David Gomez-Cabrero
| Snapshot Date: 2026-04-30 13:40 -0400 (Thu, 30 Apr 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) |
| Back to Multiple platform build/check report for BioC 3.23: simplified long |
|
This page was generated on 2026-05-01 11:35 -0400 (Fri, 01 May 2026).
| 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 | ||||
| Package 2190/2418 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| STATegRa 1.48.0 (landing page) David Gomez-Cabrero
| nebbiolo1 | Linux (Ubuntu 24.04.4 LTS) / x86_64 | OK | OK | OK | | ||||||||
| kjohnson3 | macOS 13.7.7 Ventura / arm64 | OK | OK | OK | OK | | ||||||||
| See other builds for STATegRa in R Universe. | ||||||||||||||
|
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. |
| 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: 2026-05-01 05:23:37 -0400 (Fri, 01 May 2026) |
| EndedAt: 2026-05-01 05:28:32 -0400 (Fri, 01 May 2026) |
| EllapsedTime: 295.4 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: STATegRa.Rcheck |
| Warnings: 0 |
##############################################################################
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### 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
###
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* 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: 2026-05-01 09:23:37 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
plotVAF 7.132 0.027 8.365
plotRes 5.854 0.033 5.887
* 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.
STATegRa.Rcheck/00install.out
############################################################################## ############################################################################## ### ### 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)
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 -- Fri May 1 05:26:43 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
3.127 0.199 3.311
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
21.977 0.857 22.824
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)
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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
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
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
calcNormFactors has been renamed to normLibSizes
calcNormFactors has been renamed to normLibSizes
calcNormFactors has been renamed to normLibSizes
Compute pseudo p-values based on NULL distributions...
NPC p-values calculation...
>
> proc.time()
user system elapsed
61.080 0.561 61.631
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
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 0.0781575666 -0.0431550452
sample2 -0.1192221474 0.0294019084
sample3 -0.0531408571 -0.0746837513
sample4 0.0292971669 -0.0006036996
sample5 0.0202090709 0.0110454940
sample6 0.1226088426 0.1053494244
sample7 0.1078931449 -0.0322416769
sample8 0.1782891002 0.1449329032
sample9 0.0468697259 -0.0455171586
sample10 -0.0036032766 0.0420076434
sample11 -0.0035566362 -0.0566284791
sample12 0.1006129707 0.0641394602
sample13 -0.1174412951 0.0907474834
sample14 0.0981203569 0.0617764620
sample15 0.0085337358 -0.0086953725
sample16 0.0783146755 0.1581335152
sample17 -0.1483610689 0.0638580045
sample18 -0.0963084330 0.0556689940
sample19 -0.0217243043 -0.0720130383
sample20 -0.0635633846 -0.0779608407
sample21 -0.0201844123 0.1566381613
sample22 0.0218274017 -0.0764053747
sample23 0.0852038942 -0.0032768232
sample24 -0.1287181866 0.1924421212
sample25 -0.0430575694 -0.0456641729
sample26 -0.1453899879 0.0541456544
sample27 -0.0197483437 -0.1185590110
sample28 -0.1025339526 0.0650654050
sample29 0.0706022595 -0.0682930537
sample30 -0.1295622825 -0.0066673721
sample31 0.1147449317 0.1232729009
sample32 -0.0374308125 0.0380252681
sample33 0.0599520930 0.0136939103
sample34 -0.0984199255 0.0375366507
sample35 -0.0543096346 -0.0378032143
sample36 0.1403628142 -0.0343633914
sample37 0.0228947883 -0.0732682861
sample38 -0.0222072879 -0.0962566025
sample39 -0.0941739245 0.0215179742
sample40 0.0643807287 -0.0687713344
sample41 -0.0327634873 -0.1232187160
sample42 -0.0500431627 -0.0292515541
sample43 -0.0184497131 0.0233045857
sample44 0.1487889028 0.1171203871
sample45 -0.1050778954 0.1123137719
sample46 -0.1151191453 -0.1093994338
sample47 -0.0962591448 -0.0288416042
sample48 0.0004832451 -0.0310383816
sample49 0.1135203740 0.1213934993
sample50 -0.0123549814 -0.1740763762
sample51 0.0550527353 0.1258932733
sample52 0.0499118391 0.0728582565
sample53 0.1119772633 0.1588066510
sample54 -0.0360055721 0.0228586077
sample55 0.0210418820 0.0006751579
sample56 -0.0434171576 0.0633131504
sample57 0.0197820555 0.1150755743
sample58 0.0030440733 0.0326128856
sample59 0.0500256870 0.0129526142
sample60 0.0184280145 0.0136223926
sample61 0.0150298904 0.0635100086
sample62 -0.0304758570 -0.0201232467
sample63 0.1102250039 0.1285968012
sample64 0.1552586787 0.0971185999
sample65 -0.0058503849 0.0207102199
sample66 -0.0025607550 0.0424283025
sample67 0.1546638814 -0.0661572146
sample68 0.0536374430 -0.0923600594
sample69 0.0640333089 0.0082004786
sample70 0.0163521877 -0.0663227070
sample71 -0.0102536065 -0.1345966854
sample72 -0.0654191604 -0.0196032536
sample73 -0.1048553143 0.0221002789
sample74 0.0123800535 0.0586158750
sample75 0.0392079798 -0.0209724885
sample76 0.0648954621 -0.0524759291
sample77 0.1172922669 -0.0201200984
sample78 -0.1463072776 0.0708396313
sample79 0.0265208753 -0.1603430625
sample80 0.0279739287 -0.0214151033
sample81 0.0079212172 -0.0738497474
sample82 -0.1544234525 -0.0361449848
sample83 -0.0494205204 -0.0049934055
sample84 -0.0259039780 -0.0346593414
sample85 0.1116487558 -0.0031400066
sample86 -0.1306478909 -0.0377153376
sample87 -0.0554777851 -0.0459739236
