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This page was generated on 2026-05-02 11:36 -0400 (Sat, 02 May 2026).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 24.04.4 LTS)x86_644.6.0 RC (2026-04-17 r89917) -- "Because it was There" 4988
kjohnson3macOS 13.7.7 Venturaarm644.6.0 Patched (2026-04-24 r89963) -- "Because it was There" 4718
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Package 2190/2418HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
STATegRa 1.48.0  (landing page)
David Gomez-Cabrero , Núria Planell
Snapshot Date: 2026-05-01 13:40 -0400 (Fri, 01 May 2026)
git_url: https://git.bioconductor.org/packages/STATegRa
git_branch: RELEASE_3_23
git_last_commit: e70783e
git_last_commit_date: 2026-04-28 08:40:02 -0400 (Tue, 28 Apr 2026)
nebbiolo1Linux (Ubuntu 24.04.4 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
kjohnson3macOS 13.7.7 Ventura / arm64  OK    OK    OK    OK  UNNEEDED, same version is already published
See other builds for STATegRa in R Universe.

CHECK results for STATegRa on kjohnson3

To the developers/maintainers of the STATegRa package:
- 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 Troubleshooting Build Report for more information.
- Use the following Renviron settings to reproduce errors and warnings.
- If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information.

raw results


Summary

Package: STATegRa
Version: 1.48.0
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings STATegRa_1.48.0.tar.gz
StartedAt: 2026-05-01 22:42:29 -0400 (Fri, 01 May 2026)
EndedAt: 2026-05-01 22:43:49 -0400 (Fri, 01 May 2026)
EllapsedTime: 79.9 seconds
RetCode: 0
Status:   OK  
CheckDir: STATegRa.Rcheck
Warnings: 0

Command output

##############################################################################
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###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings STATegRa_1.48.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.23-bioc/meat/STATegRa.Rcheck’
* using R version 4.6.0 Patched (2026-04-24 r89963)
* using platform: aarch64-apple-darwin23
* R was compiled by
    Apple clang version 17.0.0 (clang-1700.3.19.1)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Tahoe 26.3.1
* using session charset: UTF-8
* current time: 2026-05-02 02:42:29 UTC
* using option ‘--no-vignettes’
* checking for file ‘STATegRa/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘STATegRa’ version ‘1.48.0’
* 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 for sufficient/correct file permissions ... 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 code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* 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 ... NOTE
Found the following Rd file(s) with Rd \link{} targets missing package
anchors:
  modelSelection.Rd: ggplot
Please provide package anchors for all Rd \link{} targets not in the
package itself and the base packages.
* 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 ... OK
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  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 ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 2 NOTEs
See
  ‘/Users/biocbuild/bbs-3.23-bioc/meat/STATegRa.Rcheck/00check.log’
for details.

Installation output

STATegRa.Rcheck/00install.out

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###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL STATegRa
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.6/Resources/library’
* installing *source* package ‘STATegRa’ ...
** this is package ‘STATegRa’ version ‘1.48.0’
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** 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
* DONE (STATegRa)

Tests output

STATegRa.Rcheck/tests/runTests.Rout


R version 4.6.0 Patched (2026-04-24 r89963) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin23

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 May  1 22:43:45 2026 
*********************************************** 
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 
  1.380   0.078   1.454

STATegRa.Rcheck/tests/STATEgRa_Example.omicsCLUST.Rout


R version 4.6.0 Patched (2026-04-24 r89963) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin23

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

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

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

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

Attaching package: 'generics'

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

    as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    setequal, union


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, aperm, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
    mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    rank, rbind, rownames, sapply, saveRDS, table, tapply, 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
> 
> #############################################
> ## 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 
 12.358   0.157  12.526

STATegRa.Rcheck/tests/STATegRa_Example.omicsNPC.Rout


R version 4.6.0 Patched (2026-04-24 r89963) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin23

