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This page was generated on 2026-05-07 11:36 -0400 (Thu, 07 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" 4990
kjohnson3macOS 13.7.7 Venturaarm644.6.0 Patched (2026-04-24 r89963) -- "Because it was There" 4723
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Package 2190/2418HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
STATegRa 1.48.0  (landing page)
David Gomez-Cabrero , Núria Planell
Snapshot Date: 2026-05-06 13:40 -0400 (Wed, 06 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: 2026-05-06 22:53:21 -0400 (Wed, 06 May 2026)
EndedAt: 2026-05-06 22:54:43 -0400 (Wed, 06 May 2026)
EllapsedTime: 81.9 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: 2026-05-07 02:53:22 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 -- Wed May  6 22:54:39 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 
  1.384   0.077   1.456

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 
 12.373   0.132  12.525

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
calcNormFactors has been renamed to normLibSizes
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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
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 
 19.851   0.173  20.077

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

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    0.0781574256 -0.0431499469
sample2   -0.1192218240  0.0294092336
sample3   -0.0531412300 -0.0746839957
sample4    0.0292975341 -0.0005955782
sample5    0.0202091822  0.0110464172
sample6    0.1226089087  0.1053465611
sample7    0.1078927930 -0.0322478985
sample8    0.1782895529  0.1449365925
sample9    0.0468698173 -0.0455174464
sample10  -0.0036030387  0.0420112889
sample11  -0.0035566476 -0.0566293026
sample12   0.1006128867  0.0641380058
sample13  -0.1174408109  0.0907489135
sample14   0.0981203240  0.0617736689
sample15   0.0085334148 -0.0087016765
sample16   0.0783148760  0.1581292257
sample17  -0.1483609880  0.0638582043
sample18  -0.0963086359  0.0556637440
sample19  -0.0217244141 -0.0720083775
sample20  -0.0635636545 -0.0779655578
sample21  -0.0201840137  0.1566391868
sample22   0.0218268448 -0.0764107193
sample23   0.0852042191 -0.0032684439
sample24  -0.1287170036  0.1924550315
sample25  -0.0430574062 -0.0456561995
sample26  -0.1453896676  0.0541515238
sample27  -0.0197489100 -0.1185660489
sample28  -0.1025336134  0.0650687282
sample29   0.0706018270 -0.0682991240
sample30  -0.1295627789 -0.0066774583
sample31   0.1147449109  0.1232684301
sample32  -0.0374311025  0.0380173837
sample33   0.0599515726  0.0136863242
sample34  -0.0984200902  0.0375318561
sample35  -0.0543098490 -0.0378109988
sample36   0.1403625283 -0.0343762888
sample37   0.0228941540 -0.0732855318
sample38  -0.0222077490 -0.0962596381
sample39  -0.0941738448  0.0215200268
sample40   0.0643800808 -0.0687879711
sample41  -0.0327638192 -0.1232188221
sample42  -0.0500431847 -0.0292471032
sample43  -0.0184498912  0.0233009133
sample44   0.1487899308  0.1171362630
sample45  -0.1050773914  0.1123205074
sample46  -0.1151195950 -0.1094030663
sample47  -0.0962593855 -0.0288466365
sample48   0.0004837596 -0.0310271984
sample49   0.1135208003  0.1213975073
sample50  -0.0123553297 -0.1740742558
sample51   0.0550530004  0.1258884128
sample52   0.0499121379  0.0728542404
sample53   0.1119773707  0.1588010930
sample54  -0.0360055673  0.0228574992
sample55   0.0210419007  0.0006730601
sample56  -0.0434169104  0.0633125741
sample57   0.0197824831  0.1150711208
sample58   0.0030439844  0.0326096260
sample59   0.0500252908  0.0129412794
sample60   0.0184278566  0.0136077508
sample61   0.0150299438  0.0635021622
sample62  -0.0304764193 -0.0201324201
sample63   0.1102252611  0.1285977499
sample64   0.1552588159  0.0971167376
sample65  -0.0058503008  0.0207116208
sample66  -0.0025605223  0.0424322021
sample67   0.1546634589 -0.0661724685
sample68   0.0536368985 -0.0923688488
sample69   0.0640330221  0.0081981849
sample70   0.0163517519 -0.0663230258
sample71  -0.0102537714 -0.1345918778
sample72  -0.0654196294 -0.0196124559
sample73  -0.1048556306  0.0220934552
sample74   0.0123799421  0.0586112594
sample75   0.0392077836 -0.0209756783
sample76   0.0648953307 -0.0524764727
sample77   0.1172922094 -0.0201185920
sample78  -0.1463067832  0.0708476827
sample79   0.0265211335 -0.1603300843
sample80   0.0279737035 -0.0214208232
sample81   0.0079211459 -0.0738448154
sample82  -0.1544236624 -0.0361468968
sample83  -0.0494211751 -0.0050055184
sample84  -0.0259038393 -0.0346547041
sample85   0.1116484164 -0.0031503448
sample86  -0.1306483272 -0.0377218811
sample87  -0.0554778223 -0.0459749524
sample88  -0.0301623693  0.0382196981
sample89  -0.1016866748  0.0694031094
sample90   0.0086819772 -0.0201319910
sample91   0.1578625079 -0.2097830142
sample92   0.0170937032 -0.1655798981
sample93  -0.0979806921 -0.0121512962
sample94   0.0131483972 -0.0114932239
sample95   0.0315682641 -0.0758855526
sample96   0.0024125606 -0.0470132505
sample97   0.0634545393  0.0270333642
sample98  -0.0359374761 -0.0135489794
sample99  -0.1009163067  0.1124784215
sample100  0.0551753071  0.0246488791
sample101 -0.0080119063 -0.1627366091
sample102 -0.0046443893  0.0095641855
sample103 -0.0472523316 -0.0940393906
sample104  0.0198159608 -0.0591088116
sample105 -0.0400237726 -0.0160909709
sample106 -0.0923808314  0.0369018587
sample107 -0.1019374041  0.0224953334
sample108 -0.0877091661 -0.0128833227
sample109  0.0864824642 -0.0900932948
sample110 -0.1223115482 -0.0096084287
sample111  0.0257354772 -0.0936162071
sample112 -0.0765286648  0.0270345556
sample113  0.0258803451  0.0377500946
sample114  0.0021138792 -0.0882013301
sample115  0.0303460508 -0.0723576206
sample116  0.0780508668 -0.0685060279
sample117  0.0536898355 -0.0911901030