sample88 -0.0301626620 0.0382207102
sample89 -0.1016866171 0.0694080204
sample90 0.0086821704 -0.0201323984
sample91 0.1578629939 -0.2097790589
sample92 0.0170933393 -0.1655942120
sample93 -0.0979805037 -0.0121499941
sample94 0.0131486293 -0.0114929334
sample95 0.0315682455 -0.0758919685
sample96 0.0024125845 -0.0470187355
sample97 0.0634545816 0.0270302479
sample98 -0.0359372482 -0.0135465523
sample99 -0.1009167766 0.1124709699
sample100 0.0551754132 0.0246502665
sample101 -0.0080115889 -0.1627408919
sample102 -0.0046451431 0.0095466052
sample103 -0.0472520785 -0.0940383095
sample104 0.0198157383 -0.0591149603
sample105 -0.0400239037 -0.0160950714
sample106 -0.0923810199 0.0369003254
sample107 -0.1019372314 0.0224967880
sample108 -0.0877091509 -0.0128850396
sample109 0.0864820133 -0.0901086973
sample110 -0.1223116511 -0.0096109499
sample111 0.0257352352 -0.0936285952
sample112 -0.0765285896 0.0270380718
sample113 0.0258799724 0.0377435899
sample114 0.0021141168 -0.0882041224
sample115 0.0303455118 -0.0723741619
sample116 0.0780504327 -0.0685166462
sample117 0.0536893924 -0.0912030390
sample118 0.0666649853 -0.0236262372
sample119 0.1021872613 -0.2325006626
sample120 0.0750216323 0.0243344225
sample121 -0.0756937935 0.0942971291
sample122 -0.0259632209 0.0731918401
sample123 -0.1037844629 -0.0369177963
sample124 0.0611205071 0.0421643819
sample125 -0.0738472620 0.0066944019
sample126 0.0972919228 0.0762701374
sample127 0.0824699503 -0.0096644984
sample128 -0.1249411662 0.0929251322
sample129 -0.0734063558 -0.0434311518
sample130 -0.0003500191 -0.0309857403
sample131 0.0930184094 0.0155971491
sample132 0.0736220524 0.0732969713
sample133 -0.0498398375 -0.0462456720
sample134 0.1644872600 0.0720048691
sample135 -0.0752294966 0.0003871800
sample136 0.0227150121 -0.0495467870
sample137 0.0564721861 -0.0288858279
sample138 0.0255986427 -0.0610933669
sample139 0.0621218816 0.0235859870
sample140 -0.0604148817 -0.0435529767
sample141 0.0246743006 0.0532629565
sample142 -0.0409563996 0.0316232347
sample143 -0.0077356432 -0.0476909376
sample144 0.0173241008 -0.0156786188
sample145 0.0485467466 0.1202736547
sample146 0.0419650138 -0.0811239221
sample147 -0.0977304516 -0.0274768412
sample148 0.0368253178 0.0803969087
sample149 -0.0072864840 -0.1533018327
sample150 0.1020825523 0.0624824561
sample151 0.0305397092 -0.0289339500
sample152 -0.0533595221 -0.0638336122
sample153 -0.0891639849 0.1799447919
sample154 -0.0727554287 -0.0834128103
sample155 -0.0880665725 -0.0220768293
sample156 -0.0276558773 -0.0326600803
sample157 -0.1155031538 0.0183636327
sample158 -0.0281506673 -0.0104910814
sample159 0.0663233707 0.0443808558
sample160 -0.0302644016 0.0404302856
sample161 0.0114712863 -0.0591085554
sample162 -0.1337091137 0.1398131245
sample163 0.1330120618 0.1688768823
sample164 -0.0150338223 0.0028373690
sample165 0.0076518782 -0.0164146549
sample166 0.0367791392 0.0630612213
sample167 0.1111989852 0.0030066808
sample168 -0.0672983027 0.0446266189
sample169 -0.0413003589 0.0224449284
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 -0.0420461519 0.0867866186
sample2 -0.0820850123 -0.0410968602
sample3 0.0155967543 -0.0195186437
sample4 -0.1001342884 -0.0410776129
sample5 -0.0153479990 -0.0253257688
sample6 0.0340237361 -0.0408223382
sample7 0.0722602856 0.0002323725
sample8 -0.0457621887 -0.0370006723
sample9 -0.0086216039 0.0820184482
sample10 -0.0423631853 -0.0083917515
sample11 0.0022594355 0.0787764070
sample12 0.0322075554 0.1479823360
sample13 -0.0293971840 -0.0306742761
sample14 0.0337429857 -0.0367508400
sample15 0.0815560621 0.1275613641
sample16 0.0508329583 0.0540604326
sample17 0.0062554395 0.0041024895
sample18 0.0705600087 -0.0351053491
sample19 -0.0476782529 -0.0509595370
sample20 0.0523028440 0.0715513887
sample21 -0.0119253519 -0.0376086959
sample22 0.0724459309 -0.0095635138
sample23 -0.0992529489 0.0134299208
sample24 -0.1595268506 0.0728684732
sample25 -0.0920660425 -0.0749749064
sample26 -0.0595567563 0.0848973848
sample27 0.0826578889 -0.0086747843
sample28 -0.0384834548 0.0440972868
sample29 0.0777742287 0.1735298264
sample30 0.1229474406 -0.0819018798
sample31 0.0579748612 -0.0238646920
sample32 0.0970365199 -0.0111435362
sample33 0.1017579965 -0.0630452839
sample34 0.0637902255 0.0377936072
sample35 0.0790003763 -0.0229732719
sample36 0.1224932950 -0.1274968692
sample37 0.1798848001 -0.1673448560
sample38 0.0466395665 0.0888152987
sample39 -0.0168694856 0.0421536101
sample40 0.1756418478 -0.1526662871
sample41 0.0042471310 0.0004924467
sample42 -0.0447825025 -0.0651501289
sample43 0.0482291687 -0.0253533648
sample44 -0.1986821523 -0.0545753160
sample45 -0.0741919712 0.0054714486
sample46 0.0478863897 -0.0007080590
sample47 0.0608217161 0.0481615257
sample48 -0.1381464259 0.0578301359
sample49 -0.0530633105 -0.1405523363
sample50 -0.0173643512 0.1602386041
sample51 0.0462454557 0.0303473001
sample52 0.0279994651 0.0280387843
sample53 0.0667496268 0.0237700083
sample54 0.0121811264 -0.0521354922
sample55 0.0182392137 0.0221326584
sample56 -0.0001309956 0.0030909338
sample57 0.0316572772 0.0530190646
sample58 0.0393890482 -0.0297801872
sample59 0.1278271009 -0.0546540930
sample60 0.1486964199 0.1069141461
sample61 0.0793066439 0.0569790240
sample62 0.1172822295 -0.0149211506
sample63 -0.0028814348 0.1300524239
sample64 0.0237294912 0.1073288402
sample65 -0.0126543812 0.0589810407
sample66 -0.0468234634 -0.0771066452
sample67 0.1494286377 -0.0769877844
sample68 0.0978024708 -0.0577364161
sample69 0.0403090550 0.0156038149
sample70 0.0221599283 0.0315436512
sample71 -0.0546328018 -0.0272394918
sample72 0.1107501289 -0.0537331700
sample73 0.0906756589 0.0579957649
sample74 0.0586512596 0.0121417342
sample75 0.0390513024 0.0349278090
sample76 -0.0022939149 -0.1676560130