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
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calcNormFactors has been renamed to normLibSizes
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calcNormFactors has been renamed to normLibSizes
calcNormFactors has been renamed to normLibSizes
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calcNormFactors has been renamed to normLibSizes
calcNormFactors has been renamed to normLibSizes
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calcNormFactors has been renamed to normLibSizes
calcNormFactors has been renamed to normLibSizes
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calcNormFactors has been renamed to normLibSizes
calcNormFactors has been renamed to normLibSizes
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calcNormFactors has been renamed to normLibSizes
calcNormFactors has been renamed to normLibSizes
calcNormFactors has been renamed to normLibSizes
calcNormFactors has been renamed to normLibSizes
calcNormFactors has been renamed to normLibSizes
calcNormFactors has been renamed to normLibSizes
calcNormFactors has been renamed to normLibSizes
calcNormFactors has been renamed to normLibSizes
calcNormFactors has been renamed to normLibSizes
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Compute pseudo p-values based on NULL distributions...
NPC p-values calculation...
> 
> proc.time()
   user  system elapsed 
 19.968   0.168  20.185

STATegRa.Rcheck/tests/STATEgRa_Example.omicsPCA.Rout


R version 4.6.0 Patched (2026-04-24 r89963) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin23

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Type 'license()' or 'licence()' for distribution details.

