Multiple platform build/check report for BioC 3.23 - CHECK results for STATegRa on nebbiolo1

Hostname	OS	Arch (*)	R version	Installed pkgs
nebbiolo1	Linux (Ubuntu 24.04.4 LTS)	x86_64	4.6.0 RC (2026-04-17 r89917) -- "Because it was There"	4988
kjohnson3	macOS 13.7.7 Ventura	arm64	4.6.0 Patched (2026-04-24 r89963) -- "Because it was There"	4718
Click on any hostname to see more info about the system (e.g. compilers) () as reported by 'uname -p', except on Windows and Mac OS X*

CHECK results for STATegRa on nebbiolo1

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: /home/biocbuild/bbs-3.23-bioc/R/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/home/biocbuild/bbs-3.23-bioc/R/site-library --timings STATegRa_1.48.0.tar.gz

StartedAt: 2026-05-02 05:36:14 -0400 (Sat, 02 May 2026)

EndedAt: 2026-05-02 05:41:08 -0400 (Sat, 02 May 2026)

EllapsedTime: 294.4 seconds

RetCode: 0

Status: OK

CheckDir: STATegRa.Rcheck

Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.23-bioc/R/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/home/biocbuild/bbs-3.23-bioc/R/site-library --timings STATegRa_1.48.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.23-bioc/meat/STATegRa.Rcheck’
* using R version 4.6.0 RC (2026-04-17 r89917)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
    GNU Fortran (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
* running under: Ubuntu 24.04.4 LTS
* using session charset: UTF-8
* current time: 2026-05-02 09:36:14 UTC
* 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 loading without being on the library search path ... 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
Examples with CPU (user + system) or elapsed time > 5s
         user system elapsed
plotRes 5.331  0.065   5.398
* 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 re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE

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

Installation output

STATegRa.Rcheck/00install.out

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###
### Running command:
###
###   /home/biocbuild/bbs-3.23-bioc/R/bin/R CMD INSTALL STATegRa
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.23-bioc/R/site-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 RC (2026-04-17 r89917) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

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 -- Sat May  2 05:39:14 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 
  3.038   0.186   3.213

STATegRa.Rcheck/tests/STATEgRa_Example.omicsCLUST.Rout


R version 4.6.0 RC (2026-04-17 r89917) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

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
4: In plot.window(...) :
  axis(2, *): range of values (    0) is small wrt |M| =   3e-09 --> not pretty()
5: In plot.window(...) :
  axis(2, *): range of values (    0) is small wrt |M| =   3e-09 --> not pretty()
6: In plot.window(...) :
  axis(2, *): range of values (    0) is small wrt |M| =   3e-09 --> not pretty()
7: In plot.window(...) :
  axis(2, *): range of values (    0) is small wrt |M| =   3e-09 --> not pretty()
> 
> #############################################
> ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
> #############################################
> 
> ## IDH1
> 
> IDH1.F<-bioDistFeature(Feature = "IDH1" ,
+                        listDistW = bioDistWList,
+                        threshold.cor=0.7)
> bioDistFeaturePlot(data=IDH1.F)
> 
> ## PDGFRA
> 
> #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
> #                       listDistW = bioDistWList,
> #                       threshold.cor=0.7)
> #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
> 
> ## EGFR
> #EGFR.F<-bioDistFeature(Feature = "EGFR" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.7)
> #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
> 
> ## MGMT
> #MGMT.F<-bioDistFeature(Feature = "MGMT" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.5)
> #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
 22.344   0.857  23.189

STATegRa.Rcheck/tests/STATegRa_Example.omicsNPC.Rout


R version 4.6.0 RC (2026-04-17 r89917) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

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
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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
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
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
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
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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calcNormFactors has been renamed to normLibSizes
Compute pseudo p-values based on NULL distributions...
NPC p-values calculation...
> 
> proc.time()
   user  system elapsed 
 59.797   0.488  60.276

STATegRa.Rcheck/tests/STATEgRa_Example.omicsPCA.Rout


R version 4.6.0 RC (2026-04-17 r89917) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