sample77 -0.0232101561 -0.2067300894
sample78 -0.0929810866 -0.0434927645
sample79 -0.1619378596 -0.0378102172
sample80 0.0680393296 0.1424655718
sample81 -0.0530724419 -0.0358347589
sample82 0.0266850995 -0.0577449201
sample83 0.1517242181 -0.0448570524
sample84 -0.0570942753 -0.0273808335
sample85 0.1086271174 -0.1228130725
sample86 0.0833892259 -0.0442925073
sample87 0.0022041200 -0.0943908550
sample88 -0.0078277828 -0.1140504491
sample89 0.0611004901 -0.0094589508
sample90 0.0022941888 -0.0936254892
sample91 0.0433776713 0.3205971861
sample92 -0.1815215468 -0.0334666327
sample93 0.0267654516 0.0614425716
sample94 0.0181901941 0.0605088106
sample95 -0.0720313124 -0.0013040435
sample96 -0.0559671862 -0.0118787027
sample97 -0.0217420816 0.0195417285
sample98 0.0379199702 0.0588352642
sample99 -0.0792509800 -0.0151262075
sample100 0.0222100027 -0.0023322972
sample101 -0.0387081403 0.1224225174
sample102 -0.2094626250 -0.0516420295
sample103 0.0138559478 0.0301047519
sample104 -0.0807947258 -0.0162712223
sample105 -0.0520491836 -0.1229660230
sample106 -0.0192643559 -0.0185235088
sample107 0.0319014317 0.0405120504
sample108 -0.0140673843 0.0163422363
sample109 -0.1831855490 0.0613024100
sample110 -0.0292782467 -0.0199846415
sample111 -0.1423172219 0.0327352389
sample112 0.0426312875 -0.0029087150
sample113 -0.0771932697 0.0268742975
sample114 -0.0241566441 -0.0184080658
sample115 -0.1958954882 0.0460149056
sample116 -0.1394436591 -0.0530793177
sample117 -0.1672310704 -0.1386521583
sample118 -0.0448331325 -0.0117617883
sample119 -0.0910188684 0.2217435821
sample120 -0.0331405223 -0.0057270221
sample121 0.0307515141 0.1392506191
sample122 -0.0839839183 -0.0291983332
sample123 0.0239675949 -0.0642167499
sample124 -0.0909177031 0.0130430406
sample125 -0.0065362684 -0.1092630998
sample126 0.0935272270 0.1368276639
sample127 0.0035406179 0.0292754994
sample128 -0.0660351719 0.1018576040
sample129 0.0693672028 -0.0695430483
sample130 0.0008517778 -0.0669705417
sample131 0.0431011613 0.0174060903
sample132 -0.0637090402 0.0029383742
sample133 -0.0289463760 -0.0390817262
sample134 0.0446140286 0.0456332242
sample135 0.0712343974 0.0521627403
sample136 0.0596319862 0.0197291485
sample137 0.0793176210 -0.0380637533
sample138 -0.0973504337 -0.0454209908
sample139 0.0539864956 -0.1534332236
sample140 0.0850873449 0.0955804162
sample141 -0.0192725122 -0.0554446375
sample142 -0.0672295376 -0.0461312868
sample143 -0.0303706438 -0.0519258499
sample144 -0.0089350150 0.0145815380
sample145 -0.0638880124 0.0122269005
sample146 0.0585924605 0.0063074668
sample147 0.0894147216 -0.1124626068
sample148 -0.0216441835 -0.0615962282
sample149 -0.0515313304 -0.0839902843
sample150 0.0568227121 -0.0124472842
sample151 -0.0789513098 -0.0261823750
sample152 -0.0330691511 0.1306445036
sample153 -0.1752069393 0.1497756136
sample154 0.0421491761 -0.0037017304
sample155 0.0680199462 0.0095703297
sample156 0.0388951413 0.1057557813
sample157 0.0314764961 0.0561364571
sample158 0.0329630548 0.0353943516
sample159 -0.0398463001 -0.1007368137
sample160 0.0424904760 0.0108492963
sample161 -0.0888339297 -0.0679692625
sample162 -0.0027573773 0.1237848409
sample163 -0.0126233251 0.0725441065
sample164 -0.0566787436 -0.0458318133
sample165 -0.0315331416 -0.0236359507
sample166 -0.0612110845 -0.0425224604
sample167 0.0142729548 0.0179306957
sample168 -0.0169544232 -0.0769614787
sample169 0.0675062865 0.0131498899
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 0.0012331743 -1.635716e-01
sample2 0.0724353343 -6.022160e-03
sample3 0.0188459923 -1.080029e-01
sample4 -0.0390143042 3.106254e-04
sample5 -0.1774810587 -2.996431e-02
sample6 0.0451446545 -3.455900e-02
sample7 0.0226463370 -7.019177e-03
sample8 0.1033684733 -9.857989e-03
sample9 -0.1350014323 8.979114e-02
sample10 -0.1259884303 -5.097940e-02
sample11 -0.0979791031 7.086568e-02
sample12 0.0863021060 -8.620322e-02
sample13 0.1381401864 1.827998e-01
sample14 0.0615074774 -2.642808e-02
sample15 -0.0381600655 -3.101599e-02
sample16 0.0048779504 1.270995e-03
sample17 0.0788483323 -1.547608e-02
sample18 0.0884189532 -3.795477e-02
sample19 -0.0703043482 -1.084003e-01
sample20 0.0025581180 7.975973e-02
sample21 -0.0941596426 -4.126899e-02
sample22 0.0550270756 -7.806612e-02
sample23 -0.0679492719 -4.102080e-02
sample24 0.1310969718 1.649281e-01
sample25 -0.0113583495 -4.426901e-02
sample26 0.1402949034 2.016456e-02
sample27 -0.0261566250 1.590013e-03
sample28 0.0724200872 5.850507e-02
sample29 0.0330054608 2.062123e-03
sample30 0.0228750245 -2.015341e-02
sample31 0.0635070512 -6.670370e-02
sample32 -0.0685100015 -4.955245e-02
sample33 0.0777764917 -1.272070e-01
sample34 -0.0157842069 -3.024311e-02
sample35 0.0529627733 1.500981e-01
sample36 -0.0070908279 2.025321e-01
sample37 0.0442411442 1.802110e-01
sample38 0.0781508253 -3.676294e-02
sample39 -0.0120329993 -3.388885e-02
sample40 0.0473283499 1.471582e-01
sample41 -0.0228192308 -2.673455e-02
sample42 0.0245361927 -7.960878e-02
sample43 -0.1036361978 -8.229578e-02
sample44 0.1012235025 7.049233e-02
sample45 -0.0013726470 -2.451074e-02
sample46 0.0558506304 2.948625e-03
sample47 0.0380478615 4.554239e-02
sample48 -0.0784340400 4.888888e-02
sample49 0.0605168244 -1.162475e-02
sample50 -0.0530083074 -2.737809e-02
sample51 -0.1514645301 5.678257e-02
sample52 -0.1860936043 1.246711e-01
sample53 0.0064179817 -2.701062e-02
sample54 -0.0697037528 -2.308413e-02
sample55 -0.1633577734 1.366432e-02
sample56 -0.1011483949 4.682131e-02
sample57 -0.1730374412 1.609593e-01
sample58 0.0071384898 -1.666951e-02
sample59 0.0030458344 3.005379e-02
sample60 -0.0215842482 2.665888e-01
sample61 -0.1510585403 1.002384e-01
sample62 0.0925531489 -4.845724e-02
sample63 0.0596315589 -4.137111e-02
sample64 0.0449227342 -2.600972e-03