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Type 'demo()' for some demos, 'help()' for on-line help, or
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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   -0.0781575623  0.0431548146
sample2    0.1192221332 -0.0294022324
sample3    0.0531408728  0.0746837648
sample4   -0.0292971837  0.0006033415
sample5   -0.0202090766 -0.0110455293
sample6   -0.1226088445 -0.1053492957
sample7   -0.1078931290  0.0322419527
sample8   -0.1782891192 -0.1449330682
sample9   -0.0468697307  0.0455171676
sample10   0.0036032653 -0.0420078016
sample11   0.0035566360  0.0566285120
sample12  -0.1006129674 -0.0641394061
sample13   0.1174412758 -0.0907475499
sample14  -0.0981203545 -0.0617763370
sample15  -0.0085337222  0.0086956461
sample16  -0.0783146834 -0.1581333285
sample17   0.1483610659 -0.0638580142
sample18   0.0963084431 -0.0556687590
sample19   0.0217243078  0.0720128373
sample20   0.0635633965  0.0779610453
sample21   0.0201843950 -0.1566382018
sample22  -0.0218273772  0.0764056129
sample23  -0.0852039098  0.0032764511
sample24   0.1287181354 -0.1924427045
sample25   0.0430575614  0.0456638240
sample26   0.1453899738 -0.0541459234
sample27   0.0197483686  0.1185593254
sample28   0.1025339381 -0.0650655574
sample29  -0.0706022411  0.0682933116
sample30   0.1295623057  0.0066678267
sample31  -0.1147449297 -0.1232727017
sample32   0.0374308257 -0.0380249142
sample33  -0.0599520693 -0.0136935691
sample34   0.0984199330 -0.0375364384
sample35   0.0543096456  0.0378035593
sample36  -0.1403627991  0.0343639699
sample37  -0.0228947573  0.0732690605
sample38   0.0222073077  0.0962567309
sample39   0.0941739205 -0.0215180668
sample40  -0.0643806973  0.0687720809
sample41   0.0327635010  0.1232187217
sample42   0.0500431632  0.0292513611
sample43   0.0184497206 -0.0233044172
sample44  -0.1487889478 -0.1171210942
sample45   0.1050778730 -0.1123140709
sample46   0.1151191652  0.1093995950
sample47   0.0962591558  0.0288418242
sample48  -0.0004832693  0.0310378814
sample49  -0.1135203915 -0.1213936706
sample50   0.0123549945  0.1740762733
sample51  -0.0550527465 -0.1258930568
sample52  -0.0499118519 -0.0728580776
sample53  -0.1119772670 -0.1588064058
sample54   0.0360055720 -0.0228585531
sample55  -0.0210418834 -0.0006750627
sample56   0.0434171469 -0.0633131231
sample57  -0.0197820736 -0.1150753781
sample58  -0.0030440688 -0.0326127387
sample59  -0.0500256680 -0.0129521072
sample60  -0.0184280056 -0.0136217512
sample61  -0.0150298922 -0.0635096613
sample62   0.0304758827  0.0201236547
sample63  -0.1102250153 -0.1285968531
sample64  -0.1552586845 -0.0971185257
sample65   0.0058503802 -0.0207102833
sample66   0.0025607444 -0.0424284680
sample67  -0.1546638602  0.0661578936
sample68  -0.0536374183  0.0923604535
sample69  -0.0640332964 -0.0082003778
sample70  -0.0163521692  0.0663227187
sample71   0.0102536126  0.1345964737
sample72   0.0654191818  0.0196036680
sample73   0.1048553283 -0.0220999770
sample74  -0.0123800489 -0.0586156665
sample75  -0.0392079718  0.0209726306
sample76  -0.0648954551  0.0524759612
sample77  -0.1172922632  0.0201200418
sample78   0.1463072566 -0.0708399900
sample79  -0.0265208883  0.1603424870
sample80  -0.0279739198  0.0214153517
sample81  -0.0079212149  0.0738495308
sample82   0.1544234630  0.0361450704
sample83   0.0494205501  0.0049939485
sample84   0.0259039707  0.0346591406
sample85  -0.1116487388  0.0031404715