sample118  0.0666651228 -0.0236229147
sample119  0.1021871582 -0.2324932488
sample120  0.0750216571  0.0243381258
sample121 -0.0756936308  0.0942949364
sample122 -0.0259627840  0.0731991366
sample123 -0.1037846355 -0.0369198400
sample124  0.0611208097  0.0421728522
sample125 -0.0738472724  0.0066950472
sample126  0.0972916277  0.0762636307
sample127  0.0824697524 -0.0096636827
sample128 -0.1249407406  0.0929316442
sample129 -0.0734067754 -0.0434366068
sample130 -0.0003502112 -0.0309852397
sample131  0.0930182736  0.0155935046
sample132  0.0736222950  0.0733033492
sample133 -0.0498397955 -0.0462436301
sample134  0.1644873542  0.0720003008
sample135 -0.0752297334  0.0003814464
sample136  0.0227145516 -0.0495508350
sample137  0.0564717145 -0.0288919264
sample138  0.0255988213 -0.0610852354
sample139  0.0621217736  0.0235804472
sample140 -0.0604152758 -0.0435597109
sample141  0.0246744026  0.0532649845
sample142 -0.0409560112  0.0316282751
sample143 -0.0077355152 -0.0476895490
sample144  0.0173240815 -0.0156777434
sample145  0.0485474935  0.1202772694
sample146  0.0419645366 -0.0811283863
sample147 -0.0977308584 -0.0274844453
sample148  0.0368256361  0.0803980304
sample149 -0.0072865849 -0.1532984052
sample150  0.1020825271  0.0624771587
sample151  0.0305399183 -0.0289274453
sample152 -0.0533594771 -0.0638307425
sample153 -0.0891626927  0.1799586359
sample154 -0.0727557662 -0.0834162909
sample155 -0.0880668735 -0.0220822716
sample156 -0.0276561182 -0.0326626786
sample157 -0.1155032228  0.0183614860
sample158 -0.0281507573 -0.0104940328
sample159  0.0663235827  0.0443839073
sample160 -0.0302643885  0.0404263196
sample161  0.0114715727 -0.0591021804
sample162 -0.1337086887  0.1398135774
sample163  0.1330124709  0.1688782060
sample164 -0.0150335968  0.0028418767
sample165  0.0076520360 -0.0164127285
sample166  0.0367794549  0.0630665079
sample167  0.1111988808  0.0030057316
sample168 -0.0672981519  0.0446280094
sample169 -0.0413005057  0.0224390916
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
                      1             2
sample1    0.0420518000  0.0867862910
sample2    0.0820827282 -0.0410978615
sample3   -0.0155895705 -0.0195182104
sample4    0.1001336760 -0.0410787333
sample5    0.0153465129 -0.0253259808
sample6   -0.0340331205 -0.0408223202
sample7   -0.0722578371  0.0002332767
sample8    0.0457492569 -0.0370016835
sample9    0.0086251318  0.0820184932
sample10   0.0423596602 -0.0083923644
sample11  -0.0022545827  0.0787766172
sample12  -0.0322107600  0.1479824760
sample13   0.0293884821 -0.0306748999
sample14  -0.0337485887 -0.0367506773
sample15  -0.0815537793  0.1275623039
sample16  -0.0508459628  0.0540604650
sample17  -0.0062599270  0.0041023637
sample18  -0.0705642247 -0.0351047336
sample19   0.0476845296 -0.0509598242
sample20  -0.0522958809  0.0715522366
sample21   0.0119118970 -0.0376093492
sample22  -0.0724389091 -0.0095624480
sample23   0.0992532321  0.0134288145
sample24   0.1595108985  0.0728660583
sample25   0.0920695360 -0.0749757766
sample26   0.0595538452  0.0848965553
sample27  -0.0826479980 -0.0086734638
sample28   0.0384785372  0.0440966494
sample29  -0.0777667191  0.1735309161
sample30  -0.1229471115 -0.0819004693
sample31  -0.0579852346 -0.0238644638
sample32  -0.0970394875 -0.0111425741
sample33  -0.1017588517 -0.0630441929
sample34  -0.0637924120  0.0377942058
sample35  -0.0789983285 -0.0229722626
sample36  -0.1224939708 -0.1274954087
sample37  -0.1798819146 -0.1673426106
sample38  -0.0466298901  0.0888161457
sample39   0.0168687215  0.0421533608
sample40  -0.1756390518 -0.1526641072
sample41  -0.0042364624  0.0004929091
sample42   0.0447851208 -0.0651505230
sample43  -0.0482309457 -0.0253529002
sample44   0.1986708215 -0.0545779430
sample45   0.0741831327  0.0054702571
sample46  -0.0478766488 -0.0007071465
sample47  -0.0608186749  0.0481623103
sample48   0.1381490979  0.0578286929
sample49   0.0530513451 -0.1405533444
sample50   0.0173809490  0.1602389922
sample51  -0.0462567595  0.0303473867
sample52  -0.0280069379  0.0280388413
sample53  -0.0667629151  0.0237702139
sample54  -0.0121834947 -0.0521354288
sample55  -0.0182396143  0.0221328547
sample56   0.0001252194  0.0030907224
sample57  -0.0316681821  0.0530190236
sample58  -0.0393919890 -0.0297798555
sample59  -0.1278292159 -0.0546527144
sample60  -0.1486986385  0.1069157494
sample61  -0.0793125969  0.0569796905
sample62  -0.1172799705 -0.0149197668
sample63   0.0028721344  0.1300519539
sample64  -0.0237369009  0.1073287660
sample65   0.0126534363  0.0589808312
sample66   0.0468192295 -0.0771073089
sample67  -0.1494263230 -0.0769859194
sample68  -0.0977957246 -0.0577350220
sample69  -0.0403087099  0.0156042367
sample70  -0.0221527032  0.0315441250
sample71   0.0546441244 -0.0272396511
sample72  -0.1107486962 -0.0537318599
sample73  -0.0906761535  0.0579967167
sample74  -0.0586558073  0.0121421937
sample75  -0.0390492030  0.0349283112
sample76   0.0022962047 -0.1676558703
sample77   0.0232095862 -0.2067302912
sample78   0.0929751446 -0.0434940237
sample79   0.1619504199 -0.0378114966
sample80  -0.0680363765  0.1424663983
sample81   0.0530787801 -0.0358351037
sample82  -0.0266820144 -0.0577444842
sample83  -0.1517234762 -0.0448553324
sample84   0.0570968643 -0.0273813541
sample85  -0.1086290875 -0.1228118622
sample86  -0.0833857916 -0.0442914345
sample87  -0.0022016978 -0.0943906743
sample88   0.0078221576 -0.1140506670
sample89  -0.0611060538 -0.0094584890
sample90  -0.0022927245 -0.0936253930
sample91  -0.0433578769  0.3205983496
sample92   0.1815343060 -0.0334681137
sample93  -0.0267629256  0.0614429238
sample94  -0.0181876247  0.0605090556
sample95   0.0720379652 -0.0013045965
sample96   0.0559717478 -0.0118791679
sample97   0.0217410403  0.0195413949
sample98  -0.0379175985  0.0588357389
sample99   0.0792422098 -0.0151274547
