David Gomez-Cabrero
| Snapshot Date: 2026-03-05 13:40 -0500 (Thu, 05 Mar 2026) |
| git_url: https://git.bioconductor.org/packages/STATegRa |
| git_branch: devel |
| git_last_commit: 1932640 |
| git_last_commit_date: 2025-10-29 10:22:24 -0500 (Wed, 29 Oct 2025) |
| Back to Multiple platform build/check report for BioC 3.23: simplified long |
|
This page was generated on 2026-03-06 11:35 -0500 (Fri, 06 Mar 2026).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo1 | Linux (Ubuntu 24.04.3 LTS) | x86_64 | R Under development (unstable) (2026-01-15 r89304) -- "Unsuffered Consequences" | 4891 |
| kjohnson3 | macOS 13.7.7 Ventura | arm64 | R Under development (unstable) (2026-01-15 r89304) -- "Unsuffered Consequences" | 4593 |
| 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 2132/2357 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| STATegRa 1.47.0 (landing page) David Gomez-Cabrero
| nebbiolo1 | Linux (Ubuntu 24.04.3 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: 1.47.0 |
| Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings STATegRa_1.47.0.tar.gz |
| StartedAt: 2026-03-05 22:43:43 -0500 (Thu, 05 Mar 2026) |
| EndedAt: 2026-03-05 22:45:09 -0500 (Thu, 05 Mar 2026) |
| EllapsedTime: 86.1 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: STATegRa.Rcheck |
| Warnings: 0 |
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### Running command:
###
### /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings STATegRa_1.47.0.tar.gz
###
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* using log directory ‘/Users/biocbuild/bbs-3.23-bioc/meat/STATegRa.Rcheck’
* using R Under development (unstable) (2026-01-15 r89304)
* using platform: aarch64-apple-darwin20
* R was compiled by
Apple clang version 16.0.0 (clang-1600.0.26.6)
GNU Fortran (GCC) 14.2.0
* running under: macOS Sonoma 14.8.3
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘STATegRa/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘STATegRa’ version ‘1.47.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘STATegRa’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
modelSelection,list-numeric-character: no visible binding for global
variable ‘components’
modelSelection,list-numeric-character: no visible binding for global
variable ‘mylabel’
plotVAF,caClass: no visible binding for global variable ‘comp’
plotVAF,caClass: no visible binding for global variable ‘VAF’
plotVAF,caClass: no visible binding for global variable ‘block’
selectCommonComps,list-numeric: no visible binding for global variable
‘comps’
selectCommonComps,list-numeric: no visible binding for global variable
‘block’
selectCommonComps,list-numeric: no visible binding for global variable
‘comp’
selectCommonComps,list-numeric: no visible binding for global variable
‘ratio’
Undefined global functions or variables:
VAF block comp components comps mylabel ratio
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... NOTE
Found the following Rd file(s) with Rd \link{} targets missing package
anchors:
modelSelection.Rd: ggplot
Please provide package anchors for all Rd \link{} targets not in the
package itself and the base packages.
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
Running ‘STATEgRa_Example.omicsCLUST.R’
Running ‘STATEgRa_Example.omicsPCA.R’
Running ‘STATegRa_Example.omicsNPC.R’
Running ‘runTests.R’
OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: 2 NOTEs
See
‘/Users/biocbuild/bbs-3.23-bioc/meat/STATegRa.Rcheck/00check.log’
for details.
STATegRa.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL STATegRa ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/4.6-arm64/Resources/library’ * installing *source* package ‘STATegRa’ ... ** this is package ‘STATegRa’ version ‘1.47.0’ ** 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 Under development (unstable) (2026-01-15 r89304) -- "Unsuffered Consequences"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
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 -- Thu Mar 5 22:45:04 2026
***********************************************
Number of test functions: 4
Number of errors: 0
Number of failures: 0
1 Test Suite :
STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures
Number of test functions: 4
Number of errors: 0
Number of failures: 0
Warning messages:
1: In rownames(pData) == colnames(exprs) :
longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
>
> proc.time()
user system elapsed
1.327 0.113 1.569
STATegRa.Rcheck/tests/STATEgRa_Example.omicsCLUST.Rout
R Under development (unstable) (2026-01-15 r89304) -- "Unsuffered Consequences"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> ###########################################
> ########### EXAMPLE OF THE OMICSCLUSTERING
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
>
> #############################################
> ## PART 1: CREATING a bioMap CLASS
> #############################################
> ####### This part creates or reads the map between features.
