  0.1479820255
sample13   0.0293916184 -0.0306765956
sample14  -0.0337473499 -0.0367511377
sample15  -0.0815527337  0.1275634752
sample16  -0.0508416436  0.0540590497
sample17  -0.0062578534  0.0041017742
sample18  -0.0705628532 -0.0351044619
sample19   0.0476815226 -0.0509593075
sample20  -0.0522965456  0.0715538058
sample21   0.0119143772 -0.0376112892
sample22  -0.0724406675 -0.0095605710
sample23   0.0992527382  0.0134276426
sample24   0.1595175845  0.0728615424
sample25   0.0920673928 -0.0749762356
sample26   0.0595571664  0.0848951811
sample27  -0.0826510015 -0.0086709427
sample28   0.0384812561  0.0440953524
sample29  -0.0777659919  0.1735326174
sample30  -0.1229481078 -0.0818987233
sample31  -0.0579825133 -0.0238653516
sample32  -0.0970392805 -0.0111416944
sample33  -0.1017591880 -0.0630430598
sample34  -0.0637911956  0.0377946839
sample35  -0.0789987341 -0.0229709979
sample36  -0.1224959417 -0.1274938836
sample37  -0.1798849444 -0.1673397795
sample38  -0.0466306175  0.0888179262
sample39   0.0168696321  0.0421530400
sample40  -0.1756418872 -0.1526613869
sample41  -0.0042395056  0.0004945697
sample42   0.0447836172 -0.0651505736
sample43  -0.0482315717 -0.0253523928
sample44   0.1986735944 -0.0545821613
sample45   0.0741857933  0.0054680997
sample46  -0.0478787667 -0.0007051256
sample47  -0.0608183358  0.0481634322
sample48   0.1381487252  0.0578273643
sample49   0.0530527093 -0.1405556465
sample50   0.0173784929  0.1602410151
sample51  -0.0462543046  0.0303463758
sample52  -0.0280057793  0.0280382017
sample53  -0.0667590372  0.0237689866
sample54  -0.0121840841 -0.0521354431
sample55  -0.0182403293  0.0221331465
sample56   0.0001262364  0.0030899951
sample57  -0.0316655432  0.0530178829
sample58  -0.0393916005 -0.0297797592
sample59  -0.1278295221 -0.0546513933
sample60  -0.1486964607  0.1069170607
sample61  -0.0793111209  0.0569798432
sample62  -0.1172801471 -0.0149180420
sample63   0.0028769206  0.1300502244
sample64  -0.0237331431  0.1073276652
sample65   0.0126539424  0.0589805497
sample66   0.0468184478 -0.0771082575
sample67  -0.1494281582 -0.0769837479
sample68  -0.0977985695 -0.0577327614
sample69  -0.0403082595  0.0156046699
sample70  -0.0221535747  0.0315453054
sample71   0.0546406084 -0.0272386131
sample72  -0.1107500546 -0.0537301224
sample73  -0.0906751708  0.0579977599
sample74  -0.0586553541  0.0121423778
sample75  -0.0390499819  0.0349291289
sample76   0.0022935054 -0.1676554202
sample77   0.0232070871 -0.2067304935
sample78   0.0929774550 -0.0434960657
sample79   0.1619462295 -0.0378115907
sample80  -0.0680360029  0.1424675697
sample81   0.0530765320 -0.0358347792
sample82  -0.0266823286 -0.0577437093
sample83  -0.1517242771 -0.0448532500
sample84   0.0570947239 -0.0273814563
sample85  -0.1086302134 -0.1228107378
sample86  -0.0833864194 -0.0442898822
sample87  -0.0022040085 -0.0943900691
sample88   0.0078217465 -0.1140513565
sample89  -0.0611047460 -0.0094584434
sample90  -0.0022941919 -0.0936250555
sample91  -0.0433581866  0.3206010972
sample92   0.1815305467 -0.0334684884
sample93  -0.0267622347  0.0614435355
sample94  -0.0181871276  0.0605095128
sample95   0.0720362253 -0.0013045461
sample96   0.0559702472 -0.0118791553
sample97   0.0217415159  0.0195409207
sample98  -0.0379173234  0.0588364864
sample99   0.0792451504 -0.0151297277
sample100 -0.0222106425 -0.0023322540
sample101  0.0387218821  0.1224241773
sample102  0.2094604950 -0.0516470189
sample103 -0.0138494738  0.0301066048