sample86   0.1306479112  0.0377156299
sample87   0.0554777872  0.0459739758
sample88   0.0301626505 -0.0382206587
sample89   0.1016866203 -0.0694077997
sample90  -0.0086821617  0.0201323860
sample91  -0.1578629742  0.2097792106
sample92  -0.0170933566  0.1655935749
sample93   0.0979805117  0.0121500484
sample94  -0.0131486197  0.0114929428
sample95  -0.0315682473  0.0758916830
sample96  -0.0024125846  0.0470184936
sample97  -0.0634545807 -0.0270303863
sample98   0.0359372578  0.0135466576
sample99   0.1009167561 -0.1124713013
sample100 -0.0551754080 -0.0246502069
sample101  0.0080116011  0.1627406926
sample102  0.0046451084 -0.0095473825
sample103  0.0472520891  0.0940383562
sample104 -0.0198157490  0.0591146886
sample105  0.0400238976  0.0160948996
sample106  0.0923810121 -0.0369003929
sample107  0.1019372387 -0.0224967243
sample108  0.0877091512  0.0128849626
sample109 -0.0864820350  0.0901080065
sample110  0.1223116467  0.0096108386
sample111 -0.0257352482  0.0936280441
sample112  0.0765285934 -0.0270379144
sample113 -0.0258799899 -0.0377438787
sample114 -0.0021141074  0.0882040005
sample115 -0.0303455374  0.0723734220
sample116 -0.0780504517  0.0685161722
sample117 -0.0536894126  0.0912024719
sample118 -0.0666649909  0.0236260866
sample119 -0.1021872588  0.2325003142
sample120 -0.0750216337 -0.0243345877
sample121  0.0756937865 -0.0942970406
sample122  0.0259632009 -0.0731921601
sample123  0.1037844710  0.0369178918
sample124 -0.0611205212 -0.0421647601
sample125  0.0738472625 -0.0066944211
sample126 -0.0972919098 -0.0762698571
sample127 -0.0824699420  0.0096644594
sample128  0.1249411471 -0.0929254288
sample129  0.0734063749  0.0434313992
sample130  0.0003500275  0.0309857227
sample131 -0.0930184029 -0.0155969896
sample132 -0.0736220638 -0.0732972546
sample133  0.0498398351  0.0462455857
sample134 -0.1644872632 -0.0720046715
sample135  0.0752295073 -0.0003869279
sample136 -0.0227149921  0.0495469664
sample137 -0.0564721649  0.0288861009
sample138 -0.0255986515  0.0610930082
sample139 -0.0621218749 -0.0235857333
sample140  0.0604148987  0.0435532717
sample141 -0.0246743054 -0.0532630407
sample142  0.0409563823 -0.0316234543
sample143  0.0077356376  0.0476908797
sample144 -0.0173241007  0.0156785810
sample145 -0.0485467790 -0.1202738164
sample146 -0.0419649928  0.0811241194
sample147  0.0977304709  0.0274771861
sample148 -0.0368253316 -0.0803969526
sample149  0.0072864882  0.1533016843
sample150 -0.1020825500 -0.0624822214
sample151 -0.0305397193  0.0289336636
sample152  0.0533595194  0.0638334746
sample153  0.0891639284 -0.1799454218
sample154  0.0727554437  0.0834129648
sample155  0.0880665856  0.0220770733
sample156  0.0276558866  0.0326601925
sample157  0.1155031571 -0.0183635411
sample158  0.0281506709  0.0104912127
sample159 -0.0663233795 -0.0443809860
sample160  0.0302644009 -0.0404301061
sample161 -0.0114712992  0.0591082756
sample162  0.1337090957 -0.1398131546
sample163 -0.1330120792 -0.1688769481
sample164  0.0150338116 -0.0028375637
sample165 -0.0076518859  0.0164145739
sample166 -0.0367791530 -0.0630614547
sample167 -0.1111989808 -0.0030066403
sample168  0.0672982967 -0.0446266759
sample169  0.0413003659 -0.0224446693
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
                      1             2
sample1    0.0420464080  0.0867866045
sample2    0.0820849090 -0.0410969117
sample3   -0.0155964363 -0.0195186175
sample4    0.1001342577 -0.0410776702
sample5    0.0153479293 -0.0253257801