> ####### In the present example the map is downloaded from a resource.
> ####### then the class is created.
>
> #load("../data/STATegRa_S2.rda")
> data(STATegRa_S2)
>
> MAP.SYMBOL<-bioMap(name = "Symbol-miRNA",
+ metadata = list(type_v1="Gene",type_v2="miRNA",
+ source_database="targetscan.Hs.eg.db",
+ data_extraction="July2014"),
+ map=mapdata)
>
>
> #############################################
> ## PART 2: CREATING a bioDist CLASS
> #############################################
> ##### In the second part given a set of main features and surrogate feautres,
> ##### the profile of the main features is computed through the surrogate features.
>
> # Load Data
> data(STATegRa_S1)
> #load("../data/STATegRa.S1.Rdata")
>
> ## Create ExpressionSets
> # source("../R/STATegRa_omicsPCA_classes_and_methods.R")
> # Block1 - Expression data
> mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname"))
>
> # Create Gene-gene distance computed through miRNA data
> bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1),
+ reference = "Var1",
+ mapping = MAP.SYMBOL,
+ surrogateData = miRNA.ds, ### miRNA data
+ referenceData = mRNA.ds, ### mRNA data
+ maxitems=2,
+ selectionRule="sd",
+ expfac=NULL,
+ aggregation = "sum",
+ distance = "spearman",
+ noMappingDist = 0,
+ filtering = NULL,
+ name = "mRNAbymiRNA")
>
> require(Biobase)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: generics
Attaching package: 'generics'
The following objects are masked from 'package:base':
as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
setequal, union
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
as.data.frame, basename, cbind, colnames, dirname, do.call,
duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
unsplit, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
>
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+ distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+ map.name = "id",
+ map.metadata = list(),
+ params = list())
>
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
>
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
>
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+ bioDistList = bioDistList,
+ weights=weights)
> length(bioDistWList)
[1] 4
>
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
>
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+ listDistW = bioDistWList,
+ method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
cannot compute exact p-value with ties
>
> #############################################
> ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
> #############################################
>
> ## IDH1
>
> IDH1.F<-bioDistFeature(Feature = "IDH1" ,
+ listDistW = bioDistWList,
+ threshold.cor=0.7)
> bioDistFeaturePlot(data=IDH1.F)
>
> ## PDGFRA
>
> #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.7)
> #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
>
> ## EGFR
> #EGFR.F<-bioDistFeature(Feature = "EGFR" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.7)
> #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
>
> ## MGMT
> #MGMT.F<-bioDistFeature(Feature = "MGMT" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.5)
> #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
>
>
>
>
>
> proc.time()
user system elapsed
12.628 0.227 13.685
STATegRa.Rcheck/tests/STATegRa_Example.omicsNPC.Rout
R Under development (unstable) (2026-01-15 r89304) -- "Unsuffered Consequences"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
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
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
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
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
21.706 1.052 24.279
STATegRa.Rcheck/tests/STATEgRa_Example.omicsPCA.Rout
R Under development (unstable) (2026-01-15 r89304) -- "Unsuffered Consequences"
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Platform: aarch64-apple-darwin20
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Type 'license()' or 'licence()' for distribution details.