sample100 -0.0222117459 -0.0023321340
sample101  0.0387237872  0.1224226313
sample102  0.2094613404 -0.0516444008
sample103 -0.0138476287  0.0301052246
sample104  0.0807989525 -0.0162719326
sample105  0.0520493436 -0.1229665468
sample106  0.0192611389 -0.0185238384
sample107 -0.0319017334  0.0405123462
sample108  0.0140692076  0.0163421325
sample109  0.1831934815  0.0613006548
sample110  0.0292791107 -0.0199849245
sample111  0.1423257062  0.0327339598
sample112 -0.0426334107 -0.0029083214
sample113  0.0771902765  0.0268733061
sample114  0.0241646145 -0.0184080382
sample115  0.1959019420  0.0460129551
sample116  0.1394478467 -0.0530806583
sample117  0.1672365005 -0.1386537307
sample118  0.0448345092 -0.0117622182
sample119  0.0910398825  0.2217433269
sample120  0.0331391395 -0.0057274762
sample121 -0.0307578616  0.1392506550
sample122  0.0839777519 -0.0291995120
sample123 -0.0239648812 -0.0642163484
sample124  0.0909148966  0.0130418821
sample125  0.0065350104 -0.1092631864
sample126 -0.0935314317  0.1368284496
sample127 -0.0035386748  0.0292755681
sample128  0.0660291962  0.1018565709
sample129 -0.0693636500 -0.0695421188
sample130 -0.0008491920 -0.0669704252
sample131 -0.0431024792  0.0174065109
sample132  0.0637036961  0.0029374155
sample133  0.0289496722 -0.0390818921
sample134 -0.0446206956  0.0456334630
sample135 -0.0712336527  0.0521635423
sample136 -0.0596267927  0.0197299819
sample137 -0.0793150453 -0.0380627728
sample138  0.0973551099 -0.0454218767
sample139 -0.0539907244 -0.1534327069
sample140 -0.0850824074  0.0955815181
sample141  0.0192679093 -0.0554450303
sample142  0.0672259914 -0.0461321418
sample143  0.0303731937 -0.0519260340
sample144  0.0089365496  0.0145814889
sample145  0.0638763030  0.0122257728
sample146 -0.0585852022  0.0063083901
sample147 -0.0894132480 -0.1124615055
sample148  0.0216362245 -0.0615967440
sample149  0.0515427449 -0.0839903508
sample150 -0.0568286879 -0.0124468693
sample151  0.0789533608 -0.0261831639
sample152  0.0330757397  0.1306443506
sample153  0.1751922611  0.1497730585
sample154 -0.0421420096 -0.0037009741
sample155 -0.0680176113  0.0095711725
sample156 -0.0388908596  0.1057563285
sample157 -0.0314769681  0.0561367598
sample158 -0.0329619903  0.0353947559
sample159  0.0398413636 -0.1007374129
sample160 -0.0424940873  0.0108496367
sample161  0.0888373330 -0.0679700629
sample162  0.0027469787  0.1237843558
sample163  0.0126097220  0.0725433901
sample164  0.0566779177 -0.0458324555
sample165  0.0315336713 -0.0236362523
sample166  0.0612054818 -0.0425233567
sample167 -0.0142729891  0.0179308357
sample168  0.0169500923 -0.0769618096
sample169 -0.0675081444  0.0131505722
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
                     1             2
sample1    0.001232955 -1.635717e-01
sample2    0.072434992 -6.021215e-03
sample3    0.018846047 -1.080036e-01
sample4   -0.039014539  3.114527e-04
sample5   -0.177481169 -2.996382e-02
sample6    0.045144433 -3.455856e-02
sample7    0.022646638 -7.020210e-03
sample8    0.103368003 -9.856722e-03
sample9   -0.135001162  8.979097e-02
sample10  -0.125988739 -5.097848e-02
sample11  -0.097978824  7.086533e-02
sample12   0.086301901 -8.620317e-02
sample13   0.138140108  1.828008e-01
sample14   0.061507382 -2.642802e-02
sample15  -0.038159887 -3.101667e-02
sample16   0.004877660  1.271884e-03
sample17   0.078848084 -1.547551e-02
sample18   0.088418872 -3.795487e-02
sample19  -0.070304446 -1.084004e-01
sample20   0.002558573  7.975870e-02
sample21  -0.094160190 -4.126733e-02
sample22   0.055027354 -7.806750e-02
sample23  -0.067949543 -4.102002e-02
sample24   0.131096247  1.649311e-01
sample25  -0.011358536 -4.426861e-02
sample26   0.140294577  2.016547e-02
sample27  -0.026156089  1.588365e-03
sample28   0.072419862  5.850597e-02
sample29   0.033005876  2.060761e-03
sample30   0.022875267 -2.015434e-02
sample31   0.063506782 -6.670332e-02
sample32  -0.068509963 -4.955274e-02
sample33   0.077776523 -1.272079e-01
sample34  -0.015784243 -3.024314e-02
sample35   0.052963300  1.500971e-01
sample36  -0.007090041  2.025307e-01
sample37   0.044242103  1.802088e-01
sample38   0.078151144 -3.676427e-02
sample39  -0.012033194 -3.388839e-02
sample40   0.047329248  1.471560e-01
sample41  -0.022818927 -2.673560e-02
sample42   0.024536017 -7.960866e-02
sample43  -0.103636285 -8.229577e-02
sample44   0.101222850  7.049462e-02
sample45  -0.001373230 -2.450903e-02
sample46   0.055851020  2.947320e-03
sample47   0.038048130  4.554170e-02
sample48  -0.078434217  4.888985e-02
sample49   0.060516376 -1.162349e-02
sample50  -0.053007909 -2.737939e-02
sample51  -0.151464659  5.678349e-02
sample52  -0.186093520  1.246718e-01
sample53   0.006417697 -2.700990e-02
sample54  -0.069703838 -2.308387e-02
sample55  -0.163357700  1.366442e-02
sample56  -0.101148516  4.682209e-02
sample57  -0.173037420  1.609604e-01
sample58   0.007138470 -1.666955e-02
sample59   0.003046184  3.005281e-02
sample60  -0.021583479  2.665877e-01
sample61  -0.151058355  1.002385e-01
sample62   0.092553407 -4.845848e-02
sample63   0.059631161 -4.137018e-02
sample64   0.044922572 -2.600560e-03
sample65  -0.093938384 -4.406907e-02
sample66  -0.106340090 -5.709988e-02
sample67   0.020159047  2.361727e-01
sample68  -0.003720295  2.418381e-02
sample69   0.064516117 -1.155622e-01
sample70   0.101344003 -1.351789e-01
sample71   0.001646796 -2.976845e-02
sample72  -0.032889290 -2.835862e-02
sample73  -0.027508003 -5.148188e-02
sample74  -0.134171975 -7.895279e-02
sample75  -0.095157561 -3.943186e-02
sample76   0.086472206  3.034988e-02
sample77   0.103574957 -2.545355e-02
sample78   0.157564395  4.939600e-02
sample79  -0.018913702  4.874678e-02
sample80  -0.138414048  4.262658e-05
sample81   0.011884645 -6.357933e-02
sample82   0.167530825  3.533908e-02
sample83   0.006567352 -7.812616e-02
sample84  -0.148689166 -3.109055e-02