sample6   -0.0340241571 -0.0408223420
sample7   -0.0722601771  0.0002324195
sample8    0.0457616105 -0.0370007306
sample9    0.0086217659  0.0820184514
sample10   0.0423630265 -0.0083917842
sample11  -0.0022592143  0.0787764195
sample12  -0.0322076877  0.1479823406
sample13   0.0293967976 -0.0306743122
sample14  -0.0337432379 -0.0367508345
sample15  -0.0815559519  0.1275614124
sample16  -0.0508335334  0.0540604272
sample17  -0.0062556374  0.0041024814
sample18  -0.0705601978 -0.0351053193
sample19   0.0476785273 -0.0509595478
sample20  -0.0523025281  0.0715514349
sample21   0.0119247496 -0.0376087354
sample22  -0.0724456193 -0.0095634558
sample23   0.0992529612  0.0134298644
sample24   0.1595261490  0.0728683418
sample25   0.0920661925 -0.0749749483
sample26   0.0595566350  0.0848973410
sample27  -0.0826574495 -0.0086747120
sample28   0.0384832408  0.0440972518
sample29  -0.0777738822  0.1735298844
sample30  -0.1229474311 -0.0819018068
sample31  -0.0579753250 -0.0238646855
sample32  -0.0970366535 -0.0111434883
sample33  -0.1017580396 -0.0630452282
sample34  -0.0637903198  0.0377936368
sample35  -0.0790002852 -0.0229732196
sample36  -0.1224933338 -0.1274967956
sample37  -0.1798846824 -0.1673447399
sample38  -0.0466391291  0.0888153463
sample39   0.0168694549  0.0421535972
sample40  -0.1756417334 -0.1526661745
sample41  -0.0042466564  0.0004924757
sample42   0.0447826143 -0.0651501469
sample43  -0.0482292509 -0.0253533415
sample44   0.1986816447 -0.0545754559
sample45   0.0741915788  0.0054713839
sample46  -0.0478859552 -0.0007080074
sample47  -0.0608215762  0.0481615671
sample48   0.1381465486  0.0578300633
sample49   0.0530627678 -0.1405523931
sample50   0.0173651007  0.1602386314
sample51  -0.0462459577  0.0303472984
sample52  -0.0279997966  0.0280387828
sample53  -0.0667502171  0.0237700118
sample54  -0.0121812361 -0.0521354897
sample55  -0.0182392314  0.0221326680
sample56   0.0001307384  0.0030909202
sample57  -0.0316577592  0.0530190563
sample58  -0.0393891813 -0.0297801715
sample59  -0.1278271990 -0.0546540238
sample60  -0.1486965096  0.1069142252
sample61  -0.0793069054  0.0569790544
sample62  -0.1172821294 -0.0149210787
sample63   0.0028810300  0.1300523944
sample64  -0.0237298136  0.1073288317
sample65   0.0126543424  0.0589810294
sample66   0.0468232682 -0.0771066805
sample67  -0.1494285395 -0.0769876891
sample68  -0.0978021750 -0.0577363416
sample69  -0.0403090391  0.0156038366
sample70  -0.0221596046  0.0315436791
sample71   0.0546333036 -0.0272394941
sample72  -0.1107500691 -0.0537331020
sample73  -0.0906756767  0.0579958133
sample74  -0.0586514628  0.0121417555
sample75  -0.0390512078  0.0349278354
sample76   0.0022940053 -0.1676560040
sample77   0.0232101159 -0.2067300991
sample78   0.0929808217 -0.0434928310
sample79   0.1619384158 -0.0378102761
sample80  -0.0680391893  0.1424656145
sample81   0.0530727210 -0.0358347729
sample82  -0.0266849639 -0.0577448957
sample83  -0.1517241885 -0.0448569637
sample84   0.0570943872 -0.0273808584
sample85  -0.1086272136 -0.1228130118
sample86  -0.0833890746 -0.0442924503
sample87  -0.0022040187 -0.0943908441
sample88   0.0078275250 -0.1140504627
sample89  -0.0611007381 -0.0094589296
sample90  -0.0022941304 -0.0936254828
sample91  -0.0433767671  0.3205972525
sample92   0.1815221130 -0.0334667020
sample93  -0.0267653341  0.0614425909
sample94  -0.0181900758  0.0605088243
sample95   0.0720316081 -0.0013040683