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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 0.0781574261 0.0431499861
sample2 -0.1192218264 -0.0294091807
sample3 -0.0531412274 0.0746839933
sample4 0.0292975313 0.0005956372
sample5 0.0202091814 -0.0110464112
sample6 0.1226089085 -0.1053465827
sample7 0.1078927956 0.0322478529
sample8 0.1782895499 -0.1449365656
sample9 0.0468698164 0.0455174459
sample10 -0.0036030404 -0.0420112626
sample11 -0.0035566477 0.0566292980
sample12 0.1006128872 -0.0641380133
sample13 -0.1174408140 -0.0907489033
sample14 0.0981203245 -0.0617736899
sample15 0.0085334170 0.0087016325
sample16 0.0783148748 -0.1581292561
sample17 -0.1483609884 -0.0638582030
sample18 -0.0963086342 -0.0556637834
sample19 -0.0217244135 0.0720084105
sample20 -0.0635636527 0.0779655244
sample21 -0.0201840163 -0.1566391803
sample22 0.0218268488 0.0764106798
sample23 0.0852042165 0.0032685059
sample24 -0.1287170121 -0.1924549348
sample25 -0.0430574075 0.0456562566
sample26 -0.1453896701 -0.0541514789
sample27 -0.0197489059 0.1185659968
sample28 -0.1025336158 -0.0650687029
sample29 0.0706018299 0.0682990830
sample30 -0.1295627750 0.0066773822
sample31 0.1147449114 -0.1232684632
sample32 -0.0374311002 -0.0380174422
sample33 0.0599515766 -0.0136863812
sample34 -0.0984200889 -0.0375318910
sample35 -0.0543098471 0.0378109412
sample36 0.1403625310 0.0343761919
sample37 0.0228941593 0.0732854019
sample38 -0.0222077459 0.0962596176
sample39 -0.0941738455 -0.0215200111
sample40 0.0643800861 0.0687878461
sample41 -0.0327638170 0.1232188212
sample42 -0.0500431846 0.0292471345
sample43 -0.0184498898 -0.0233009412
sample44 0.1487899234 -0.1171361466
sample45 -0.1050773951 -0.1123204580
sample46 -0.1151195919 0.1094030394
sample47 -0.0962593837 0.0288466003
sample48 0.0004837555 0.0310272818
sample49 0.1135207976 -0.1213974802
sample50 -0.0123553279 0.1740742745
sample51 0.0550529988 -0.1258884481
sample52 0.0499121360 -0.0728542695
sample53 0.1119773703 -0.1588011332
sample54 -0.0360055672 -0.0228575087
sample55 0.0210419005 -0.0006730754
sample56 -0.0434169120 -0.0633125785
sample57 0.0197824803 -0.1150711527
sample58 0.0030439852 -0.0326096506
sample59 0.0500252941 -0.0129413638
sample60 0.0184278581 -0.0136078561
sample61 0.0150299437 -0.0635022190
sample62 -0.0304764151 0.0201323523
sample63 0.1102252592 -0.1285977400
sample64 0.1552588149 -0.0971167488
sample65 -0.0058503015 -0.0207116096
sample66 -0.0025605240 -0.0424321752
sample67 0.1546634625 0.0661723553
sample68 0.0536369026 0.0923687831
sample69 0.0640330241 -0.0081982014
sample70 0.0163517548 0.0663230241
sample71 -0.0102537705 0.1345919126
sample72 -0.0654196257 0.0196123868
sample73 -0.1048556282 -0.0220935046
sample74 0.0123799430 -0.0586112935
sample75 0.0392077849 0.0209756554
sample76 0.0648953319 0.0524764657
sample77 0.1172922101 0.0201185993
sample78 -0.1463067867 -0.0708476243
sample79 0.0265211311 0.1603301791
sample80 0.0279737050 0.0214207837
sample81 0.0079211461 0.0738448510
sample82 -0.1544236607 0.0361468816
sample83 -0.0494211701 0.0050054282
sample84 -0.0259038405 0.0346547374
sample85 0.1116484194 0.0031502667
sample86 -0.1306483238 0.0377218320
sample87 -0.0554778219 0.0459749429
sample88 -0.0301623711 -0.0382197077
sample89 -0.1016866741 -0.0694031461
sample90 0.0086819787 0.0201319922
sample91 0.1578625106 0.2097829920
sample92 0.0170937000 0.1655800031
sample93 -0.0979806909 0.0121512876
sample94 0.0131483987 0.0114932229