sample85   0.053272462  7.417879e-02
sample86   0.113847746 -1.920477e-05
sample87  -0.043286388  6.080471e-02
sample88  -0.043345033  1.402491e-01
sample89  -0.033120583 -1.395399e-02
sample90   0.060741278 -8.610416e-02
sample91   0.056627308  1.303746e-01
sample92   0.035958259  1.061604e-01
sample93   0.043364636 -4.443636e-02
sample94   0.047729126 -1.059574e-01
sample95   0.024959576 -3.980526e-02
sample96  -0.003521908 -9.293928e-02
sample97   0.006604859 -1.527230e-01
sample98  -0.002036680 -5.579552e-02
sample99   0.088661581 -3.728218e-02
sample100  0.109125911 -3.560421e-02
sample101  0.073972662 -4.318005e-02
sample102 -0.057446143 -2.783905e-02
sample103 -0.014273088  9.705516e-03
sample104 -0.071039519  4.068352e-02
sample105 -0.098083142 -3.452950e-02
sample106  0.025425925  3.628986e-02
sample107  0.016065337 -9.173394e-02
sample108  0.020098762 -2.379692e-02
sample109  0.038978059  1.692361e-02
sample110  0.032630483  2.988111e-02
sample111 -0.067693765 -6.038211e-02
sample112 -0.016788343  5.336939e-03
sample113 -0.096921717 -2.757598e-02
sample114  0.002639837 -9.209159e-02
sample115  0.030804720  1.603827e-02
sample116  0.124030721  1.273000e-01
sample117 -0.033472910  5.392713e-02
sample118  0.103715296  6.252430e-02
sample119  0.106417696  1.196202e-01
sample120  0.077135496 -1.004932e-01
sample121  0.012935067  3.181979e-02
sample122 -0.084749253 -5.568319e-02
sample123  0.004133684  7.693160e-03
sample124  0.058345779 -8.396385e-02
sample125 -0.063484467 -5.232539e-02
sample126  0.066258089 -1.091733e-01
sample127  0.086502455 -1.094176e-01
sample128  0.062781720 -1.470957e-02
sample129  0.033627655 -4.007863e-02
sample130  0.029351773 -8.046119e-02
sample131  0.046919771 -2.209769e-03
sample132  0.024174043 -1.248598e-01
sample133 -0.090730318  1.466701e-02
sample134  0.035084212  7.539662e-02
sample135 -0.000133333  9.185355e-03
sample136  0.033587612 -9.860279e-02
sample137  0.064014899 -7.554475e-02
sample138 -0.006096488 -1.742761e-02
sample139  0.059208455  5.614967e-02
sample140 -0.042798579 -1.099555e-02
sample141 -0.061879654 -9.301034e-02
sample142 -0.089855457  3.573422e-02
sample143 -0.081738912  8.880524e-02
sample144 -0.078775480 -3.821391e-02
sample145 -0.108582169  1.569477e-01
sample146  0.058955809 -4.373366e-02
sample147  0.049533057  7.277155e-03
sample148 -0.116159291  9.079134e-03
sample149  0.012157966  7.788369e-02
sample150  0.031451257  3.520212e-02
sample151 -0.057538224 -1.945350e-02
sample152  0.049454219  7.025536e-02
sample153  0.094133244  2.153299e-01
sample154  0.033593217  2.078723e-02
sample155 -0.069045758 -2.780412e-02
sample156 -0.103990159 -6.292527e-02
sample157  0.040864578  8.065514e-03
sample158 -0.101810525  7.816868e-03
sample159  0.028173044 -1.207203e-02
sample160 -0.164305302  2.978120e-03
sample161 -0.037432921  8.524612e-02
sample162  0.080453517  8.349761e-02
sample163  0.074322775 -1.406218e-02
sample164 -0.120880610 -2.139457e-02
sample165 -0.160811591  2.025194e-02
sample166  0.042594447 -2.660710e-02
sample167  0.022684948 -4.464283e-02
sample168  0.018073549 -7.465912e-04
sample169 -0.019077894  2.645401e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
                   [,1]          [,2]
sample1   -0.0572060227 -1.729087e-02
sample2    0.0875245208  1.112588e-02
sample3    0.0403482602 -3.168994e-02
sample4   -0.0218345996  4.052760e-06
sample5   -0.0150905011  4.795041e-03
sample6   -0.0924362933  4.511003e-02
sample7   -0.0793066751 -1.243823e-02
sample8   -0.1342997187  6.215220e-02
sample9   -0.0338886944 -1.854401e-02
sample10   0.0020547173  1.749421e-02
sample11   0.0037275602 -2.364116e-02
sample12  -0.0753094533  2.772698e-02
sample13   0.0856160091  3.679963e-02
sample14  -0.0737457307  2.668452e-02
sample15  -0.0062111746 -3.554864e-03
sample16  -0.0602355268  6.675115e-02
sample17   0.1086768843  2.524534e-02
sample18   0.0702999472  2.231671e-02
sample19   0.0173785882 -3.024846e-02
sample20   0.0484173812 -3.310904e-02
sample21   0.0124657042  6.517144e-02
sample22  -0.0140989936 -3.159137e-02
sample23  -0.0627028403 -5.393710e-04
sample24   0.0919972100  7.909297e-02
sample25   0.0326998483 -1.945206e-02
sample26   0.1064741246  2.120849e-02
sample27   0.0166058995 -4.964993e-02
sample28   0.0743504770  2.614211e-02
sample29  -0.0511008491 -2.782647e-02
sample30   0.0962250842 -3.974893e-03
sample31  -0.0869563008  5.250819e-02
sample32   0.0271858919  1.552005e-02
sample33  -0.0448364581  6.243160e-03
sample34   0.0718415218  1.469396e-02
sample35   0.0403086451 -1.632629e-02
sample36  -0.1036402827 -1.304320e-02
sample37  -0.0159385744 -3.036525e-02
sample38   0.0182198369 -4.034805e-02
sample39   0.0690363619  8.058350e-03
sample40  -0.0467312750 -2.810325e-02
sample41   0.0263674438 -5.171216e-02
sample42   0.0374578960 -1.268634e-02
sample43   0.0132336869  9.536642e-03
sample44  -0.1119154428  5.028683e-02
sample45   0.0759639367  4.587903e-02
sample46   0.0871885519 -4.670385e-02
sample47   0.0721490571 -1.288540e-02
sample48   0.0005086144 -1.290565e-02
sample49  -0.0858177028  5.173760e-02
sample50   0.0118992665 -7.276215e-02
sample51  -0.0426446855  5.306205e-02
sample52  -0.0381605826  3.086785e-02
sample53  -0.0855757630  6.730043e-02
sample54   0.0261723092  9.184260e-03
sample55  -0.0156418304  4.682404e-04
sample56   0.0307831193  2.597550e-02
sample57  -0.0157242103  4.829381e-02
sample58  -0.0031174404  1.359898e-02
sample59  -0.0373001859  5.868397e-03
sample60  -0.0142609099  5.831654e-03
sample61  -0.0122255144  2.663579e-02
sample62   0.0228002942 -8.692265e-03
sample63  -0.0833127581  5.473229e-02
sample64  -0.1166548159  4.196500e-02
sample65   0.0038808902  8.568590e-03
sample66   0.0011561811  1.766612e-02
sample67  -0.1129311062 -2.608702e-02
sample68  -0.0382526429 -3.804045e-02
sample69  -0.0476502440  4.003241e-03