sample104  0.0807970293 -0.0162722224
sample105  0.0520467726 -0.1229668324
sample106  0.0192620742 -0.0185244991
sample107 -0.0319007892  0.0405126788
sample108  0.0140692268  0.0163422284
sample109  0.1831923867  0.0612993680
sample110  0.0292789963 -0.0199850988
sample111  0.1423233428  0.0327334616
sample112 -0.0426328664 -0.0029080436
sample113  0.0771907869  0.0268719654
sample114  0.0241621199 -0.0184071298
sample115  0.1959010492  0.0460113592
sample116  0.1394466677 -0.0530818683
sample117  0.1672325754 -0.1386547695
sample118  0.0448345910 -0.0117627052
sample119  0.0910381456  0.2217447364
sample120  0.0331398129 -0.0057281882
sample121 -0.0307537511  0.1392498988
sample122  0.0839784270 -0.0292012910
sample123 -0.0239664213 -0.0642154995
sample124  0.0909161477  0.0130402440
sample125  0.0065333277 -0.1092631576
sample126 -0.0935279014  0.1368286529
sample127 -0.0035383266  0.0292757255
sample128  0.0660330358  0.1018547166
sample129 -0.0693653632 -0.0695406203
sample130 -0.0008507596 -0.0669699469
sample131 -0.0431016691  0.0174067204
sample132  0.0637052160  0.0029357927
sample133  0.0289476160 -0.0390815796
sample134 -0.0446181455  0.0456328249
sample135 -0.0712329335  0.0521644837
sample136 -0.0596277558  0.0197314103
sample137 -0.0793160110 -0.0380614446
sample138  0.0973530356 -0.0454223222
sample139 -0.0539915884 -0.1534325141
sample140 -0.0850825004  0.0955832140
sample141  0.0192678160 -0.0554457865
sample142  0.0672257080 -0.0461333009
sample143  0.0303711694 -0.0519258695
sample144  0.0089357618  0.0145816598
sample145  0.0638789373  0.0122233191
sample146 -0.0585867724  0.0063100855
sample147 -0.0894148835 -0.1124600044
sample148  0.0216366005 -0.0615979729
sample149  0.0515384390 -0.0839891950
sample150 -0.0568271647 -0.0124471176
sample151  0.0789519376 -0.0261837460
sample152  0.0330763950  0.1306446613
sample153  0.1751994805  0.1497684218
sample154 -0.0421436806 -0.0036993976
sample155 -0.0680184590  0.0095724627
sample156 -0.0388911131  0.1057573968
sample157 -0.0314755046  0.0561370044
sample158 -0.0329624000  0.0353953836
sample159  0.0398412571 -0.1007385270
sample160 -0.0424940233  0.0108498056
sample161  0.0888351042 -0.0679705251
sample162  0.0027525460  0.1237825704
sample163  0.0126148824  0.0725409725
sample164  0.0566766125 -0.0458330597
sample165  0.0315320370 -0.0236363591
sample166  0.0612065962 -0.0425249248
sample167 -0.0142727372  0.0179308986
sample168  0.0169503293 -0.0769625102
sample169 -0.0675074891  0.0131511499
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
                      1             2
sample1   -0.0012323450  1.635715e-01
sample2   -0.0724333900  6.022339e-03
sample3   -0.0188462104  1.080036e-01
sample4    0.0390150610 -3.115206e-04
sample5    0.1774816562  2.996231e-02
sample6   -0.0451430236  3.455914e-02
sample7   -0.0226480327  7.020022e-03
sample8   -0.1033658755  9.858095e-03
sample9    0.1349993764 -8.979246e-02
sample10   0.1259902016  5.097760e-02
sample11   0.0979771653 -7.086649e-02
sample12  -0.0863007602  8.620377e-02
sample13  -0.1381395050 -1.827990e-01
sample14  -0.0615066879  2.642863e-02
sample15   0.0381593793  3.101591e-02
sample16  -0.0048756597 -1.271630e-03
sample17  -0.0788462743  1.547654e-02
sample18  -0.0884175949  3.795579e-02
sample19   0.0703046200  1.083998e-01
sample20  -0.0025607334 -7.975908e-02
sample21   0.0941634211  4.126706e-02
sample22  -0.0550284954  7.806756e-02