sample95 0.0315682636 0.0758855999
sample96 0.0024125605 0.0470132906
sample97 0.0634545394 -0.0270333409
sample98 -0.0359374745 0.0135489626
sample99 -0.1009163101 -0.1124783671
sample100 0.0551753080 -0.0246488890
sample101 -0.0080119046 0.1627366431
sample102 -0.0046443951 -0.0095640573
sample103 -0.0472523300 0.0940393831
sample104 0.0198159590 0.0591088565
sample105 -0.0400237735 0.0160909983
sample106 -0.0923808327 -0.0369018479
sample107 -0.1019374029 -0.0224953436
sample108 -0.0877091661 0.0128833355
sample109 0.0864824603 0.0900934096
sample110 -0.1223115490 0.0096084467
sample111 0.0257354749 0.0936162988
sample112 -0.0765286641 -0.0270345818
sample113 0.0258803422 -0.0377500463
sample114 0.0021138807 0.0882013502
sample115 0.0303460463 0.0723577434
sample116 0.0780508635 0.0685061056
sample117 0.0536898321 0.0911901956
sample118 0.0666651217 0.0236229393
sample119 0.1021871580 0.2324933084
sample120 0.0750216568 -0.0243380985
sample121 -0.0756936320 -0.0942949499
sample122 -0.0259627872 -0.0731990838
sample123 -0.1037846341 0.0369198235
sample124 0.0611208074 -0.0421727895
sample125 -0.0738472722 -0.0066950449
sample126 0.0972916299 -0.0762636756
sample127 0.0824697537 0.0096636895
sample128 -0.1249407439 -0.0929315944
sample129 -0.0734067722 0.0434365651
sample130 -0.0003502098 0.0309852420
sample131 0.0930182747 -0.0155935308
sample132 0.0736222932 -0.0733033021
sample133 -0.0498397959 0.0462436441
sample134 0.1644873537 -0.0720003330
sample135 -0.0752297317 -0.0003814877
sample136 0.0227145548 0.0495508055
sample137 0.0564717180 0.0288918809
sample138 0.0255988198 0.0610852944
sample139 0.0621217749 -0.0235804907
sample140 -0.0604152730 0.0435596630
sample141 0.0246744019 -0.0532649709
sample142 -0.0409560140 -0.0316282391
sample143 -0.0077355161 0.0476895581
sample144 0.0173240815 0.0156777500
sample145 0.0485474883 -0.1202772425
sample146 0.0419645400 0.0811283537
sample147 -0.0977308551 0.0274843869
sample148 0.0368256340 -0.0803980235
sample149 -0.0072865843 0.1532984289
sample150 0.1020825276 -0.0624771976
sample151 0.0305399166 0.0289274926
sample152 -0.0533594777 0.0638307663
sample153 -0.0891627021 -0.1799585307
sample154 -0.0727557638 0.0834162652
sample155 -0.0880668713 0.0220822313
sample156 -0.0276561167 0.0326626613
sample157 -0.1155032223 -0.0183615009
sample158 -0.0281507567 0.0104940115
sample159 0.0663235813 -0.0443838867
sample160 -0.0302643885 -0.0404263490
sample161 0.0114715705 0.0591022260
sample162 -0.1337086916 -0.1398135716
sample163 0.1330124681 -0.1688781944
sample164 -0.0150335985 -0.0028418447
sample165 0.0076520349 0.0164127420
sample166 0.0367794527 -0.0630664698
sample167 0.1111988815 -0.0030057380
sample168 -0.0672981528 -0.0446280008
sample169 -0.0413005045 -0.0224391344
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 0.0420517568 0.0867862933
sample2 0.0820827457 -0.0410978520
sample3 -0.0155896229 -0.0195182158
sample4 0.1001336815 -0.0410787227
sample5 0.0153465246 -0.0253259786
sample6 -0.0340330503 -0.0408223189
sample7 -0.0722578549 0.0002332680
sample8 0.0457493531 -0.0370016719
sample9 0.0086251041 0.0820184925
sample10 0.0423596865 -0.0083923582
sample11 -0.0022546201 0.0787766147
sample12 -0.0322107395 0.1479824755
sample13 0.0293885464 -0.0306748928
sample14 -0.0337485465 -0.0367506779
sample15 -0.0815537989 0.1275622950
sample16 -0.0508458682 0.0540604669
sample17 -0.0062598943 0.0041023654
sample18 -0.0705641931 -0.0351047389
sample19 0.0476844846 -0.0509598226