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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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> ###########################################
> ########### 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    0.0781574277 -0.0431500412
sample2   -0.1192218309  0.0294091076
sample3   -0.0531412238 -0.0746839892
sample4    0.0292975283 -0.0005957275
sample5    0.0202091834  0.0110463879
sample6    0.1226089072  0.1053466167
sample7    0.1078927988 -0.0322477846
sample8    0.1782895436  0.1449365333
sample9    0.0468698176 -0.0455174531
sample10  -0.0036030406  0.0420112139
sample11  -0.0035566461 -0.0566292965
sample12   0.1006128866  0.0641380350
sample13  -0.1174408215  0.0907488988
sample14   0.0981203239  0.0617737244
sample15   0.0085334209 -0.0087015678
sample16   0.0783148727  0.1581293023
sample17  -0.1483609906  0.0638582071
sample18  -0.0963086335  0.0556638468
sample19  -0.0217244111 -0.0720084659
sample20  -0.0635636502 -0.0779654734
sample21  -0.0201840185  0.1566391622
sample22   0.0218268536 -0.0764106183
sample23   0.0852042144 -0.0032686013
sample24  -0.1287170265  0.1924548062
sample25  -0.0430574089 -0.0456563430
sample26  -0.1453896758  0.0541514266
sample27  -0.0197488998 -0.1185659231
sample28  -0.1025336206  0.0650686728
sample29   0.0706018335 -0.0682990155
sample30  -0.1295627703 -0.0066772717
sample31   0.1147449107  0.1232685158
sample32  -0.0374310959  0.0380175219
sample33   0.0599515807  0.0136864685
sample34  -0.0984200869  0.0375319415
sample35  -0.0543098461 -0.0378108532
sample36   0.1403625338 -0.0343760537
sample37   0.0228941648 -0.0732852128
sample38  -0.0222077426 -0.0962595792
sample39  -0.0941738460  0.0215199883
sample40   0.0643800917 -0.0687876635
sample41  -0.0327638133 -0.1232188217
sample42  -0.0500431847 -0.0292471805
sample43  -0.0184498860  0.0233009729
sample44   0.1487899112  0.1171359834
sample45  -0.1050774000  0.1123203857
sample46  -0.1151195884 -0.1094029959
sample47  -0.0962593821 -0.0288465432
sample48   0.0004837515 -0.0310274080
sample49   0.1135207924  0.1213974416
sample50  -0.0123553236 -0.1740743014
sample51   0.0550529987  0.1258884889
sample52   0.0499121360  0.0728542986
sample53   0.1119773692  0.1588011932
sample54  -0.0360055659  0.0228575154
sample55   0.0210419031  0.0006730856
sample56  -0.0434169128  0.0633125772
sample57   0.0197824787  0.1150711875
sample58   0.0030439861  0.0326096862
sample59   0.0500252981  0.0129414858
sample60   0.0184278597  0.0136080120
sample61   0.0150299456  0.0635022919
sample62  -0.0304764109 -0.0201322466
sample63   0.1102252556  0.1285977339
sample64   0.1552588127  0.0971167717
sample65  -0.0058503007  0.0207115874
sample66  -0.0025605243  0.0424321253
sample67   0.1546634661 -0.0661721898
sample68   0.0536369081 -0.0923686889
sample69   0.0640330261  0.0081982307
sample70   0.0163517576 -0.0663230132
sample71  -0.0102537689 -0.1345919641
sample72  -0.0654196203 -0.0196122902
sample73  -0.1048556245  0.0220935761
sample74   0.0123799466  0.0586113329
sample75   0.0392077886 -0.0209756284
sample76   0.0648953318 -0.0524764533
sample77   0.1172922091 -0.0201186078
sample78  -0.1463067944  0.0708475499
sample79   0.0265211287 -0.1603303201
sample80   0.0279737095 -0.0214207329
sample81   0.0079211467 -0.0738449031
sample82  -0.1544236615 -0.0361468480
sample83  -0.0494211634 -0.0050052974