sample70  -0.0110329882 -2.752719e-02
sample71   0.0096850282 -5.627056e-02
sample72   0.0487124704 -8.800131e-03
sample73   0.0773058132  8.239864e-03
sample74  -0.0102488176  2.454957e-02
sample75  -0.0286613976 -8.387293e-03
sample76  -0.0472655595 -2.129315e-02
sample77  -0.0865043074 -7.296820e-03
sample78   0.1070293698  2.818346e-02
sample79  -0.0165060681 -6.659721e-02
sample80  -0.0206765949 -8.712112e-03
sample81  -0.0050943615 -3.079175e-02
sample82   0.1153622361 -1.647054e-02
sample83   0.0367979217 -2.538114e-03
sample84   0.0199463070 -1.468961e-02
sample85  -0.0827122185 -2.709824e-04
sample86   0.0969487314 -1.699897e-02
sample87   0.0421957457 -1.965953e-02
sample88   0.0215934743  1.566050e-02
sample89   0.0751559502  2.811652e-02
sample90  -0.0057328000 -8.283795e-03
sample91  -0.1134005268 -8.603522e-02
sample92  -0.0101689918 -6.894992e-02
sample93   0.0725967502 -6.003176e-03
sample94  -0.0096878852 -4.693081e-03
sample95  -0.0223502239 -3.139636e-02
sample96  -0.0013232863 -1.963604e-02
sample97  -0.0476541710  1.183660e-02
sample98   0.0269546160 -5.978398e-03
sample99   0.0728179461  4.597884e-02
sample100 -0.0413398038  1.079347e-02
sample101  0.0087536994 -6.796076e-02
sample102  0.0032509529  3.932612e-03
sample103  0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105  0.0294940465 -7.140722e-03
sample106  0.0686472054  1.462895e-02
sample107  0.0748635927  8.401339e-03
sample108  0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110  0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112  0.0570870472  1.066018e-02
sample113 -0.0200110554  1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586  1.089843e-02
sample121  0.0542443861  3.861344e-02
sample122  0.0178575357  3.027138e-02
sample123  0.0775020581 -1.636852e-02
sample124 -0.0460701050  1.814758e-02
sample125  0.0543846585  2.075898e-03
sample126 -0.0729417144  3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128  0.0908136899  3.758801e-02
sample129  0.0552445878 -1.879062e-02
sample130  0.0007128089 -1.294308e-02
sample131 -0.0693311345  7.357082e-03
sample132 -0.0556565156  3.126995e-02
sample133  0.0375870104 -1.977240e-02
sample134 -0.1229130924  3.159495e-02
sample135  0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201  1.040683e-02
sample140  0.0452288969 -1.876279e-02
sample141 -0.0189142561  2.247042e-02
sample142  0.0297545566  1.280524e-02
sample143  0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491  5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147  0.0726083535 -1.239968e-02
sample148 -0.0284795794  3.389732e-02
sample149  0.0082261455 -6.399305e-02
sample150 -0.0765013197  2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152  0.0403422737 -2.714879e-02
sample153  0.0629117719  7.425085e-02
sample154  0.0551622927 -3.548984e-02
sample155  0.0654439133 -1.005306e-02
sample156  0.0209310714 -1.390213e-02
sample157  0.0851522597  6.577150e-03
sample158  0.0208354599 -4.663078e-03
sample159 -0.0498794349  1.913257e-02
sample160  0.0216074437  1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162  0.0963663017  5.705881e-02
sample163 -0.1009542191  7.174224e-02
sample164  0.0109881996  1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357  2.673084e-02
sample167 -0.0825048036  2.278863e-03
sample168  0.0486147429  1.793843e-02
sample169  0.0302506727  8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
                   [,1]          [,2]
sample1   -0.0621842115 -1.364509e-02
sample2    0.0944623785  9.720892e-03
sample3    0.0406196267 -2.236338e-02
sample4   -0.0229316496 -3.932487e-04
sample5   -0.0157330047  3.231033e-03
sample6   -0.0945794025  3.120720e-02
sample7   -0.0854427118 -1.052880e-02
sample8   -0.1376625920  4.286608e-02
sample9   -0.0377115311 -1.415134e-02
sample10   0.0035244506  1.280825e-02
sample11   0.0016639987 -1.717895e-02
sample12  -0.0781403168  1.884368e-02
sample13   0.0938400516  2.838858e-02
sample14  -0.0759839772  1.810989e-02
sample15  -0.0068340837 -2.705361e-03
sample16  -0.0590150849  4.757848e-02
sample17   0.1178805097  2.040526e-02
sample18   0.0767858320  1.756604e-02
sample19   0.0157112113 -2.172867e-02
sample20   0.0485318300 -2.327033e-02
sample21   0.0185928176  4.777095e-02
sample22  -0.0191358702 -2.329775e-02
sample23  -0.0672994194 -1.535656e-03
sample24   0.1047476642  5.935707e-02
sample25   0.0329844953 -1.358036e-02
sample26   0.1154952052  1.741529e-02
sample27   0.0133849853 -3.590922e-02
sample28   0.0821554039  2.042376e-02
sample29  -0.0567643690 -2.123848e-02
sample30   0.1016073931 -1.134728e-03
sample31  -0.0880396372  3.670548e-02
sample32   0.0300363338  1.182406e-02
sample33  -0.0467252272  3.739254e-03
sample34   0.0783666394  1.203777e-02
sample35   0.0424227097 -1.118559e-02
sample36  -0.1107646166 -1.143464e-02
sample37  -0.0191667664 -2.246060e-02
sample38   0.0155968095 -2.909621e-02
sample39   0.0746847148  7.148218e-03
sample40  -0.0517028178 -2.137267e-02
sample41   0.0234979494 -3.723018e-02
sample42   0.0388797356 -8.557228e-03
sample43   0.0149555568  7.210002e-03
sample44  -0.1150305613  3.461805e-02
sample45   0.0846146236  3.486020e-02
sample46   0.0884426404 -3.246853e-02
sample47   0.0748644971 -8.083045e-03
sample48  -0.0012033198 -9.403647e-03
sample49  -0.0872662737  3.616245e-02
sample50   0.0066941314 -5.284863e-02
sample51  -0.0411777630  3.791830e-02
sample52  -0.0379355780  2.180834e-02
sample53  -0.0851639886  4.751761e-02
sample54   0.0288006248  7.184424e-03
sample55  -0.0164920835  5.919925e-05
sample56   0.0355115616  1.951043e-02
sample57  -0.0141146068  3.492409e-02
sample58  -0.0015636132  9.862883e-03
sample59  -0.0390656483  3.590929e-03
sample60  -0.0139454780  3.963030e-03
sample61  -0.0106410274  1.919705e-02
sample62   0.0236748439 -5.922677e-03