sample20 -0.0522959339 0.0715522276
sample21 0.0119119970 -0.0376093411
sample22 -0.0724389606 -0.0095624592
sample23 0.0992532299 0.0134288249
sample24 0.1595110139 0.0728660834
sample25 0.0920695119 -0.0749757692
sample26 0.0595538643 0.0848965635
sample27 -0.0826480706 -0.0086734778
sample28 0.0384785721 0.0440966562
sample29 -0.0777667782 0.1735309051
sample30 -0.1229471123 -0.0819004829
sample31 -0.0579851576 -0.0238644643
sample32 -0.0970394654 -0.0111425827
sample33 -0.1017588439 -0.0630442031
sample34 -0.0637923969 0.0377942005
sample35 -0.0789983433 -0.0229722724
sample36 -0.1224939629 -0.1274954222
sample37 -0.1798819322 -0.1673426323
sample38 -0.0466299634 0.0888161363
sample39 0.0168687261 0.0421533632
sample40 -0.1756390690 -0.1526641281
sample41 -0.0042365410 0.0004929031
sample42 0.0447851030 -0.0651505199
sample43 -0.0482309319 -0.0253529043
sample44 0.1986709062 -0.0545779165
sample45 0.0741831975 0.0054702695
sample46 -0.0478767208 -0.0007071567
sample47 -0.0608186986 0.0481623025
sample48 0.1381490770 0.0578287062
sample49 0.0530514365 -0.1405533332
sample50 0.0173808233 0.1602389862
sample51 -0.0462566768 0.0303473878
sample52 -0.0280068834 0.0280388421
sample53 -0.0667628176 0.0237702142
sample54 -0.0121834761 -0.0521354291
sample55 -0.0182396117 0.0221328530
sample56 0.0001252618 0.0030907253
sample57 -0.0316681029 0.0530190260
sample58 -0.0393919667 -0.0297798582
sample59 -0.1278291990 -0.0546527270
sample60 -0.1486986247 0.1069157351
sample61 -0.0793125543 0.0569796854
sample62 -0.1172799869 -0.0149197802
sample63 0.0028722001 0.1300519600
sample64 -0.0237368486 0.1073287682
sample65 0.0126534421 0.0589808334
sample66 0.0468192625 -0.0771073022
sample67 -0.1494263382 -0.0769859370
sample68 -0.0977957729 -0.0577350361
sample69 -0.0403087127 0.0156042327
sample70 -0.0221527570 0.0315441194
sample71 0.0546440417 -0.0272396514
sample72 -0.1107487056 -0.0537318726
sample73 -0.0906761513 0.0579967078
sample74 -0.0586557739 0.0121421901
sample75 -0.0390492191 0.0349283062
sample76 0.0022961916 -0.1676558722
sample77 0.0232095952 -0.2067302895
sample78 0.0929751890 -0.0434940112
sample79 0.1619503283 -0.0378114865
sample80 -0.0680364013 0.1424663904
sample81 0.0530787343 -0.0358351016
sample82 -0.0266820362 -0.0577444890
sample83 -0.1517234807 -0.0448553488
sample84 0.0570968459 -0.0273813497
sample85 -0.1086290702 -0.1228118733
sample86 -0.0833858162 -0.0442914453
sample87 -0.0022017136 -0.0943906766
sample88 0.0078222015 -0.1140506642
sample89 -0.0611060128 -0.0094584926
sample90 -0.0022927332 -0.0936253944
sample91 -0.0433580297 0.3205983365
sample92 0.1815342128 -0.0334681018
sample93 -0.0267629458 0.0614429201
sample94 -0.0181876449 0.0605090530
sample95 0.0720379163 -0.0013045924
sample96 0.0559717145 -0.0118791643
sample97 0.0217410477 0.0195413982
sample98 -0.0379176173 0.0588357341
sample99 0.0792422745 -0.0151274418
sample100 -0.0222117330 -0.0023321353
sample101 0.0387236692 0.1224226280
sample102 0.2094613511 -0.0516443785
sample103 -0.0138476907 0.0301052189
sample104 0.0807989218 -0.0162719265
sample105 0.0520493452 -0.1229665422
sample106 0.0192611628 -0.0185238350
sample107 -0.0319017322 0.0405123434
sample108 0.0140691937 0.0163421331
sample109 0.1831934219 0.0613006702
sample110 0.0292791045 -0.0199849221
sample111 0.1423256431 0.0327339705
sample112 -0.0426333952 -0.0029083249
sample113 0.0771902981 0.0268733160
sample114 0.0241645563 -0.0184080398
sample115 0.1959018934 0.0460129725