sample23   0.0679501128  4.101955e-02
sample24  -0.1310929557 -1.649288e-01
sample25   0.0113589679  4.426874e-02
sample26  -0.1402929097 -2.016386e-02
sample27   0.0261536207 -1.589150e-03
sample28  -0.0724186367 -5.850497e-02
sample29  -0.0330079676 -2.061172e-03
sample30  -0.0228753703  2.015444e-02
sample31  -0.0635049079  6.670404e-02
sample32   0.0685106131  4.955204e-02
sample33  -0.0777759915  1.272084e-01
sample34   0.0157851410  3.024300e-02
sample35  -0.0529655480 -1.500969e-01
sample36   0.0070863624 -2.025311e-01
sample37  -0.0442460835 -1.802089e-01
sample38  -0.0781527187  3.676446e-02
sample39   0.0120343383  3.388843e-02
sample40  -0.0473330119 -1.471561e-01
sample41   0.0228171757  2.673503e-02
sample42  -0.0245352616  7.960904e-02
sample43   0.1036372151  8.229483e-02
sample44  -0.1012208233 -7.049291e-02
sample45   0.0013762712  2.450965e-02
sample46  -0.0558527300 -2.947166e-03
sample47  -0.0380490444 -4.554157e-02
sample48   0.0784340722 -4.889037e-02
sample49  -0.0605142435  1.162462e-02
sample50   0.0530052324  2.737822e-02
sample51   0.1514657528 -5.678468e-02
sample52   0.1860932355 -1.246734e-01
sample53  -0.0064156502  2.701013e-02
sample54   0.0697045473  2.308339e-02
sample55   0.1633573698 -1.366599e-02
sample56   0.1011493555 -4.682277e-02
sample57   0.1730376158 -1.609617e-01
sample58  -0.0071380026  1.666965e-02
sample59  -0.0030472086 -3.005305e-02
sample60   0.0215802244 -2.665884e-01
sample61   0.1510579351 -1.002400e-01
sample62  -0.0925539129  4.845899e-02
sample63  -0.0596291308  4.137084e-02
sample64  -0.0449217686  2.600912e-03
sample65   0.0939391959  4.406825e-02
sample66   0.1063416877  5.709928e-02
sample67  -0.0201638355 -2.361731e-01
sample68   0.0037177018 -2.418436e-02
sample69  -0.0645154700  1.155626e-01
sample70  -0.1013441481  1.351795e-01
sample71  -0.0016484197  2.976824e-02
sample72   0.0328888274  2.835811e-02
sample73   0.0275086724  5.148149e-02
sample74   0.1341732568  7.895158e-02
sample75   0.0951571237  3.943075e-02
sample76  -0.0864735145 -3.034911e-02
sample77  -0.1035752715  2.545464e-02
sample78  -0.1575624012 -4.939393e-02
sample79   0.0189114598 -4.874701e-02
sample80   0.1384130361 -4.432829e-05
sample81  -0.0118850072  6.357938e-02
sample82  -0.1675312381 -3.533753e-02
sample83  -0.0065674487  7.812591e-02
sample84   0.1486893412  3.108931e-02
sample85  -0.0532742482 -7.417855e-02
sample86  -0.1138483335  2.008117e-05
sample87   0.0432853426 -6.080507e-02
sample88   0.0433446792 -1.402492e-01
sample89   0.0331219423  1.395386e-02
sample90  -0.0607408314  8.610475e-02
sample91  -0.0566332233 -1.303754e-01
sample92  -0.0359609479 -1.061601e-01
sample93  -0.0433642435  4.443666e-02
sample94  -0.0477285808  1.059576e-01
sample95  -0.0249601921  3.980541e-02
sample96   0.0035222922  9.293926e-02
sample97  -0.0066031669  1.527232e-01
sample98   0.0020368223  5.579532e-02
sample99  -0.0886583936  3.728365e-02
sample100 -0.1091255396  3.560516e-02
sample101 -0.0739748599  4.318016e-02
sample102  0.0574479289  2.783918e-02
sample103  0.0142714863 -9.705964e-03
sample104  0.0710385804 -4.068413e-02
sample105  0.0980837602  3.452890e-02
sample106 -0.0254251001 -3.628936e-02
sample107 -0.0160639285  9.173412e-02
sample108 -0.0200983230  2.379716e-02
sample109 -0.0389790040 -1.692323e-02
sample110 -0.0326302038 -2.988062e-02
sample111  0.0676934736  6.038151e-02
sample112  0.0167888117 -5.337039e-03
sample113  0.0969229669  2.757536e-02
sample114 -0.0026403576  9.209143e-02