sample65 -0.0939382186 -4.406951e-02
sample66 -0.1063397610 -5.710081e-02
sample67 0.0201580433 2.361747e-01
sample68 -0.0037208604 2.418548e-02
sample69 0.0645162029 -1.155618e-01
sample70 0.1013439734 -1.351780e-01
sample71 0.0016466027 -2.976771e-02
sample72 -0.0328895502 -2.835769e-02
sample73 -0.0275080400 -5.148151e-02
sample74 -0.1341718311 -7.895304e-02
sample75 -0.0951576686 -3.943147e-02
sample76 0.0864719863 3.035055e-02
sample77 0.1035749502 -2.545324e-02
sample78 0.1575648027 4.939471e-02
sample79 -0.0189138436 4.874690e-02
sample80 -0.1384142863 4.316691e-05
sample81 0.0118846670 -6.357907e-02
sample82 0.1675306578 3.533971e-02
sample83 0.0065671065 -7.812494e-02
sample84 -0.1486890621 -3.109097e-02
sample85 0.0532720178 7.417992e-02
sample86 0.1138474819 -1.817049e-05
sample87 -0.0432866029 6.080500e-02
sample88 -0.0433451228 1.402486e-01
sample89 -0.0331204757 -1.395429e-02
sample90 0.0607413505 -8.610385e-02
sample91 0.0566263375 1.303771e-01
sample92 0.0359580615 1.061605e-01
sample93 0.0433646461 -4.443609e-02
sample94 0.0477292161 -1.059570e-01
sample95 0.0249595926 -3.980509e-02
sample96 -0.0035217524 -9.293931e-02
sample97 0.0066052134 -1.527234e-01
sample98 -0.0020367074 -5.579514e-02
sample99 0.0886621961 -3.728379e-02
sample100 0.1091259622 -3.560400e-02
sample101 0.0739723719 -4.317881e-02
sample102 -0.0574455492 -2.784092e-02
sample103 -0.0142733863 9.706378e-03
sample104 -0.0710395601 4.068330e-02
sample105 -0.0980829885 -3.452998e-02
sample106 0.0254260544 3.628931e-02
sample107 0.0160655106 -9.173398e-02
sample108 0.0200988338 -2.379699e-02
sample109 0.0389781985 1.692310e-02
sample110 0.0326305268 2.988086e-02
sample111 -0.0676935878 -6.038250e-02
sample112 -0.0167883512 5.336923e-03
sample113 -0.0969213833 -2.757706e-02
sample114 0.0026397958 -9.209100e-02
sample115 0.0308049649 1.603742e-02
sample116 0.1240306357 1.272998e-01
sample117 -0.0334728639 5.392661e-02
sample118 0.1037152124 6.252440e-02
sample119 0.1064170329 1.196218e-01
sample120 0.0771357813 -1.004935e-01
sample121 0.0129352380 3.181913e-02
sample122 -0.0847487368 -5.568468e-02
sample123 0.0041335469 7.693564e-03
sample124 0.0583462370 -8.396478e-02
sample125 -0.0634843196 -5.232569e-02
sample126 0.0662582145 -1.091730e-01
sample127 0.0865025654 -1.094172e-01
sample128 0.0627822215 -1.471097e-02
sample129 0.0336274516 -4.007772e-02
sample130 0.0293518128 -8.046086e-02
sample131 0.0469196758 -2.209373e-03
sample132 0.0241745831 -1.248608e-01
sample133 -0.0907303824 1.466698e-02
sample134 0.0350841198 7.539660e-02
sample135 -0.0001334938 9.185832e-03
sample136 0.0335874755 -9.860179e-02
sample137 0.0640147206 -7.554368e-02
sample138 -0.0060964020 -1.742783e-02
sample139 0.0592082697 5.615007e-02
sample140 -0.0427988711 -1.099463e-02
sample141 -0.0618793148 -9.301104e-02
sample142 -0.0898552431 3.573321e-02
sample143 -0.0817391139 8.880528e-02
sample144 -0.0787754464 -3.821395e-02
sample145 -0.1085819428 1.569460e-01
sample146 0.0589554884 -4.373233e-02
sample147 0.0495327815 7.278085e-03
sample148 -0.1161590393 9.078106e-03
sample149 0.0121575343 7.788465e-02
sample150 0.0314511957 3.520220e-02
sample151 -0.0575380900 -1.945394e-02
sample152 0.0494540286 7.025567e-02
sample153 0.0941338767 2.153269e-01
sample154 0.0335928704 2.078828e-02
sample155 -0.0690459098 -2.780360e-02
sample156 -0.1039902331 -6.292487e-02
sample157 0.0408645846 8.065531e-03
sample158 -0.1018106383 7.817025e-03
sample159 0.0281732607 -1.207262e-02
sample160 -0.1643052856 2.977801e-03
sample161 -0.0374330118 8.524588e-02
sample162 0.0804538386 8.349632e-02
sample163 0.0743232570 -1.406350e-02
sample164 -0.1208804213 -2.139526e-02
sample165 -0.1608115957 2.025158e-02
sample166 0.0425948062 -2.660803e-02
sample167 0.0226849506 -4.464257e-02
sample168 0.0180737440 -7.471710e-04
sample169 -0.0190780253 2.645427e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
[,1] [,2]
sample1 -0.0572060227 -1.729087e-02
sample2 0.0875245208 1.112588e-02
sample3 0.0403482602 -3.168994e-02
sample4 -0.0218345996 4.052760e-06
sample5 -0.0150905011 4.795041e-03
sample6 -0.0924362933 4.511003e-02
sample7 -0.0793066751 -1.243823e-02
sample8 -0.1342997187 6.215220e-02
sample9 -0.0338886944 -1.854401e-02
sample10 0.0020547173 1.749421e-02
sample11 0.0037275602 -2.364116e-02
sample12 -0.0753094533 2.772698e-02
sample13 0.0856160091 3.679963e-02
sample14 -0.0737457307 2.668452e-02
sample15 -0.0062111746 -3.554864e-03
sample16 -0.0602355268 6.675115e-02
sample17 0.1086768843 2.524534e-02
sample18 0.0702999472 2.231671e-02
sample19 0.0173785882 -3.024846e-02
sample20 0.0484173812 -3.310904e-02
sample21 0.0124657042 6.517144e-02
sample22 -0.0140989936 -3.159137e-02
sample23 -0.0627028403 -5.393710e-04
sample24 0.0919972100 7.909297e-02
sample25 0.0326998483 -1.945206e-02
sample26 0.1064741246 2.120849e-02
sample27 0.0166058995 -4.964993e-02
sample28 0.0743504770 2.614211e-02
sample29 -0.0511008491 -2.782647e-02
sample30 0.0962250842 -3.974893e-03
sample31 -0.0869563008 5.250819e-02
sample32 0.0271858919 1.552005e-02
sample33 -0.0448364581 6.243160e-03
sample34 0.0718415218 1.469396e-02
sample35 0.0403086451 -1.632629e-02
sample36 -0.1036402827 -1.304320e-02
sample37 -0.0159385744 -3.036525e-02
sample38 0.0182198369 -4.034805e-02
sample39 0.0690363619 8.058350e-03
sample40 -0.0467312750 -2.810325e-02
sample41 0.0263674438 -5.171216e-02
sample42 0.0374578960 -1.268634e-02
sample43 0.0132336869 9.536642e-03
sample44 -0.1119154428 5.028683e-02
sample45 0.0759639367 4.587903e-02
sample46 0.0871885519 -4.670385e-02
sample47 0.0721490571 -1.288540e-02
sample48 0.0005086144 -1.290565e-02
sample49 -0.0858177028 5.173760e-02
sample50 0.0118992665 -7.276215e-02
sample51 -0.0426446855 5.306205e-02
sample52 -0.0381605826 3.086785e-02