sample116 0.1394478170 -0.0530806463
sample117 0.1672364637 -0.1386537169
sample118 0.0448344993 -0.0117622143
sample119 0.0910397228 0.2217433265
sample120 0.0331391499 -0.0057274718
sample121 -0.0307578182 0.1392506559
sample122 0.0839777980 -0.0291995001
sample123 -0.0239648999 -0.0642163528
sample124 0.0909149171 0.0130418934
sample125 0.0065350220 -0.1092631856
sample126 -0.0935314036 0.1368284431
sample127 -0.0035386896 0.0292755672
sample128 0.0660292376 0.1018565816
sample129 -0.0693636747 -0.0695421283
sample130 -0.0008492095 -0.0669704269
sample131 -0.0431024698 0.0174065073
sample132 0.0637037355 0.0029374253
sample133 0.0289496488 -0.0390818911
sample134 -0.0446206473 0.0456334621
sample135 -0.0712336595 0.0521635347
sample136 -0.0596268313 0.0197299733
sample137 -0.0793150633 -0.0380627825
sample138 0.0973550766 -0.0454218692
sample139 -0.0539906898 -0.1534327112
sample140 -0.0850824459 0.0955815071
sample141 0.0192679446 -0.0554450260
sample142 0.0672260185 -0.0461321333
sample143 0.0303731761 -0.0519260327
sample144 0.0089365381 0.0145814892
sample145 0.0638763890 0.0122257854
sample146 -0.0585852557 0.0063083803
sample147 -0.0894132565 -0.1124615164
sample148 0.0216362846 -0.0615967380
sample149 0.0515426627 -0.0839903521
sample150 -0.0568286436 -0.0124468722
sample151 0.0789533464 -0.0261831568
sample152 0.0330756881 0.1306443512
sample153 0.1751923655 0.1497730850
sample154 -0.0421420624 -0.0037009824
sample155 -0.0680176288 0.0095711641
sample156 -0.0388908934 0.1057563228
sample157 -0.0314769661 0.0561367571
sample158 -0.0329619989 0.0353947520
sample159 0.0398414023 -0.1007374066
sample160 -0.0424940609 0.0108496341
sample161 0.0888373094 -0.0679700560
sample162 0.0027470521 0.1237843620
sample163 0.0126098206 0.0725433992
sample164 0.0566779249 -0.0458324494
sample165 0.0315336681 -0.0236362496
sample166 0.0612055240 -0.0425233475
sample167 -0.0142729891 0.0179308345
sample168 0.0169501258 -0.0769618061
sample169 -0.0675081311 0.0131505661
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 -0.0012329572 -1.635717e-01
sample2 -0.0724349941 -6.021221e-03
sample3 -0.0188460464 -1.080036e-01
sample4 0.0390145370 3.114466e-04
sample5 0.1774811678 -2.996383e-02
sample6 -0.0451444344 -3.455856e-02
sample7 -0.0226466361 -7.020203e-03
sample8 -0.1033680063 -9.856732e-03
sample9 0.1350011641 8.979098e-02
sample10 0.1259887362 -5.097849e-02
sample11 0.0979788266 7.086533e-02
sample12 -0.0863019028 -8.620317e-02
sample13 -0.1381401085 1.828007e-01
sample14 -0.0615073827 -2.642803e-02
sample15 0.0381598884 -3.101666e-02
sample16 -0.0048776626 1.271877e-03
sample17 -0.0788480861 -1.547552e-02
sample18 -0.0884188729 -3.795487e-02
sample19 0.0703044457 -1.084004e-01
sample20 -0.0025585692 7.975870e-02
sample21 0.0941601861 -4.126734e-02
sample22 -0.0550273518 -7.806749e-02
sample23 0.0679495409 -4.102002e-02
sample24 -0.1310962525 1.649310e-01
sample25 0.0113585350 -4.426861e-02
sample26 -0.1402945796 2.016547e-02
sample27 0.0261560928 1.588377e-03
sample28 -0.0724198635 5.850596e-02
sample29 -0.0330058726 2.060771e-03
sample30 -0.0228752653 -2.015434e-02
sample31 -0.0635067847 -6.670332e-02
sample32 0.0685099632 -4.955274e-02
sample33 -0.0777765232 -1.272079e-01
sample34 0.0157842423 -3.024314e-02
sample35 -0.0529632957 1.500972e-01
sample36 0.0070900468 2.025307e-01
sample37 -0.0442420955 1.802088e-01
sample38 -0.0781511419 -3.676426e-02
sample39 0.0120331929 -3.388839e-02