sample115 -0.0308050285 -1.603780e-02
sample116 -0.1240324500 -1.272987e-01
sample117  0.0334717923 -5.392715e-02
sample118 -0.1037162513 -6.252337e-02
sample119 -0.1064227542 -1.196201e-01
sample120 -0.0771342789  1.004940e-01
sample121 -0.0129337185 -3.181957e-02
sample122  0.0847516606  5.568293e-02
sample123 -0.0041340529 -7.693114e-03
sample124 -0.0583439980  8.396466e-02
sample125  0.0634854804  5.232507e-02
sample126 -0.0662568981  1.091736e-01
sample127 -0.0865020248  1.094182e-01
sample128 -0.0627791344  1.471058e-02
sample129 -0.0336281135  4.007878e-02
sample130 -0.0293515341  8.046144e-02
sample131 -0.0469201587  2.210015e-03
sample132 -0.0241715791  1.248603e-01
sample133  0.0907298158 -1.466779e-02
sample134 -0.0350846654 -7.539642e-02
sample135  0.0001330883 -9.185535e-03
sample136 -0.0335880546  9.860273e-02
sample137 -0.0640154210  7.554503e-02
sample138  0.0060961392  1.742766e-02
sample139 -0.0592090491 -5.614906e-02
sample140  0.0427975861  1.099475e-02
sample141  0.0618814570  9.301005e-02
sample142  0.0898563020 -3.573466e-02
sample143  0.0817375592 -8.880597e-02
sample144  0.0787755292  3.821314e-02
sample145  0.1085829977 -1.569482e-01
sample146 -0.0589573487  4.373374e-02
sample147 -0.0495336636 -7.276784e-03
sample148  0.1161606061 -9.079830e-03
sample149 -0.0121608688 -7.788380e-02
sample150 -0.0314512843 -3.520187e-02
sample151  0.0575382913  1.945310e-02
sample152 -0.0494556009 -7.025514e-02
sample153 -0.0941307419 -2.153281e-01
sample154 -0.0335947844 -2.078722e-02
sample155  0.0690455667  2.780333e-02
sample156  0.1039899643  6.292402e-02
sample157 -0.0408640899 -8.065133e-03
sample158  0.1018101620 -7.817921e-03
sample159 -0.0281721344  1.207259e-02
sample160  0.1643058171 -2.979569e-03
sample161  0.0374317034 -8.524633e-02
sample162 -0.0804513917 -8.349648e-02
sample163 -0.0743202968  1.406318e-02
sample164  0.1208813028  2.139371e-02
sample165  0.1608113117 -2.025333e-02
sample166 -0.0425928712  2.660785e-02
sample167 -0.0226850642  4.464287e-02
sample168 -0.0180723374  7.471015e-04
sample169  0.0190777792 -2.645426e-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 
  11.32    0.48   11.81 

Example timings

STATegRa.Rcheck/examples_i386/STATegRa-Ex.timings

nameusersystemelapsed
STATegRaUsersGuide000
STATegRa_data0.30.00.3
STATegRa_data_TCGA_BRCA000
bioDist0.470.030.50
bioDistFeature0.390.000.39
bioDistFeaturePlot0.380.010.39
bioDistW0.360.020.38
bioDistWPlot0.360.020.38
bioMap0.020.000.01
combiningMappings000
createOmicsExpressionSet0.170.000.18
getInitialData0.510.120.64
getLoadings0.570.080.64
getMethodInfo0.610.150.76
getPreprocessing0.820.211.03
getScores0.720.190.91
getVAF0.830.120.95
holistOmics0.020.000.02
modelSelection1.510.381.89
omicsCompAnalysis3.380.103.48
omicsNPC000
plotRes4.380.024.39
plotVAF3.680.033.72

STATegRa.Rcheck/examples_x64/STATegRa-Ex.timings

nameusersystemelapsed
STATegRaUsersGuide000
STATegRa_data0.310.030.34
STATegRa_data_TCGA_BRCA000
bioDist0.500.020.51
bioDistFeature0.500.010.51
bioDistFeaturePlot0.370.020.39
bioDistW0.520.000.52
bioDistWPlot0.630.020.64
bioMap0.020.000.01
combiningMappings0.010.000.02
createOmicsExpressionSet0.250.000.25
getInitialData0.860.181.04
getLoadings0.890.090.98
getMethodInfo0.710.130.83
getPreprocessing0.870.231.11
getScores0.770.110.88
getVAF0.650.130.78
holistOmics000
modelSelection1.50.41.9
omicsCompAnalysis4.020.104.11
omicsNPC0.010.000.02
plotRes4.740.114.84
plotVAF4.120.104.24