sample53 -0.0855757630 6.730043e-02
sample54 0.0261723092 9.184260e-03
sample55 -0.0156418304 4.682404e-04
sample56 0.0307831193 2.597550e-02
sample57 -0.0157242103 4.829381e-02
sample58 -0.0031174404 1.359898e-02
sample59 -0.0373001859 5.868397e-03
sample60 -0.0142609099 5.831654e-03
sample61 -0.0122255144 2.663579e-02
sample62 0.0228002942 -8.692265e-03
sample63 -0.0833127581 5.473229e-02
sample64 -0.1166548159 4.196500e-02
sample65 0.0038808902 8.568590e-03
sample66 0.0011561811 1.766612e-02
sample67 -0.1129311062 -2.608702e-02
sample68 -0.0382526429 -3.804045e-02
sample69 -0.0476502440 4.003241e-03
sample70 -0.0110329882 -2.752719e-02
sample71 0.0096850282 -5.627056e-02
sample72 0.0487124704 -8.800131e-03
sample73 0.0773058132 8.239864e-03
sample74 -0.0102488176 2.454957e-02
sample75 -0.0286613976 -8.387293e-03
sample76 -0.0472655595 -2.129315e-02
sample77 -0.0865043074 -7.296820e-03
sample78 0.1070293698 2.818346e-02
sample79 -0.0165060681 -6.659721e-02
sample80 -0.0206765949 -8.712112e-03
sample81 -0.0050943615 -3.079175e-02
sample82 0.1153622361 -1.647054e-02
sample83 0.0367979217 -2.538114e-03
sample84 0.0199463070 -1.468961e-02
sample85 -0.0827122185 -2.709824e-04
sample86 0.0969487314 -1.699897e-02
sample87 0.0421957457 -1.965953e-02
sample88 0.0215934743 1.566050e-02
sample89 0.0751559502 2.811652e-02
sample90 -0.0057328000 -8.283795e-03
sample91 -0.1134005268 -8.603522e-02
sample92 -0.0101689918 -6.894992e-02
sample93 0.0725967502 -6.003176e-03
sample94 -0.0096878852 -4.693081e-03
sample95 -0.0223502239 -3.139636e-02
sample96 -0.0013232863 -1.963604e-02
sample97 -0.0476541710 1.183660e-02
sample98 0.0269546160 -5.978398e-03
sample99 0.0728179461 4.597884e-02
sample100 -0.0413398038 1.079347e-02
sample101 0.0087536994 -6.796076e-02
sample102 0.0032509529 3.932612e-03
sample103 0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105 0.0294940465 -7.140722e-03
sample106 0.0686472054 1.462895e-02
sample107 0.0748635927 8.401339e-03
sample108 0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110 0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112 0.0570870472 1.066018e-02
sample113 -0.0200110554 1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586 1.089843e-02
sample121 0.0542443861 3.861344e-02
sample122 0.0178575357 3.027138e-02
sample123 0.0775020581 -1.636852e-02
sample124 -0.0460701050 1.814758e-02
sample125 0.0543846585 2.075898e-03
sample126 -0.0729417144 3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128 0.0908136899 3.758801e-02
sample129 0.0552445878 -1.879062e-02
sample130 0.0007128089 -1.294308e-02
sample131 -0.0693311345 7.357082e-03
sample132 -0.0556565156 3.126995e-02
sample133 0.0375870104 -1.977240e-02
sample134 -0.1229130924 3.159495e-02
sample135 0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201 1.040683e-02
sample140 0.0452288969 -1.876279e-02
sample141 -0.0189142561 2.247042e-02
sample142 0.0297545566 1.280524e-02
sample143 0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491 5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147 0.0726083535 -1.239968e-02
sample148 -0.0284795794 3.389732e-02
sample149 0.0082261455 -6.399305e-02
sample150 -0.0765013197 2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152 0.0403422737 -2.714879e-02
sample153 0.0629117719 7.425085e-02
sample154 0.0551622927 -3.548984e-02
sample155 0.0654439133 -1.005306e-02
sample156 0.0209310714 -1.390213e-02
sample157 0.0851522597 6.577150e-03
sample158 0.0208354599 -4.663078e-03
sample159 -0.0498794349 1.913257e-02
sample160 0.0216074437 1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162 0.0963663017 5.705881e-02
sample163 -0.1009542191 7.174224e-02
sample164 0.0109881996 1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357 2.673084e-02
sample167 -0.0825048036 2.278863e-03
sample168 0.0486147429 1.793843e-02
sample169 0.0302506727 8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
[,1] [,2]
sample1 -0.0621842115 -1.364509e-02
sample2 0.0944623785 9.720892e-03
sample3 0.0406196267 -2.236338e-02
sample4 -0.0229316496 -3.932487e-04
sample5 -0.0157330047 3.231033e-03
sample6 -0.0945794025 3.120720e-02
sample7 -0.0854427118 -1.052880e-02
sample8 -0.1376625920 4.286608e-02
sample9 -0.0377115311 -1.415134e-02
sample10 0.0035244506 1.280825e-02
sample11 0.0016639987 -1.717895e-02
sample12 -0.0781403168 1.884368e-02
sample13 0.0938400516 2.838858e-02
sample14 -0.0759839772 1.810989e-02
sample15 -0.0068340837 -2.705361e-03
sample16 -0.0590150849 4.757848e-02
sample17 0.1178805097 2.040526e-02
sample18 0.0767858320 1.756604e-02
sample19 0.0157112113 -2.172867e-02
sample20 0.0485318300 -2.327033e-02
sample21 0.0185928176 4.777095e-02
sample22 -0.0191358702 -2.329775e-02
sample23 -0.0672994194 -1.535656e-03
sample24 0.1047476642 5.935707e-02
sample25 0.0329844953 -1.358036e-02
sample26 0.1154952052 1.741529e-02
sample27 0.0133849853 -3.590922e-02
sample28 0.0821554039 2.042376e-02
sample29 -0.0567643690 -2.123848e-02
sample30 0.1016073931 -1.134728e-03
sample31 -0.0880396372 3.670548e-02
sample32 0.0300363338 1.182406e-02
sample33 -0.0467252272 3.739254e-03
sample34 0.0783666394 1.203777e-02
sample35 0.0424227097 -1.118559e-02
sample36 -0.1107646166 -1.143464e-02
sample37 -0.0191667664 -2.246060e-02
sample38 0.0155968095 -2.909621e-02
sample39 0.0746847148 7.148218e-03
sample40 -0.0517028178 -2.137267e-02
sample41 0.0234979494 -3.723018e-02
sample42 0.0388797356 -8.557228e-03
sample43 0.0149555568 7.210002e-03
sample44 -0.1150305613 3.461805e-02
sample45 0.0846146236 3.486020e-02
sample46 0.0884426404 -3.246853e-02
sample47 0.0748644971 -8.083045e-03
sample48 -0.0012033198 -9.403647e-03
sample49 -0.0872662737 3.616245e-02
sample50 0.0066941314 -5.284863e-02
sample51 -0.0411777630 3.791830e-02
sample52 -0.0379355780 2.180834e-02