sample63  -0.0846790877  3.839102e-02
sample64  -0.1202581015  2.846469e-02
sample65   0.0050548584  6.328644e-03
sample66   0.0028013072  1.291807e-02
sample67  -0.1231623009 -2.112565e-02
sample68  -0.0437782161 -2.845072e-02
sample69  -0.0501199692  2.053469e-03
sample70  -0.0140278645 -2.027157e-02
sample71   0.0057489505 -4.085977e-02
sample72   0.0511212704 -5.522408e-03
sample73   0.0828141409  7.431582e-03
sample74  -0.0085959456  1.772951e-02
sample75  -0.0312180394 -6.636869e-03
sample76  -0.0519051781 -1.640191e-02
sample77  -0.0925924762 -6.907800e-03
sample78   0.1163971046  2.251122e-02
sample79  -0.0240906926 -4.887766e-02
sample80  -0.0221327065 -6.730703e-03
sample81  -0.0072114968 -2.254399e-02
sample82   0.1204416674 -9.907422e-03
sample83   0.0386739485 -1.171663e-03
sample84   0.0195988488 -1.033806e-02
sample85  -0.0877680171 -1.725057e-03
sample86   0.1023541048 -1.062501e-02
sample87   0.0425213089 -1.356865e-02
sample88   0.0244788514  1.180820e-02
sample89   0.0804276691  2.188588e-02
sample90  -0.0074639871 -6.140721e-03
sample91  -0.1278832404 -6.485140e-02
sample92  -0.0162199697 -5.048358e-02
sample93   0.0769344893 -3.045135e-03
sample94  -0.0104345587 -3.593172e-03
sample95  -0.0260058453 -2.330475e-02
sample96  -0.0025018700 -1.433516e-02
sample97  -0.0492358305  7.774183e-03
sample98   0.0279220220 -3.862141e-03
sample99   0.0813921923  3.487339e-02
sample100 -0.0428797405  7.112807e-03
sample101  0.0032855240 -4.940743e-02
sample102  0.0038439317  2.938008e-03
sample103  0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105  0.0314853405 -4.656633e-03
sample106  0.0726456731  1.192390e-02
sample107  0.0807342975  7.508627e-03
sample108  0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110  0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112  0.0599954082  8.820317e-03
sample113 -0.0195006577  1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131  6.937326e-03
sample121  0.0613899126  2.915307e-02
sample122  0.0218424338  2.241775e-02
sample123  0.0809008460 -1.051759e-02
sample124 -0.0472109313  1.239887e-02
sample125  0.0583180947  2.521167e-03
sample126 -0.0753941872  2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128  0.1000212216  2.908091e-02
sample129  0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877  4.091672e-03
sample132 -0.0566219024  2.179861e-02
sample133  0.0384172955 -1.372840e-02
sample134 -0.1280862736  2.077912e-02
sample135  0.0592633273  6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861  6.729304e-03
sample140  0.0468926306 -1.285498e-02
sample141 -0.0186248693  1.605439e-02
sample142  0.0328031246  9.887746e-03
sample143  0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310  3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147  0.0765025670 -7.714769e-03
sample148 -0.0276016641  2.420589e-02
sample149  0.0027545308 -4.653007e-02
sample150 -0.0792296010  1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152  0.0409796547 -1.907063e-02
sample153  0.0734301757  5.528780e-02
sample154  0.0557740684 -2.487723e-02
sample155  0.0689436560 -6.127635e-03
sample156  0.0212272938 -9.747423e-03
sample157  0.0911931194  6.355708e-03
sample158  0.0220840645 -3.016357e-03
sample159 -0.0513244242  1.304175e-02
sample160  0.0246213576  1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162  0.1078802043  4.337260e-02
sample163 -0.1017965082  5.047171e-02
sample164  0.0119430799  9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180  1.899222e-02
sample167 -0.0872832229  1.516582e-04
sample168  0.0540714512  1.397701e-02
sample169  0.0328432652  7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
                   [,1]          [,2]
sample1    0.0133684846  2.195848e-02
sample2    0.0254157197 -1.058416e-02
sample3   -0.0049551479 -4.840017e-03
sample4    0.0310390570 -1.063929e-02
sample5    0.0046941318 -6.488426e-03
sample6   -0.0107406753 -1.026702e-02
sample7   -0.0225157631  2.624712e-04
sample8    0.0141320952 -9.505821e-03
sample9    0.0029681280  2.078210e-02
sample10   0.0131729174 -2.275042e-03
sample11  -0.0004164298  1.994019e-02
sample12  -0.0095211620  3.759883e-02
sample13   0.0091018604 -7.953956e-03
sample14  -0.0106557524 -9.181659e-03
sample15  -0.0249924121  3.262724e-02
sample16  -0.0156216400  1.375700e-02
sample17  -0.0019382446  1.073994e-03
sample18  -0.0221072481 -8.703592e-03
sample19   0.0146917619 -1.311712e-02
sample20  -0.0160353760  1.826290e-02
sample21   0.0035947899 -9.616341e-03
sample22  -0.0225060762 -2.532589e-03
sample23   0.0310000683  3.033060e-03
sample24   0.0499544372  1.809450e-02
sample25   0.0284442301 -1.932558e-02
sample26   0.0188220043  2.146985e-02
sample27  -0.0257763219 -1.999228e-03
sample28   0.0120888648  1.125834e-02
sample29  -0.0236482520  4.426726e-02
sample30  -0.0385486305 -2.055935e-02
sample31  -0.0181539336 -5.877838e-03
sample32  -0.0302630460 -2.607192e-03
sample33  -0.0319565715 -1.562628e-02
sample34  -0.0197970124  9.906813e-03
sample35  -0.0247412713 -5.434440e-03
sample36  -0.0386259060 -3.190394e-02
sample37  -0.0566199273 -4.192574e-02
sample38  -0.0142060273  2.259644e-02
sample39   0.0053589035  1.076485e-02
sample40  -0.0552546493 -3.819896e-02
sample41  -0.0013089975  9.278818e-05
sample42   0.0137252142 -1.664652e-02
sample43  -0.0151259626 -6.290953e-03
sample44   0.0617391754 -1.442883e-02
sample45   0.0231410886  1.163143e-03
sample46  -0.0148898209 -1.384176e-04
sample47  -0.0187252536  1.221690e-02
sample48   0.0432839432  1.416671e-02
sample49   0.0160818605 -3.588745e-02
sample50   0.0059333545  4.067003e-02
sample51  -0.0142914866  7.776270e-03
sample52  -0.0086339952  7.208917e-03
sample53  -0.0207386980  6.272432e-03
sample54  -0.0039856719 -1.316934e-02
sample55  -0.0056217017  5.692315e-03
sample56   0.0000123292  8.978290e-04
sample57  -0.0095805555  1.324253e-02