sample84  -0.0259038393 -0.0346547984
sample85   0.1116484219 -0.0031501515
sample86  -0.1306483214 -0.0377217527
sample87  -0.0554778208 -0.0459749348
sample88  -0.0301623734  0.0382197156
sample89  -0.1016866729  0.0694031966
sample90   0.0086819798 -0.0201319920
sample91   0.1578625140 -0.2097829458
sample92   0.0170936955 -0.1655801542
sample93  -0.0979806897 -0.0121512703
sample94   0.0131484004 -0.0114932164
sample95   0.0315682630 -0.0758856671
sample96   0.0024125609 -0.0470133499
sample97   0.0634545400  0.0270333077
sample98  -0.0359374722 -0.0135489367
sample99  -0.1009163162  0.1124782938
sample100  0.0551753072  0.0246489120
sample101 -0.0080119028 -0.1627366838
sample102 -0.0046444017  0.0095638639
sample103 -0.0472523273 -0.0940393725
sample104  0.0198159579 -0.0591089280
sample105 -0.0400237730 -0.0160910497
sample106 -0.0923808350  0.0369018335
sample107 -0.1019374013  0.0224953606
sample108 -0.0877091663 -0.0128833523
sample109  0.0864824549 -0.0900935726
sample110 -0.1223115506 -0.0096084711
sample111  0.0257354736 -0.0936164373
sample112 -0.0765286630  0.0270346185
sample113  0.0258803402  0.0377499689
sample114  0.0021138831 -0.0882013801
sample115  0.0303460402 -0.0723579191
sample116  0.0780508568 -0.0685062104
sample117  0.0536898282 -0.0911903371
sample118  0.0666651185 -0.0236229673
sample119  0.1021871568 -0.2324933804
sample120  0.0750216554  0.0243380643
sample121 -0.0756936340  0.0942949745
sample122 -0.0259627900  0.0731989990
sample123 -0.1037846324 -0.0369198009
sample124  0.0611208034  0.0421727026
sample125 -0.0738472709  0.0066950334
sample126  0.0972916318  0.0762637502
sample127  0.0824697543 -0.0096636921
sample128 -0.1249407493  0.0929315293
sample129 -0.0734067685 -0.0434365038
sample130 -0.0003502082 -0.0309852452
sample131  0.0930182752  0.0155935733
sample132  0.0736222905  0.0733032351
sample133 -0.0498397947 -0.0462436728
sample134  0.1644873520  0.0720003844
sample135 -0.0752297293  0.0003815495
sample136  0.0227145590 -0.0495507596
sample137  0.0564717218 -0.0288918106
sample138  0.0255988181 -0.0610853823
sample139  0.0621217750  0.0235805548
sample140 -0.0604152684 -0.0435595938
sample141  0.0246744021  0.0532649443
sample142 -0.0409560164  0.0316281783
sample143 -0.0077355161 -0.0476895791
sample144  0.0173240831 -0.0156777654
sample145  0.0485474824  0.1202771954
sample146  0.0419645438 -0.0811283014
sample147 -0.0977308518 -0.0274843011
sample148  0.0368256329  0.0803980026
sample149 -0.0072865836 -0.1532984647
sample150  0.1020825273  0.0624772568
sample151  0.0305399155 -0.0289275669
sample152 -0.0533594793 -0.0638307934
sample153 -0.0891627171  0.1799583892
sample154 -0.0727557610 -0.0834162251
sample155 -0.0880668670 -0.0220821779
sample156 -0.0276561125 -0.0326626413
sample157 -0.1155032223  0.0183615272
sample158 -0.0281507541 -0.0104939874
sample159  0.0663235788  0.0443838560
sample160 -0.0302643858  0.0404263793
sample161  0.0114715682 -0.0591022973
sample162 -0.1337086974  0.1398135731
sample163  0.1330124627  0.1688781855
sample164 -0.0150335986  0.0028417872
sample165  0.0076520360 -0.0164127748
sample166  0.0367794489  0.0630664161
sample167  0.1111988822  0.0030057496
sample168 -0.0672981545  0.0446279875
sample169 -0.0413005027  0.0224391958
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
                      1             2
sample1    0.0420516959  0.0867862968
sample2    0.0820827726 -0.0410978417