sample53 -0.0851639886 4.751761e-02
sample54 0.0288006248 7.184424e-03
sample55 -0.0164920835 5.919925e-05
sample56 0.0355115616 1.951043e-02
sample57 -0.0141146068 3.492409e-02
sample58 -0.0015636132 9.862883e-03
sample59 -0.0390656483 3.590929e-03
sample60 -0.0139454780 3.963030e-03
sample61 -0.0106410274 1.919705e-02
sample62 0.0236748439 -5.922677e-03
sample63 -0.0846790877 3.839102e-02
sample64 -0.1202581015 2.846469e-02
sample65 0.0050548584 6.328644e-03
sample66 0.0028013072 1.291807e-02
sample67 -0.1231623009 -2.112565e-02
sample68 -0.0437782161 -2.845072e-02
sample69 -0.0501199692 2.053469e-03
sample70 -0.0140278645 -2.027157e-02
sample71 0.0057489505 -4.085977e-02
sample72 0.0511212704 -5.522408e-03
sample73 0.0828141409 7.431582e-03
sample74 -0.0085959456 1.772951e-02
sample75 -0.0312180394 -6.636869e-03
sample76 -0.0519051781 -1.640191e-02
sample77 -0.0925924762 -6.907800e-03
sample78 0.1163971046 2.251122e-02
sample79 -0.0240906926 -4.887766e-02
sample80 -0.0221327065 -6.730703e-03
sample81 -0.0072114968 -2.254399e-02
sample82 0.1204416674 -9.907422e-03
sample83 0.0386739485 -1.171663e-03
sample84 0.0195988488 -1.033806e-02
sample85 -0.0877680171 -1.725057e-03
sample86 0.1023541048 -1.062501e-02
sample87 0.0425213089 -1.356865e-02
sample88 0.0244788514 1.180820e-02
sample89 0.0804276691 2.188588e-02
sample90 -0.0074639871 -6.140721e-03
sample91 -0.1278832404 -6.485140e-02
sample92 -0.0162199697 -5.048358e-02
sample93 0.0769344893 -3.045135e-03
sample94 -0.0104345587 -3.593172e-03
sample95 -0.0260058453 -2.330475e-02
sample96 -0.0025018700 -1.433516e-02
sample97 -0.0492358305 7.774183e-03
sample98 0.0279220220 -3.862141e-03
sample99 0.0813921923 3.487339e-02
sample100 -0.0428797405 7.112807e-03
sample101 0.0032855240 -4.940743e-02
sample102 0.0038439317 2.938008e-03
sample103 0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105 0.0314853405 -4.656633e-03
sample106 0.0726456731 1.192390e-02
sample107 0.0807342975 7.508627e-03
sample108 0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110 0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112 0.0599954082 8.820317e-03
sample113 -0.0195006577 1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131 6.937326e-03
sample121 0.0613899126 2.915307e-02
sample122 0.0218424338 2.241775e-02
sample123 0.0809008460 -1.051759e-02
sample124 -0.0472109313 1.239887e-02
sample125 0.0583180947 2.521167e-03
sample126 -0.0753941872 2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128 0.1000212216 2.908091e-02
sample129 0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877 4.091672e-03
sample132 -0.0566219024 2.179861e-02
sample133 0.0384172955 -1.372840e-02
sample134 -0.1280862736 2.077912e-02
sample135 0.0592633273 6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861 6.729304e-03
sample140 0.0468926306 -1.285498e-02
sample141 -0.0186248693 1.605439e-02
sample142 0.0328031246 9.887746e-03
sample143 0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310 3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147 0.0765025670 -7.714769e-03
sample148 -0.0276016641 2.420589e-02
sample149 0.0027545308 -4.653007e-02
sample150 -0.0792296010 1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152 0.0409796547 -1.907063e-02
sample153 0.0734301757 5.528780e-02
sample154 0.0557740684 -2.487723e-02
sample155 0.0689436560 -6.127635e-03
sample156 0.0212272938 -9.747423e-03
sample157 0.0911931194 6.355708e-03
sample158 0.0220840645 -3.016357e-03
sample159 -0.0513244242 1.304175e-02
sample160 0.0246213576 1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162 0.1078802043 4.337260e-02
sample163 -0.1017965082 5.047171e-02
sample164 0.0119430799 9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180 1.899222e-02
sample167 -0.0872832229 1.516582e-04
sample168 0.0540714512 1.397701e-02
sample169 0.0328432652 7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
[,1] [,2]
sample1 0.0133684846 2.195848e-02
sample2 0.0254157197 -1.058416e-02
sample3 -0.0049551479 -4.840017e-03
sample4 0.0310390570 -1.063929e-02
sample5 0.0046941318 -6.488426e-03
sample6 -0.0107406753 -1.026702e-02
sample7 -0.0225157631 2.624712e-04
sample8 0.0141320952 -9.505821e-03
sample9 0.0029681280 2.078210e-02
sample10 0.0131729174 -2.275042e-03
sample11 -0.0004164298 1.994019e-02
sample12 -0.0095211620 3.759883e-02
sample13 0.0091018604 -7.953956e-03
sample14 -0.0106557524 -9.181659e-03
sample15 -0.0249924121 3.262724e-02
sample16 -0.0156216400 1.375700e-02
sample17 -0.0019382446 1.073994e-03
sample18 -0.0221072481 -8.703592e-03
sample19 0.0146917619 -1.311712e-02
sample20 -0.0160353760 1.826290e-02
sample21 0.0035947899 -9.616341e-03
sample22 -0.0225060762 -2.532589e-03
sample23 0.0310000683 3.033060e-03
sample24 0.0499544372 1.809450e-02
sample25 0.0284442301 -1.932558e-02
sample26 0.0188220043 2.146985e-02
sample27 -0.0257763219 -1.999228e-03
sample28 0.0120888648 1.125834e-02
sample29 -0.0236482520 4.426726e-02
sample30 -0.0385486305 -2.055935e-02
sample31 -0.0181539336 -5.877838e-03
sample32 -0.0302630460 -2.607192e-03
sample33 -0.0319565715 -1.562628e-02
sample34 -0.0197970124 9.906813e-03
sample35 -0.0247412713 -5.434440e-03
sample36 -0.0386259060 -3.190394e-02
sample37 -0.0566199273 -4.192574e-02
sample38 -0.0142060273 2.259644e-02
sample39 0.0053589035 1.076485e-02
sample40 -0.0552546493 -3.819896e-02
sample41 -0.0013089975 9.278818e-05
sample42 0.0137252142 -1.664652e-02
sample43 -0.0151259626 -6.290953e-03
sample44 0.0617391754 -1.442883e-02
sample45 0.0231410886 1.163143e-03
sample46 -0.0148898209 -1.384176e-04
sample47 -0.0187252536 1.221690e-02
sample48 0.0432839432 1.416671e-02
sample49 0.0160818605 -3.588745e-02
sample50 0.0059333545 4.067003e-02
sample51 -0.0142914866 7.776270e-03
sample52 -0.0086339952 7.208917e-03