sample58  -0.0124160295 -7.326376e-03
sample59  -0.0400195442 -1.349736e-02
sample60  -0.0460063358  2.770091e-02
sample61  -0.0245266456  1.470710e-02
sample62  -0.0366022783 -3.437352e-03
sample63   0.0013742171  3.288796e-02
sample64  -0.0070599859  2.739588e-02
sample65   0.0041201911  1.498268e-02
sample66   0.0143173351 -1.968812e-02
sample67  -0.0467477531 -1.929938e-02
sample68  -0.0306751978 -1.436184e-02
sample69  -0.0125317217  4.130407e-03
sample70  -0.0068071487  8.080857e-03
sample71   0.0169170264 -7.027348e-03
sample72  -0.0346909749 -1.333770e-02
sample73  -0.0280506153  1.493843e-02
sample74  -0.0182611498  3.294697e-03
sample75  -0.0120563964  8.974612e-03
sample76   0.0001437236 -4.253184e-02
sample77   0.0065330299 -5.252886e-02
sample78   0.0288278141 -1.127782e-02
sample79   0.0503961481 -1.023318e-02
sample80  -0.0207693429  3.648391e-02
sample81   0.0163562768 -9.074596e-03
sample82  -0.0084317129 -1.478976e-02
sample83  -0.0474097918 -1.103126e-02
sample84   0.0177181395 -7.191197e-03
sample85  -0.0342718548 -3.082360e-02
sample86  -0.0261671791 -1.089491e-02
sample87  -0.0009486358 -2.411514e-02
sample88   0.0020528931 -2.894615e-02
sample89  -0.0189361111 -2.638639e-03
sample90  -0.0009863658 -2.390075e-02
sample91  -0.0124352695  8.153234e-02
sample92   0.0564264106 -8.909537e-03
sample93  -0.0081461774  1.570851e-02
sample94  -0.0054896581  1.547251e-02
sample95   0.0224073150 -4.374348e-04
sample96   0.0173528924 -3.050441e-03
sample97   0.0067948115  5.008237e-03
sample98  -0.0116030825  1.498764e-02
sample99   0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101  0.0124923691  3.091503e-02
sample102  0.0650835386 -1.367400e-02
sample103 -0.0042741828  7.855985e-03
sample104  0.0250591040 -4.171938e-03
sample105  0.0157516368 -3.121990e-02
sample106  0.0060593853 -5.101693e-03
sample107 -0.0098329626  1.044506e-02
sample108  0.0044269853  4.142036e-03
sample109  0.0572473486  1.517542e-02
sample110  0.0090474827 -5.119868e-03
sample111  0.0444263015  7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113  0.0241047399  6.706740e-03
sample114  0.0074558775 -4.728652e-03
sample115  0.0611851433  1.117210e-02
sample116  0.0432646951 -1.380556e-02
sample117  0.0516750066 -3.575617e-02
sample118  0.0139942100 -3.279138e-03
sample119  0.0291722987  5.587946e-02
sample120  0.0103515853 -1.690016e-03
sample121 -0.0091396331  3.552116e-02
sample122  0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124  0.0283466326  3.127845e-03
sample125  0.0016472378 -2.770692e-02
sample126 -0.0286529417  3.489336e-02
sample127 -0.0010224500  7.483214e-03
sample128  0.0209049296  2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968  4.637390e-03
sample132  0.0198526786  5.723983e-04
sample133  0.0088812957 -9.988115e-03
sample134 -0.0137484514  1.172591e-02
sample135 -0.0220314568  1.347465e-02
sample136 -0.0185173353  5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138  0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762  2.456863e-02
sample141  0.0058369763 -1.420854e-02
sample142  0.0207886071 -1.188764e-02
sample143  0.0092832598 -1.324238e-02
sample144  0.0028442140  3.627979e-03
sample145  0.0199749569  2.862202e-03
sample146 -0.0182236697  1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148  0.0065435868 -1.572917e-02
sample149  0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151  0.0245166984 -6.888241e-03
sample152  0.0107259913  3.314630e-02
sample153  0.0550963965  3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574  2.642246e-03
sample156 -0.0117803505  2.698265e-02
sample157 -0.0096167165  1.433840e-02
sample158 -0.0101754772  9.137620e-03
sample159  0.0120662931 -2.565236e-02
sample160 -0.0132238202  2.916023e-03
sample161  0.0274491966 -1.748284e-02
sample162  0.0012482909  3.152261e-02
sample163  0.0042031315  1.830701e-02
sample164  0.0174896157 -1.175915e-02
sample165  0.0097517662 -6.119019e-03
sample166  0.0190134679 -1.121582e-02
sample167 -0.0044140836  4.665585e-03
sample168  0.0049689168 -1.941822e-02
sample169 -0.0209802098  3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
                   [,1]          [,2]
sample1   -0.0515543627 -0.0305856787
sample2   -0.0144993256  0.0236342950
sample3   -0.0371833108 -0.0140263348
sample4    0.0068945388 -0.0132539692
sample5    0.0215035333 -0.0663338101
sample6   -0.0187055152  0.0088773016
sample7   -0.0061521552  0.0064029054
sample8   -0.0210874459  0.0334652901
sample9    0.0516865043 -0.0291142799
sample10   0.0059440366 -0.0527217447
sample11   0.0393010793 -0.0200624712
sample12  -0.0420837100  0.0131331362
sample13   0.0333252565  0.0818552509
sample14  -0.0190062644  0.0160202175
sample15  -0.0030968049 -0.0189230681
sample16  -0.0004452158  0.0018880102
sample17  -0.0185848615  0.0240170131
sample18  -0.0273093598  0.0230213640
sample19  -0.0217761111 -0.0445894441
sample20   0.0245820821  0.0159812738
sample21   0.0034527644 -0.0400016054
sample22  -0.0340789054  0.0039289109
sample23  -0.0010344929 -0.0310161212
sample24   0.0289468503  0.0760962436
sample25  -0.0119098496 -0.0122798760
sample26  -0.0181001057  0.0517892852
sample27   0.0050465417 -0.0086515844
sample28   0.0057491502  0.0358830107
sample29  -0.0051104246  0.0116605117
sample30  -0.0103085904  0.0039678538
sample31  -0.0319929858  0.0090606113
sample32  -0.0036232521 -0.0328202010
sample33  -0.0534742153  0.0024751837
sample34  -0.0067495749 -0.0111000311
sample35   0.0378745721  0.0465929296
sample36   0.0647886800  0.0359987924
sample37   0.0488441236  0.0492906912
sample38  -0.0251514062  0.0197110110
sample39  -0.0085428066 -0.0105117852
sample40   0.0379324087  0.0440810741
sample41  -0.0044199152 -0.0128820644
sample42  -0.0292553573 -0.0067045265
sample43  -0.0077829155 -0.0510178219
sample44   0.0045122248  0.0479660309