sample96   0.0559673877 -0.0118787238
sample97   0.0217420354  0.0195417112
sample98  -0.0379198606  0.0588352895
sample99   0.0792505898 -0.0151262749
sample100 -0.0222100800 -0.0023322898
sample101  0.0387088457  0.1224225304
sample102  0.2094625635 -0.0516421505
sample103 -0.0138555753  0.0301047801
sample104  0.0807949122 -0.0162712565
sample105  0.0520491810 -0.1229660487
sample106  0.0192642124 -0.0185235269
sample107 -0.0319014421  0.0405120657
sample108  0.0140674671  0.0163422322
sample109  0.1831859064  0.0613023238
sample110  0.0292782849 -0.0199846551
sample111  0.1423176007  0.0327351779
sample112 -0.0426313819 -0.0029086958
sample113  0.0771931373  0.0268742454
sample114  0.0241569969 -0.0184080601
sample115  0.1958957788  0.0460148091
sample116  0.1394438431 -0.0530793843
sample117  0.1672313017 -0.1386522353
sample118  0.0448331938 -0.0117618099
sample119  0.0910198204  0.2217435775
sample120  0.0331404599 -0.0057270458
sample121 -0.0307517858  0.1392506172
sample122  0.0839836409 -0.0291983959
sample123 -0.0239674781 -0.0642167274
sample124  0.0909175788  0.0130429802
sample125  0.0065362041 -0.1092631038
sample126 -0.0935274049  0.1368277011
sample127 -0.0035405297  0.0292755037
sample128  0.0660349145  0.1018575483
sample129 -0.0693670493 -0.0695429985
sample130 -0.0008516678 -0.0669705338
sample131 -0.0431012189  0.0174061107
sample132  0.0637088017  0.0029383228
sample133  0.0289465194 -0.0390817326
sample134 -0.0446143221  0.0456332321
sample135 -0.0712343600  0.0521627814
sample136 -0.0596317542  0.0197291937
sample137 -0.0793175094 -0.0380637018
sample138  0.0973506382 -0.0454210334
sample139 -0.0539866943 -0.1534331989
sample140 -0.0850871184  0.0955804745
sample141  0.0192723021 -0.0554446592
sample142  0.0672293762 -0.0461313318
sample143  0.0303707534 -0.0519258580
sample144  0.0089350833  0.0145815362
sample145  0.0638874920  0.0122268365
sample146 -0.0585921371  0.0063075174
sample147 -0.0894146631 -0.1124625490
sample148  0.0216438238 -0.0615962582
sample149  0.0515318329 -0.0839902818
sample150 -0.0568229790 -0.0124472664
sample151  0.0789513984 -0.0261824141
sample152  0.0330694548  0.1306444983
sample153  0.1752062997  0.1497754750
sample154 -0.0421488566 -0.0037016882
sample155 -0.0680198419  0.0095703741
sample156 -0.0388949443  0.1057558110
sample157 -0.0314765120  0.0561364723
sample158 -0.0329630056  0.0353943726
sample159  0.0398460730 -0.1007368463
sample160 -0.0424906372  0.0108493119
sample161  0.0888340764 -0.0679693015
sample162  0.0027569255  0.1237848105
sample163  0.0126227249  0.0725440624
sample164  0.0566787025 -0.0458318461
sample165  0.0315331622 -0.0236359658
sample166  0.0612108322 -0.0425225086
sample167 -0.0142729558  0.0179307025
sample168  0.0169542255 -0.0769614971
sample169 -0.0675063680  0.0131499237
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
                      1             2
sample1   -0.0012331631  1.635716e-01
sample2   -0.0724353172  6.022120e-03
sample3   -0.0188459948  1.080029e-01
sample4    0.0390143153 -3.106620e-04
sample5    0.1774810643  2.996428e-02
sample6   -0.0451446427  3.455898e-02
sample7   -0.0226463520  7.019221e-03
sample8   -0.1033684494  9.857935e-03
sample9    0.1350014181 -8.979114e-02
sample10   0.1259884459  5.097936e-02
sample11   0.0979790886 -7.086566e-02
sample12  -0.0863020948  8.620322e-02
sample13  -0.1381401827 -1.827998e-01
sample14  -0.0615074721  2.642808e-02
sample15   0.0381600572  3.101602e-02