sample53 -0.0207386980 6.272432e-03
sample54 -0.0039856719 -1.316934e-02
sample55 -0.0056217017 5.692315e-03
sample56 0.0000123292 8.978290e-04
sample57 -0.0095805555 1.324253e-02
sample58 -0.0124160295 -7.326376e-03
sample59 -0.0400195442 -1.349736e-02
sample60 -0.0460063358 2.770091e-02
sample61 -0.0245266456 1.470710e-02
sample62 -0.0366022783 -3.437352e-03
sample63 0.0013742171 3.288796e-02
sample64 -0.0070599859 2.739588e-02
sample65 0.0041201911 1.498268e-02
sample66 0.0143173351 -1.968812e-02
sample67 -0.0467477531 -1.929938e-02
sample68 -0.0306751978 -1.436184e-02
sample69 -0.0125317217 4.130407e-03
sample70 -0.0068071487 8.080857e-03
sample71 0.0169170264 -7.027348e-03
sample72 -0.0346909749 -1.333770e-02
sample73 -0.0280506153 1.493843e-02
sample74 -0.0182611498 3.294697e-03
sample75 -0.0120563964 8.974612e-03
sample76 0.0001437236 -4.253184e-02
sample77 0.0065330299 -5.252886e-02
sample78 0.0288278141 -1.127782e-02
sample79 0.0503961481 -1.023318e-02
sample80 -0.0207693429 3.648391e-02
sample81 0.0163562768 -9.074596e-03
sample82 -0.0084317129 -1.478976e-02
sample83 -0.0474097918 -1.103126e-02
sample84 0.0177181395 -7.191197e-03
sample85 -0.0342718548 -3.082360e-02
sample86 -0.0261671791 -1.089491e-02
sample87 -0.0009486358 -2.411514e-02
sample88 0.0020528931 -2.894615e-02
sample89 -0.0189361111 -2.638639e-03
sample90 -0.0009863658 -2.390075e-02
sample91 -0.0124352695 8.153234e-02
sample92 0.0564264106 -8.909537e-03
sample93 -0.0081461774 1.570851e-02
sample94 -0.0054896581 1.547251e-02
sample95 0.0224073150 -4.374348e-04
sample96 0.0173528924 -3.050441e-03
sample97 0.0067948115 5.008237e-03
sample98 -0.0116030825 1.498764e-02
sample99 0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101 0.0124923691 3.091503e-02
sample102 0.0650835386 -1.367400e-02
sample103 -0.0042741828 7.855985e-03
sample104 0.0250591040 -4.171938e-03
sample105 0.0157516368 -3.121990e-02
sample106 0.0060593853 -5.101693e-03
sample107 -0.0098329626 1.044506e-02
sample108 0.0044269853 4.142036e-03
sample109 0.0572473486 1.517542e-02
sample110 0.0090474827 -5.119868e-03
sample111 0.0444263015 7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113 0.0241047399 6.706740e-03
sample114 0.0074558775 -4.728652e-03
sample115 0.0611851433 1.117210e-02
sample116 0.0432646951 -1.380556e-02
sample117 0.0516750066 -3.575617e-02
sample118 0.0139942100 -3.279138e-03
sample119 0.0291722987 5.587946e-02
sample120 0.0103515853 -1.690016e-03
sample121 -0.0091396331 3.552116e-02
sample122 0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124 0.0283466326 3.127845e-03
sample125 0.0016472378 -2.770692e-02
sample126 -0.0286529417 3.489336e-02
sample127 -0.0010224500 7.483214e-03
sample128 0.0209049296 2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968 4.637390e-03
sample132 0.0198526786 5.723983e-04
sample133 0.0088812957 -9.988115e-03
sample134 -0.0137484514 1.172591e-02
sample135 -0.0220314568 1.347465e-02
sample136 -0.0185173353 5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138 0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762 2.456863e-02
sample141 0.0058369763 -1.420854e-02
sample142 0.0207886071 -1.188764e-02
sample143 0.0092832598 -1.324238e-02
sample144 0.0028442140 3.627979e-03
sample145 0.0199749569 2.862202e-03
sample146 -0.0182236697 1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148 0.0065435868 -1.572917e-02
sample149 0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151 0.0245166984 -6.888241e-03
sample152 0.0107259913 3.314630e-02
sample153 0.0550963965 3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574 2.642246e-03
sample156 -0.0117803505 2.698265e-02
sample157 -0.0096167165 1.433840e-02
sample158 -0.0101754772 9.137620e-03
sample159 0.0120662931 -2.565236e-02
sample160 -0.0132238202 2.916023e-03
sample161 0.0274491966 -1.748284e-02
sample162 0.0012482909 3.152261e-02
sample163 0.0042031315 1.830701e-02
sample164 0.0174896157 -1.175915e-02
sample165 0.0097517662 -6.119019e-03
sample166 0.0190134679 -1.121582e-02
sample167 -0.0044140836 4.665585e-03
sample168 0.0049689168 -1.941822e-02
sample169 -0.0209802098 3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
[,1] [,2]
sample1 -0.0515543627 -0.0305856787
sample2 -0.0144993256 0.0236342950
sample3 -0.0371833108 -0.0140263348
sample4 0.0068945388 -0.0132539692
sample5 0.0215035333 -0.0663338101
sample6 -0.0187055152 0.0088773016
sample7 -0.0061521552 0.0064029054
sample8 -0.0210874459 0.0334652901
sample9 0.0516865043 -0.0291142799
sample10 0.0059440366 -0.0527217447
sample11 0.0393010793 -0.0200624712
sample12 -0.0420837100 0.0131331362
sample13 0.0333252565 0.0818552509
sample14 -0.0190062644 0.0160202175
sample15 -0.0030968049 -0.0189230681
sample16 -0.0004452158 0.0018880102
sample17 -0.0185848615 0.0240170131
sample18 -0.0273093598 0.0230213640
sample19 -0.0217761111 -0.0445894441
sample20 0.0245820821 0.0159812738
sample21 0.0034527644 -0.0400016054
sample22 -0.0340789054 0.0039289109
sample23 -0.0010344929 -0.0310161212
sample24 0.0289468503 0.0760962436
sample25 -0.0119098496 -0.0122798760
sample26 -0.0181001057 0.0517892852
sample27 0.0050465417 -0.0086515844
sample28 0.0057491502 0.0358830107
sample29 -0.0051104246 0.0116605117
sample30 -0.0103085904 0.0039678538
sample31 -0.0319929858 0.0090606113
sample32 -0.0036232521 -0.0328202010
sample33 -0.0534742153 0.0024751837
sample34 -0.0067495749 -0.0111000311
sample35 0.0378745721 0.0465929296
sample36 0.0647886800 0.0359987924
sample37 0.0488441236 0.0492906912
sample38 -0.0251514062 0.0197110110
sample39 -0.0085428066 -0.0105117852
sample40 0.0379324087 0.0440810741
sample41 -0.0044199152 -0.0128820644
sample42 -0.0292553573 -0.0067045265
sample43 -0.0077829155 -0.0510178219
sample44 0.0045122248 0.0479660309