sample3   -0.0155896996 -0.0195182206
sample4    0.1001336868 -0.0410787106
sample5    0.0153465354 -0.0253259755
sample6   -0.0340329484 -0.0408223190
sample7   -0.0722578808  0.0002332582
sample8    0.0457494948 -0.0370016613
sample9    0.0086250623  0.0820184926
sample10   0.0423597207 -0.0083923510
sample11  -0.0022546751  0.0787766128
sample12  -0.0322107027  0.1479824738
sample13   0.0293886443 -0.0306748867
sample14  -0.0337484848 -0.0367506798
sample15  -0.0815538245  0.1275622851
sample16  -0.0508457283  0.0540604668
sample17  -0.0062598435  0.0041023664
sample18  -0.0705641450 -0.0351047459
sample19   0.0476844151 -0.0509598193
sample20  -0.0522960085  0.0715522184
sample21   0.0119121389 -0.0376093335
sample22  -0.0724390346 -0.0095624709
sample23   0.0992532247  0.0134288371
sample24   0.1595111896  0.0728661088
sample25   0.0920694740 -0.0749757599
sample26   0.0595539001  0.0848965717
sample27  -0.0826481777 -0.0086734919
sample28   0.0384786274  0.0440966627
sample29  -0.0777668580  0.1735308933
sample30  -0.1229471150 -0.0819004981
sample31  -0.0579850445 -0.0238644668
sample32  -0.0970394354 -0.0111425927
sample33  -0.1017588327 -0.0630442150
sample34  -0.0637923736  0.0377941942
sample35  -0.0789983636 -0.0229722835
sample36  -0.1224939561 -0.1274954378
sample37  -0.1798819621 -0.1673426566
sample38  -0.0466300651  0.0888161268
sample39   0.0168687342  0.0421533659
sample40  -0.1756390979 -0.1526641517
sample41  -0.0042366563  0.0004928981
sample42   0.0447850756 -0.0651505159
sample43  -0.0482309159 -0.0253529088
sample44   0.1986710306 -0.0545778887
sample45   0.0741832927  0.0054702823
sample46  -0.0478768236 -0.0007071669
sample47  -0.0608187299  0.0481622938
sample48   0.1381490460  0.0578287219
sample49   0.0530515665 -0.1405533225
sample50   0.0173806434  0.1602389821
sample51  -0.0462565597  0.0303473878
sample52  -0.0280068084  0.0280388424
sample53  -0.0667626748  0.0237702123
sample54  -0.0121834526 -0.0521354295
sample55  -0.0182396122  0.0221328516
sample56   0.0001253211  0.0030907280
sample57  -0.0316679906  0.0530190273
sample58  -0.0393919349 -0.0297798617
sample59  -0.1278291763 -0.0546527417
sample60  -0.1486986014  0.1069157180
sample61  -0.0793124946  0.0569796790
sample62  -0.1172800085 -0.0149197954
sample63   0.0028723017  0.1300519650
sample64  -0.0237367679  0.1073287690
sample65   0.0126534495  0.0589808361
sample66   0.0468193049 -0.0771072946
sample67  -0.1494263628 -0.0769859571
sample68  -0.0977958457 -0.0577350511
sample69  -0.0403087147  0.0156042280
sample70  -0.0221528318  0.0315441139
sample71   0.0546439200 -0.0272396499
sample72  -0.1107487219 -0.0537318865
sample73  -0.0906761465  0.0579966977
sample74  -0.0586557289  0.0121421859
sample75  -0.0390492445  0.0349283013
sample76   0.0022961692 -0.1676558741
sample77   0.0232096039 -0.2067302878
sample78   0.0929752578 -0.0434939985
sample79   0.1619501926 -0.0378114730
sample80  -0.0680364370  0.1424663822
sample81   0.0530786664 -0.0358350980
sample82  -0.0266820642 -0.0577444946
sample83  -0.1517234884 -0.0448553672
sample84   0.0570968138 -0.0273813435
sample85  -0.1086290478 -0.1228118864
sample86  -0.0833858495 -0.0442914574
sample87  -0.0022017408 -0.0943906784
sample88   0.0078222608 -0.1140506617
sample89  -0.0611059537 -0.0094584973
sample90  -0.0022927473 -0.0936253957
sample91  -0.0433582409  0.3205983236
sample92   0.1815340766 -0.0334680864
sample93  -0.0267629714  0.0614429160