sample45  -0.0074444298 -0.0051116726
sample46  -0.0088025512  0.0196186661
sample47   0.0076696301  0.0215947965
sample48   0.0290108585 -0.0175568376
sample49  -0.0141754858  0.0184717099
sample50   0.0006282201 -0.0233054373
sample51   0.0441995177 -0.0410022921
sample52   0.0715329391 -0.0399499475
sample53  -0.0095954087 -0.0029140909
sample54   0.0048933768 -0.0281884386
sample55   0.0327325487 -0.0532290012
sample56   0.0323068984 -0.0256595538
sample57   0.0806603122 -0.0286748097
sample58  -0.0064792049 -0.0006945349
sample59   0.0088958941  0.0067389649
sample60   0.0874124612  0.0431964341
sample61   0.0577604571 -0.0326112099
sample62  -0.0313318464  0.0224391756
sample63  -0.0233625220  0.0125110562
sample64  -0.0086426068  0.0148770341
sample65   0.0025256193 -0.0404466327
sample66   0.0006014071 -0.0471576264
sample67   0.0706087042  0.0516228406
sample68   0.0082301011  0.0033109509
sample69  -0.0475076743  0.0001452708
sample70  -0.0600773716  0.0089986962
sample71  -0.0096321627 -0.0050761187
sample72  -0.0031773546 -0.0166221542
sample73  -0.0113700517 -0.0191726684
sample74  -0.0014179662 -0.0608101325
sample75   0.0041911740 -0.0399981269
sample76  -0.0055326449  0.0353114263
sample77  -0.0260214459  0.0305731380
sample78  -0.0119267436  0.0632236007
sample79   0.0186017239  0.0027402910
sample80   0.0241047889 -0.0472697181
sample81  -0.0220288317 -0.0079577210
sample82  -0.0180751258  0.0639051029
sample83  -0.0256671713 -0.0125898269
sample84   0.0161392598 -0.0567222449
sample85   0.0139988188  0.0322763454
sample86  -0.0198382995  0.0389225776
sample87   0.0266270281 -0.0032979996
sample88   0.0515677078  0.0117902495
sample89   0.0014022125 -0.0140510488
sample90  -0.0375949749  0.0044004551
sample91   0.0310397965  0.0440610926
sample92   0.0270570567  0.0324380452
sample93  -0.0215009202  0.0063993941
sample94  -0.0415702912 -0.0037692077
sample95  -0.0168416047  0.0010019120
sample96  -0.0285582661 -0.0187991000
sample97  -0.0490843868 -0.0266760748
sample98  -0.0171579033 -0.0112897471
sample99  -0.0271316525  0.0232395583
sample100 -0.0301789816  0.0305498693
sample101 -0.0264371151  0.0170723968
sample102  0.0012767734 -0.0248949597
sample103  0.0055214687 -0.0030040587
sample104  0.0251346074 -0.0165212671
sample105  0.0062424215 -0.0400309901
sample106  0.0069768684  0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679  0.0023637162
sample109 -0.0014762845  0.0165583675
sample110  0.0036971063  0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112  0.0046098120 -0.0048009350
sample113  0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453  0.0135805368
sample116  0.0183575759  0.0665377581
sample117  0.0227640036 -0.0012287760
sample118  0.0015695248  0.0472617382
sample119  0.0190084932  0.0590034062
sample120 -0.0449645755  0.0072755697
sample121  0.0077307184  0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123  0.0016959300  0.0028593594
sample124 -0.0365091615  0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408  0.0019165544
sample127 -0.0494064872  0.0088209044
sample128 -0.0155454766  0.0186819802
sample129 -0.0184340400  0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422  0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133  0.0204029276 -0.0282209049
sample134  0.0175513332  0.0262883962
sample135  0.0029009925  0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323  0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139  0.0073103935  0.0308956174
sample140  0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142  0.0268670676 -0.0239229634
sample143  0.0421049126 -0.0110888235
sample144  0.0017253664 -0.0341766012
sample145  0.0681741320 -0.0073526377
sample146 -0.0239965222  0.0118396767
sample147 -0.0063453522  0.0183130585
sample148  0.0230825251 -0.0379753037
sample149  0.0223298673  0.0188909118
sample150  0.0055709108  0.0174179009
sample151  0.0039177786 -0.0233533275
sample152  0.0134325667  0.0302344591
sample153  0.0511990309  0.0730230140
sample154  0.0006698324  0.0154177486
sample155  0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599  0.0154934573
sample158  0.0201775524 -0.0332982124
sample159 -0.0086909001  0.0073496711
sample160  0.0295437331 -0.0555734536
sample161  0.0332754288  0.0033779619
sample162  0.0121954537  0.0433540412
sample163 -0.0173490933  0.0227219128
sample164  0.0143374783 -0.0453542590
sample165  0.0343612593 -0.0511194536
sample166 -0.0157536004  0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919  0.0060747155
sample169  0.0116231468 -0.0015112800
> 
> ## 3.3 Plotting VAF
> 
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
> 
> # JIVE plotVAF
> plotVAF(jiveRes)
> 
> 
> #########################
> ## PART 4. Plot Results
> 
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+              combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+              background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+              axisSize=NULL,titleSize=NULL)
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 
  5.729   0.100   5.842

Example timings

STATegRa.Rcheck/STATegRa-Ex.timings

nameusersystemelapsed
STATegRaUsersGuide0.0010.0010.000
STATegRa_data0.1010.0050.108
STATegRa_data_TCGA_BRCA0.0010.0010.001
bioDist0.1660.0120.189
bioDistFeature0.1290.0110.148
bioDistFeaturePlot0.1310.0100.156
bioDistW0.1350.0130.166
bioDistWPlot0.1310.0120.162
bioMap0.0010.0010.002
combiningMappings0.0040.0010.005
createOmicsExpressionSet0.0540.0030.062
getInitialData0.2770.0200.312
getLoadings0.2700.0300.308
getMethodInfo0.3240.0040.335
getPreprocessing0.3230.3480.713
getScores0.3320.0090.347
getVAF0.2580.0020.260
holistOmics0.0010.0010.002
modelSelection0.6940.9501.707
omicsCompAnalysis1.9380.0061.950
omicsNPC0.0010.0010.001
plotRes2.3410.0072.352
plotVAF2.0460.0042.052