sample45 -0.0074444298 -0.0051116726
sample46 -0.0088025512 0.0196186661
sample47 0.0076696301 0.0215947965
sample48 0.0290108585 -0.0175568376
sample49 -0.0141754858 0.0184717099
sample50 0.0006282201 -0.0233054373
sample51 0.0441995177 -0.0410022921
sample52 0.0715329391 -0.0399499475
sample53 -0.0095954087 -0.0029140909
sample54 0.0048933768 -0.0281884386
sample55 0.0327325487 -0.0532290012
sample56 0.0323068984 -0.0256595538
sample57 0.0806603122 -0.0286748097
sample58 -0.0064792049 -0.0006945349
sample59 0.0088958941 0.0067389649
sample60 0.0874124612 0.0431964341
sample61 0.0577604571 -0.0326112099
sample62 -0.0313318464 0.0224391756
sample63 -0.0233625220 0.0125110562
sample64 -0.0086426068 0.0148770341
sample65 0.0025256193 -0.0404466327
sample66 0.0006014071 -0.0471576264
sample67 0.0706087042 0.0516228406
sample68 0.0082301011 0.0033109509
sample69 -0.0475076743 0.0001452708
sample70 -0.0600773716 0.0089986962
sample71 -0.0096321627 -0.0050761187
sample72 -0.0031773546 -0.0166221542
sample73 -0.0113700517 -0.0191726684
sample74 -0.0014179662 -0.0608101325
sample75 0.0041911740 -0.0399981269
sample76 -0.0055326449 0.0353114263
sample77 -0.0260214459 0.0305731380
sample78 -0.0119267436 0.0632236007
sample79 0.0186017239 0.0027402910
sample80 0.0241047889 -0.0472697181
sample81 -0.0220288317 -0.0079577210
sample82 -0.0180751258 0.0639051029
sample83 -0.0256671713 -0.0125898269
sample84 0.0161392598 -0.0567222449
sample85 0.0139988188 0.0322763454
sample86 -0.0198382995 0.0389225776
sample87 0.0266270281 -0.0032979996
sample88 0.0515677078 0.0117902495
sample89 0.0014022125 -0.0140510488
sample90 -0.0375949749 0.0044004551
sample91 0.0310397965 0.0440610926
sample92 0.0270570567 0.0324380452
sample93 -0.0215009202 0.0063993941
sample94 -0.0415702912 -0.0037692077
sample95 -0.0168416047 0.0010019120
sample96 -0.0285582661 -0.0187991000
sample97 -0.0490843868 -0.0266760748
sample98 -0.0171579033 -0.0112897471
sample99 -0.0271316525 0.0232395583
sample100 -0.0301789816 0.0305498693
sample101 -0.0264371151 0.0170723968
sample102 0.0012767734 -0.0248949597
sample103 0.0055214687 -0.0030040587
sample104 0.0251346074 -0.0165212671
sample105 0.0062424215 -0.0400309901
sample106 0.0069768684 0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679 0.0023637162
sample109 -0.0014762845 0.0165583675
sample110 0.0036971063 0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112 0.0046098120 -0.0048009350
sample113 0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453 0.0135805368
sample116 0.0183575759 0.0665377581
sample117 0.0227640036 -0.0012287760
sample118 0.0015695248 0.0472617382
sample119 0.0190084932 0.0590034062
sample120 -0.0449645755 0.0072755697
sample121 0.0077307184 0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123 0.0016959300 0.0028593594
sample124 -0.0365091615 0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408 0.0019165544
sample127 -0.0494064872 0.0088209044
sample128 -0.0155454766 0.0186819802
sample129 -0.0184340400 0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422 0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133 0.0204029276 -0.0282209049
sample134 0.0175513332 0.0262883962
sample135 0.0029009925 0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323 0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139 0.0073103935 0.0308956174
sample140 0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142 0.0268670676 -0.0239229634
sample143 0.0421049126 -0.0110888235
sample144 0.0017253664 -0.0341766012
sample145 0.0681741320 -0.0073526377
sample146 -0.0239965222 0.0118396767
sample147 -0.0063453522 0.0183130585
sample148 0.0230825251 -0.0379753037
sample149 0.0223298673 0.0188909118
sample150 0.0055709108 0.0174179009
sample151 0.0039177786 -0.0233533275
sample152 0.0134325667 0.0302344591
sample153 0.0511990309 0.0730230140
sample154 0.0006698324 0.0154177486
sample155 0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599 0.0154934573
sample158 0.0201775524 -0.0332982124
sample159 -0.0086909001 0.0073496711
sample160 0.0295437331 -0.0555734536
sample161 0.0332754288 0.0033779619
sample162 0.0121954537 0.0433540412
sample163 -0.0173490933 0.0227219128
sample164 0.0143374783 -0.0453542590
sample165 0.0343612593 -0.0511194536
sample166 -0.0157536004 0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919 0.0060747155
sample169 0.0116231468 -0.0015112800
>
> ## 3.3 Plotting VAF
>
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
>
> # JIVE plotVAF
> plotVAF(jiveRes)
>
>
> #########################
> ## PART 4. Plot Results
>
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
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
14.262 0.325 14.576
STATegRa.Rcheck/STATegRa-Ex.timings
| name | user | system | elapsed | |
| STATegRaUsersGuide | 0.001 | 0.000 | 0.001 | |
| STATegRa_data | 0.222 | 0.014 | 0.236 | |
| STATegRa_data_TCGA_BRCA | 0.001 | 0.002 | 0.002 | |
| bioDist | 0.381 | 0.034 | 0.416 | |
| bioDistFeature | 0.302 | 0.013 | 0.316 | |
| bioDistFeaturePlot | 0.307 | 0.038 | 0.345 | |
| bioDistW | 0.311 | 0.015 | 0.327 | |
| bioDistWPlot | 0.305 | 0.006 | 0.312 | |
| bioMap | 0.004 | 0.000 | 0.003 | |
| combiningMappings | 0.009 | 0.002 | 0.011 | |
| createOmicsExpressionSet | 0.138 | 0.002 | 0.140 | |
| getInitialData | 0.580 | 0.022 | 0.602 | |
| getLoadings | 0.613 | 0.050 | 0.664 | |
| getMethodInfo | 0.710 | 0.015 | 0.725 | |
| getPreprocessing | 0.715 | 0.678 | 1.393 | |
| getScores | 0.711 | 0.022 | 0.732 | |
| getVAF | 0.596 | 0.004 | 0.599 | |
| holistOmics | 0.001 | 0.001 | 0.003 | |
| modelSelection | 1.738 | 1.720 | 3.459 | |
| omicsCompAnalysis | 4.011 | 0.093 | 4.104 | |
| omicsNPC | 0.001 | 0.001 | 0.003 | |
| plotRes | 5.854 | 0.033 | 5.887 | |
| plotVAF | 7.132 | 0.027 | 8.365 | |