sample94  -0.0181876711  0.0605090502
sample95   0.0720378454 -0.0013045866
sample96   0.0559716653 -0.0118791594
sample97   0.0217410589  0.0195414018
sample98  -0.0379176427  0.0588357290
sample99   0.0792423716 -0.0151274288
sample100 -0.0222117112 -0.0023321374
sample101  0.0387235032  0.1224226262
sample102  0.2094613632 -0.0516443529
sample103 -0.0138477804  0.0301052138
sample104  0.0807988743 -0.0162719187
sample105  0.0520493406 -0.1229665362
sample106  0.0192611984 -0.0185238316
sample107 -0.0319017283  0.0405123402
sample108  0.0140691748  0.0163421340
sample109  0.1831933376  0.0613006887
sample110  0.0292790964 -0.0199849193
sample111  0.1423255499  0.0327339844
sample112 -0.0426333726 -0.0029083290
sample113  0.0771903274  0.0268733271
sample114  0.0241644706 -0.0184080402
sample115  0.1959018249  0.0460129931
sample116  0.1394477755 -0.0530806326
sample117  0.1672364042 -0.1386537001
sample118  0.0448344873 -0.0117622102
sample119  0.0910394999  0.2217433285
sample120  0.0331391668 -0.0057274672
sample121 -0.0307577492  0.1392506555
sample122  0.0839778618 -0.0291994870
sample123 -0.0239649288 -0.0642163573
sample124  0.0909149488  0.0130419056
sample125  0.0065350338 -0.1092631845
sample126 -0.0935313567  0.1368284346
sample127 -0.0035387082  0.0292755662
sample128  0.0660293041  0.1018565924
sample129 -0.0693637119 -0.0695421384
sample130 -0.0008492366 -0.0669704283
sample131 -0.0431024545  0.0174065027
sample132  0.0637037935  0.0029374356
sample133  0.0289496109 -0.0390818891
sample134 -0.0446205749  0.0456334596
sample135 -0.0712336673  0.0521635260
sample136 -0.0596268862  0.0197299642
sample137 -0.0793150893 -0.0380627932
sample138  0.0973550260 -0.0454218598
sample139 -0.0539906429 -0.1534327169
sample140 -0.0850825000  0.0955814954
sample141  0.0192679922 -0.0554450215
sample142  0.0672260541 -0.0461321238
sample143  0.0303731463 -0.0519260305
sample144  0.0089365193  0.0145814900
sample145  0.0638765117  0.0122257981
sample146 -0.0585853321  0.0063083701
sample147 -0.0894132708 -0.1124615285
sample148  0.0216363667 -0.0615967318
sample149  0.0515425403 -0.0839903517
sample150 -0.0568285786 -0.0124468766
sample151  0.0789533226 -0.0261831482
sample152  0.0330756189  0.1306443525
sample153  0.1751925264  0.1497731120
sample154 -0.0421421383 -0.0037009908
sample155 -0.0680176557  0.0095711554
sample156 -0.0388909423  0.1057563174
sample157 -0.0314769595  0.0561367536
sample158 -0.0329620132  0.0353947481
sample159  0.0398414560 -0.1007374002
sample160 -0.0424940267  0.0108496312
sample161  0.0888372717 -0.0679700473
sample162  0.0027471667  0.1237843669
sample163  0.0126099688  0.0725434068
sample164  0.0566779303 -0.0458324420
sample165  0.0315336577 -0.0236362456
sample166  0.0612055854 -0.0425233380
sample167 -0.0142729883  0.0179308330
sample168  0.0169501730 -0.0769618026
sample169 -0.0675081115  0.0131505589
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
                      1             2
sample1   -0.0012329570 -1.635717e-01
sample2   -0.0724349907 -6.021234e-03
sample3   -0.0188460468 -1.080036e-01
sample4    0.0390145370  3.114392e-04
sample5    0.1774811688 -2.996382e-02
sample6   -0.0451444304 -3.455857e-02
sample7   -0.0226466390 -7.020192e-03
sample8   -0.1033680017 -9.856749e-03
sample9    0.1350011592  8.979098e-02
sample10   0.1259887394 -5.097849e-02
sample11   0.0979788223  7.086534e-02