sample16  -0.0048779348 -1.271033e-03
sample17  -0.0788483192  1.547605e-02
sample18  -0.0884189481  3.795477e-02
sample19   0.0703043529  1.084003e-01
sample20  -0.0025581412 -7.975969e-02
sample21   0.0941596711  4.126891e-02
sample22  -0.0550270893  7.806618e-02
sample23   0.0679492850  4.102076e-02
sample24  -0.1310969356 -1.649283e-01
sample25   0.0113583583  4.426900e-02
sample26  -0.1402948868 -2.016460e-02
sample27   0.0261565979 -1.589942e-03
sample28  -0.0724200757 -5.850511e-02
sample29  -0.0330054815 -2.062065e-03
sample30  -0.0228750359  2.015345e-02
sample31  -0.0635070367  6.670368e-02
sample32   0.0685100006  4.955246e-02
sample33  -0.0777764923  1.272070e-01
sample34   0.0157842096  3.024311e-02
sample35  -0.0529628001 -1.500981e-01
sample36   0.0070907876 -2.025320e-01
sample37  -0.0442411927 -1.802109e-01
sample38  -0.0781508414  3.676300e-02
sample39   0.0120330095  3.388883e-02
sample40  -0.0473283953 -1.471581e-01
sample41   0.0228192151  2.673459e-02
sample42  -0.0245361839  7.960878e-02
sample43   0.1036362029  8.229577e-02
sample44  -0.1012234708 -7.049243e-02
sample45   0.0013726768  2.451067e-02
sample46  -0.0558506501 -2.948567e-03
sample47  -0.0380478748 -4.554236e-02
sample48   0.0784340478 -4.888893e-02
sample49  -0.0605168018  1.162470e-02
sample50   0.0530082866  2.737815e-02
sample51   0.1514645373 -5.678261e-02
sample52   0.1860936000 -1.246711e-01
sample53  -0.0064179662  2.701059e-02
sample54   0.0697037574  2.308412e-02
sample55   0.1633577697 -1.366433e-02
sample56   0.1011484012 -4.682135e-02
sample57   0.1730374403 -1.609594e-01
sample58  -0.0071384884  1.666951e-02
sample59  -0.0030458515 -3.005374e-02
sample60   0.0215842095 -2.665887e-01
sample61   0.1510585314 -1.002384e-01
sample62  -0.0925531611  4.845730e-02
sample63  -0.0596315381  4.137107e-02
sample64  -0.0449227256  2.600954e-03
sample65   0.0939382272  4.406949e-02
sample66   0.1063397777  5.710077e-02
sample67  -0.0201580945 -2.361746e-01
sample68   0.0037208318 -2.418540e-02
sample69  -0.0645161979  1.155618e-01
sample70  -0.1013439746  1.351780e-01
sample71  -0.0016466134  2.976774e-02
sample72   0.0328895378  2.835773e-02
sample73   0.0275080392  5.148153e-02
sample74   0.1341718393  7.895302e-02
sample75   0.0951576634  3.943148e-02
sample76  -0.0864719981 -3.035052e-02
sample77  -0.1035749510  2.545326e-02
sample78  -0.1575647824 -4.939476e-02
sample79   0.0189138344 -4.874690e-02
sample80   0.1384142747 -4.314666e-05
sample81  -0.0118846664  6.357908e-02
sample82  -0.1675306662 -3.533968e-02
sample83  -0.0065671177  7.812499e-02
sample84   0.1486890670  3.109095e-02
sample85  -0.0532720402 -7.417987e-02
sample86  -0.1138474948  1.821813e-05
sample87   0.0432865917 -6.080499e-02
sample88   0.0433451177 -1.402486e-01
sample89   0.0331204820  1.395428e-02
sample90  -0.0607413467  8.610386e-02
sample91  -0.0566263876 -1.303770e-01
sample92  -0.0359580739 -1.061605e-01
sample93  -0.0433646451  4.443610e-02
sample94  -0.0477292111  1.059571e-01
sample95  -0.0249595925  3.980510e-02
sample96   0.0035217600  9.293931e-02
sample97  -0.0066051950  1.527234e-01
sample98   0.0020367066  5.579516e-02
sample99  -0.0886621648  3.728372e-02
sample100 -0.1091259593  3.560401e-02
sample101 -0.0739723872  4.317887e-02
sample102  0.0574455780  2.784083e-02
sample103  0.0142733709 -9.706341e-03
sample104  0.0710395570 -4.068331e-02
sample105  0.0980829959  3.452996e-02
sample106 -0.0254260478 -3.628934e-02