sample12  -0.0863019002 -8.620317e-02
sample13  -0.1381401065  1.828007e-01
sample14  -0.0615073806 -2.642803e-02
sample15   0.0381598878 -3.101666e-02
sample16  -0.0048776570  1.271867e-03
sample17  -0.0788480809 -1.547552e-02
sample18  -0.0884188683 -3.795487e-02
sample19   0.0703044453 -1.084004e-01
sample20  -0.0025585740  7.975871e-02
sample21   0.0941601945 -4.126736e-02
sample22  -0.0550273540 -7.806748e-02
sample23   0.0679495407 -4.102003e-02
sample24  -0.1310962456  1.649310e-01
sample25   0.0113585350 -4.426862e-02
sample26  -0.1402945759  2.016545e-02
sample27   0.0261560875  1.588396e-03
sample28  -0.0724198605  5.850595e-02
sample29  -0.0330058777  2.060784e-03
sample30  -0.0228752633 -2.015433e-02
sample31  -0.0635067793 -6.670333e-02
sample32   0.0685099663 -4.955273e-02
sample33  -0.0777765207 -1.272079e-01
sample34   0.0157842456 -3.024314e-02
sample35  -0.0529632999  1.500972e-01
sample36   0.0070900395  2.025307e-01
sample37  -0.0442421025  1.802088e-01
sample38  -0.0781511457 -3.676424e-02
sample39   0.0120331957 -3.388840e-02
sample40  -0.0473292473  1.471561e-01
sample41   0.0228189251 -2.673558e-02
sample42  -0.0245360165 -7.960866e-02
sample43   0.1036362868 -8.229576e-02
sample44  -0.1012228525  7.049458e-02
sample45   0.0013732329 -2.450906e-02
sample46  -0.0558510200  2.947341e-03
sample47  -0.0380481295  4.554171e-02
sample48   0.0784342129  4.888984e-02
sample49  -0.0605163744 -1.162352e-02
sample50   0.0530079042 -2.737936e-02
sample51   0.1514646612  5.678348e-02
sample52   0.1860935201  1.246717e-01
sample53  -0.0064176932 -2.700991e-02
sample54   0.0697038396 -2.308388e-02
sample55   0.1633576998  1.366443e-02
sample56   0.1011485170  4.682208e-02
sample57   0.1730374216  1.609604e-01
sample58  -0.0071384682 -1.666955e-02
sample59  -0.0030461825  3.005282e-02
sample60   0.0215834786  2.665877e-01
sample61   0.1510583568  1.002385e-01
sample62  -0.0925534048 -4.845846e-02
sample63  -0.0596311596 -4.137020e-02
sample64  -0.0449225720 -2.600569e-03
sample65   0.0939383839 -4.406907e-02
sample66   0.1063400913 -5.709990e-02
sample67  -0.0201590490  2.361727e-01
sample68   0.0037202938  2.418384e-02
sample69  -0.0645161157 -1.155622e-01
sample70  -0.1013440031 -1.351789e-01
sample71  -0.0016467999 -2.976844e-02
sample72   0.0328892927 -2.835860e-02
sample73   0.0275080057 -5.148187e-02
sample74   0.1341719779 -7.895279e-02
sample75   0.0951575614 -3.943185e-02
sample76  -0.0864722076  3.034989e-02
sample77  -0.1035749576 -2.545355e-02
sample78  -0.1575643936  4.939597e-02
sample79   0.0189136948  4.874679e-02
sample80   0.1384140473  4.264120e-05
sample81  -0.0118846467 -6.357932e-02
sample82  -0.1675308236  3.533909e-02
sample83  -0.0065673485 -7.812613e-02
sample84   0.1486891649 -3.109055e-02
sample85  -0.0532724617  7.417881e-02
sample86  -0.1138477438 -1.919014e-05
sample87   0.0432863881  6.080472e-02
sample88   0.0433450332  1.402491e-01
sample89   0.0331205864 -1.395400e-02
sample90  -0.0607412775 -8.610415e-02
sample91  -0.0566273163  1.303746e-01
sample92  -0.0359582669  1.061604e-01
sample93  -0.0433646346 -4.443636e-02
sample94  -0.0477291255 -1.059574e-01
sample95  -0.0249595792 -3.980526e-02
sample96   0.0035219069 -9.293928e-02
sample97  -0.0066048583 -1.527231e-01
sample98   0.0020366812 -5.579551e-02
sample99  -0.0886615783 -3.728221e-02
sample100 -0.1091259105 -3.560421e-02
sample101 -0.0739726663 -4.318003e-02
sample102  0.0574461403 -2.783908e-02
sample103  0.0142730868  9.705532e-03
sample104  0.0710395162  4.068352e-02
sample105  0.0980831421 -3.452951e-02
sample106 -0.0254259240  3.628985e-02
sample107 -0.0160653343 -9.173394e-02
sample108 -0.0200987619 -2.379692e-02
sample109 -0.0389780652  1.692360e-02
sample110 -0.0326304822  2.988110e-02
sample111  0.0676937603 -6.038211e-02
sample112  0.0167883449  5.336939e-03
sample113  0.0969217166 -2.757600e-02
sample114 -0.0026398388 -9.209158e-02
sample115 -0.0308047259  1.603825e-02
sample116 -0.1240307267  1.273000e-01
sample117  0.0334729054  5.392712e-02
sample118 -0.1037152980  6.252430e-02
sample119 -0.1064177061  1.196202e-01
sample120 -0.0771354959 -1.004932e-01
sample121 -0.0129350650  3.181978e-02
sample122  0.0847492542 -5.568321e-02
sample123 -0.0041336833  7.693167e-03
sample124 -0.0583457794 -8.396387e-02
sample125  0.0634844686 -5.232539e-02
sample126 -0.0662580867 -1.091733e-01
sample127 -0.0865024557 -1.094176e-01
sample128 -0.0627817183 -1.470959e-02
sample129 -0.0336276529 -4.007862e-02
sample130 -0.0293517726 -8.046118e-02
sample131 -0.0469197709 -2.209764e-03
sample132 -0.0241740420 -1.248598e-01
sample133  0.0907303173  1.466701e-02
sample134 -0.0350842124  7.539662e-02
sample135  0.0001333346  9.185364e-03
sample136 -0.0335876121 -9.860277e-02
sample137 -0.0640148984 -7.554473e-02
sample138  0.0060964856 -1.742762e-02
sample139 -0.0592084535  5.614967e-02
sample140  0.0427985796 -1.099554e-02
sample141  0.0618796559 -9.301035e-02
sample142  0.0898554569  3.573421e-02
sample143  0.0817389103  8.880524e-02
sample144  0.0787754793 -3.821391e-02
sample145  0.1085821685  1.569477e-01
sample146 -0.0589558100 -4.373364e-02
sample147 -0.0495330547  7.277171e-03
sample148  0.1161592923  9.079119e-03
sample149 -0.0121579701  7.788371e-02
sample150 -0.0314512558  3.520212e-02
sample151  0.0575382216 -1.945351e-02
sample152 -0.0494542215  7.025536e-02
sample153 -0.0941332448  2.153298e-01
sample154 -0.0335932177  2.078725e-02
sample155  0.0690457594 -2.780411e-02
sample156  0.1039901586 -6.292526e-02
sample157 -0.0408645764  8.065513e-03
sample158  0.1018105258  7.816874e-03
sample159 -0.0281730433 -1.207204e-02
sample160  0.1643053035  2.978120e-03
sample161  0.0374329179  8.524611e-02
sample162 -0.0804535143  8.349758e-02
sample163 -0.0743227729 -1.406221e-02
sample164  0.1208806098 -2.139457e-02
sample165  0.1608115903  2.025194e-02
sample166 -0.0425944466 -2.660712e-02
sample167 -0.0226849485 -4.464283e-02
sample168 -0.0180735474 -7.466022e-04
sample169  0.0190778961  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 
 14.206   0.322  14.513

Example timings

STATegRa.Rcheck/STATegRa-Ex.timings

name	user	system	elapsed
STATegRaUsersGuide	0.000	0.000	0.001
STATegRa_data	0.224	0.012	0.237
STATegRa_data_TCGA_BRCA	0.001	0.000	0.002
bioDist	0.390	0.028	0.419
bioDistFeature	0.316	0.010	0.326
bioDistFeaturePlot	0.315	0.036	0.352
bioDistW	0.312	0.023	0.335
bioDistWPlot	0.314	0.010	0.325
bioMap	0.003	0.002	0.003
combiningMappings	0.008	0.004	0.011
createOmicsExpressionSet	0.140	0.002	0.142
getInitialData	0.604	0.022	0.626
getLoadings	0.610	0.045	0.654
getMethodInfo	0.711	0.066	0.777
getPreprocessing	0.759	0.650	1.409
getScores	0.745	0.016	0.762
getVAF	0.597	0.001	0.598
holistOmics	0.002	0.001	0.002
modelSelection	1.687	1.831	3.518
omicsCompAnalysis	3.999	0.080	4.079
omicsNPC	0.002	0.001	0.003
plotRes	5.331	0.065	5.398
plotVAF	4.659	0.040	4.700