sample107 -0.0160655010  9.173398e-02
sample108 -0.0200988301  2.379699e-02
sample109 -0.0389781932 -1.692312e-02
sample110 -0.0326305247 -2.988087e-02
sample111  0.0676935956  6.038248e-02
sample112  0.0167883512 -5.336924e-03
sample113  0.0969214000  2.757701e-02
sample114 -0.0026397981  9.209103e-02
sample115 -0.0308049542 -1.603745e-02
sample116 -0.1240306419 -1.272998e-01
sample117  0.0334728643 -5.392663e-02
sample118 -0.1037152174 -6.252439e-02
sample119 -0.1064170685 -1.196217e-01
sample120 -0.0771357667  1.004935e-01
sample121 -0.0129352286 -3.181915e-02
sample122  0.0847487629  5.568461e-02
sample123 -0.0041335538 -7.693545e-03
sample124 -0.0583462140  8.396474e-02
sample125  0.0634843272  5.232567e-02
sample126 -0.0662582068  1.091730e-01
sample127 -0.0865025596  1.094173e-01
sample128 -0.0627821958  1.471091e-02
sample129 -0.0336274615  4.007776e-02
sample130 -0.0293518107  8.046087e-02
sample131 -0.0469196804  2.209390e-03
sample132 -0.0241745555  1.248608e-01
sample133  0.0907303787 -1.466698e-02
sample134 -0.0350841243 -7.539660e-02
sample135  0.0001334863 -9.185811e-03
sample136 -0.0335874819  9.860183e-02
sample137 -0.0640147291  7.554373e-02
sample138  0.0060964055  1.742782e-02
sample139 -0.0592082790 -5.615005e-02
sample140  0.0427988569  1.099467e-02
sample141  0.0618793323  9.301101e-02
sample142  0.0898552535 -3.573326e-02
sample143  0.0817391029 -8.880528e-02
sample144  0.0787754481  3.821395e-02
sample145  0.1085819537 -1.569461e-01
sample146 -0.0589555045  4.373239e-02
sample147 -0.0495327950 -7.278043e-03
sample148  0.1161590521 -9.078152e-03
sample149 -0.0121575575 -7.788461e-02
sample150 -0.0314511984 -3.520220e-02
sample151  0.0575380961  1.945392e-02
sample152 -0.0494540388 -7.025565e-02
sample153 -0.0941338455 -2.153270e-01
sample154 -0.0335928880 -2.078823e-02
sample155  0.0690459027  2.780362e-02
sample156  0.1039902298  6.292489e-02
sample157 -0.0408645838 -8.065529e-03
sample158  0.1018106328 -7.817020e-03
sample159 -0.0281732500  1.207259e-02
sample160  0.1643052869 -2.977817e-03
sample161  0.0374330060 -8.524589e-02
sample162 -0.0804538217 -8.349637e-02
sample163 -0.0743232320  1.406344e-02
sample164  0.1208804305  2.139523e-02
sample165  0.1608115951 -2.025159e-02
sample166 -0.0425947882  2.660799e-02
sample167 -0.0226849504  4.464258e-02
sample168 -0.0180737340  7.471472e-04
sample169  0.0190780191 -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)
Warning message:
`aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.
ℹ The deprecated feature was likely used in the STATegRa package.
  Please report the issue to the authors. 
> 
> # 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 
  5.786   0.111   5.912

Example timings

STATegRa.Rcheck/STATegRa-Ex.timings

nameusersystemelapsed
STATegRaUsersGuide0.0000.0010.000
STATegRa_data0.1030.0030.105
STATegRa_data_TCGA_BRCA0.0000.0000.001
bioDist0.1560.0060.162
bioDistFeature0.1240.0050.130
bioDistFeaturePlot0.1330.0050.137
bioDistW0.1260.0050.136
bioDistWPlot0.1230.0050.134
bioMap0.0010.0000.002
combiningMappings0.0040.0010.005
createOmicsExpressionSet0.0520.0010.054
getInitialData0.2780.0080.285
getLoadings0.2720.0170.290
getMethodInfo0.3170.0020.319
getPreprocessing0.3180.1690.491
getScores0.3260.0160.341
getVAF0.2590.0020.263
holistOmics0.0000.0000.001
modelSelection0.6880.4421.133
omicsCompAnalysis1.9250.0051.936
omicsNPC0.0010.0010.002
plotRes2.3710.0072.389
plotVAF2.0830.0052.097