sample40 -0.0473292406 1.471561e-01
sample41 0.0228189297 -2.673559e-02
sample42 -0.0245360179 -7.960866e-02
sample43 0.1036362838 -8.229577e-02
sample44 -0.1012228550 7.049461e-02
sample45 0.0013732258 -2.450904e-02
sample46 -0.0558510165 2.947329e-03
sample47 -0.0380481281 4.554171e-02
sample48 0.0784342153 4.888984e-02
sample49 -0.0605163793 -1.162350e-02
sample50 0.0530079120 -2.737938e-02
sample51 0.1514646577 5.678349e-02
sample52 0.1860935203 1.246717e-01
sample53 -0.0064176991 -2.700990e-02
sample54 0.0697038374 -2.308388e-02
sample55 0.1633577005 1.366442e-02
sample56 0.1011485146 4.682208e-02
sample57 0.1730374204 1.609604e-01
sample58 -0.0071384700 -1.666955e-02
sample59 -0.0030461817 3.005281e-02
sample60 0.0215834847 2.665877e-01
sample61 0.1510583567 1.002385e-01
sample62 -0.0925534051 -4.845847e-02
sample63 -0.0596311642 -4.137018e-02
sample64 -0.0449225738 -2.600563e-03
sample65 0.0939383822 -4.406907e-02
sample66 0.1063400877 -5.709989e-02
sample67 -0.0201590391 2.361727e-01
sample68 0.0037202991 2.418382e-02
sample69 -0.0645161175 -1.155622e-01
sample70 -0.1013440025 -1.351789e-01
sample71 -0.0016467948 -2.976845e-02
sample72 0.0328892921 -2.835861e-02
sample73 0.0275080027 -5.148187e-02
sample74 0.1341719739 -7.895279e-02
sample75 0.0951575622 -3.943186e-02
sample76 -0.0864722045 3.034989e-02
sample77 -0.1035749567 -2.545355e-02
sample78 -0.1575643981 4.939599e-02
sample79 0.0189137029 4.874679e-02
sample80 0.1384140495 4.262997e-05
sample81 -0.0118846449 -6.357933e-02
sample82 -0.1675308235 3.533909e-02
sample83 -0.0065673504 -7.812615e-02
sample84 0.1486891652 -3.109055e-02
sample85 -0.0532724589 7.417880e-02
sample86 -0.1138477439 -1.919687e-05
sample87 0.0432863903 6.080471e-02
sample88 0.0433450335 1.402491e-01
sample89 0.0331205817 -1.395399e-02
sample90 -0.0607412787 -8.610415e-02
sample91 -0.0566273005 1.303746e-01
sample92 -0.0359582576 1.061604e-01
sample93 -0.0433646360 -4.443636e-02
sample94 -0.0477291267 -1.059574e-01
sample95 -0.0249595764 -3.980526e-02
sample96 0.0035219069 -9.293928e-02
sample97 -0.0066048619 -1.527231e-01
sample98 0.0020366805 -5.579551e-02
sample99 -0.0886615857 -3.728219e-02
sample100 -0.1091259116 -3.560421e-02
sample101 -0.0739726595 -4.318004e-02
sample102 0.0574461385 -2.783906e-02
sample103 0.0142730907 9.705522e-03
sample104 0.0710395197 4.068352e-02
sample105 0.0980831409 -3.452951e-02
sample106 -0.0254259263 3.628986e-02
sample107 -0.0160653384 -9.173394e-02
sample108 -0.0200987630 -2.379692e-02
sample109 -0.0389780598 1.692361e-02
sample110 -0.0326304829 2.988110e-02
sample111 0.0676937634 -6.038211e-02
sample112 0.0167883429 5.336939e-03
sample113 0.0969217146 -2.757599e-02
sample114 -0.0026398368 -9.209159e-02
sample115 -0.0308047222 1.603826e-02
sample116 -0.1240307204 1.273000e-01
sample117 0.0334729103 5.392713e-02
sample118 -0.1037152949 6.252430e-02
sample119 -0.1064176910 1.196202e-01
sample120 -0.0771354982 -1.004932e-01
sample121 -0.0129350688 3.181978e-02
sample122 0.0847492490 -5.568320e-02
sample123 -0.0041336831 7.693163e-03
sample124 -0.0583457823 -8.396385e-02
sample125 0.0634844657 -5.232539e-02
sample126 -0.0662580903 -1.091733e-01
sample127 -0.0865024563 -1.094176e-01
sample128 -0.0627817241 -1.470958e-02
sample129 -0.0336276529 -4.007863e-02
sample130 -0.0293517732 -8.046118e-02
sample131 -0.0469197703 -2.209766e-03
sample132 -0.0241740470 -1.248598e-01
sample133 0.0907303188 1.466701e-02
sample134 -0.0350842116 7.539662e-02
sample135 0.0001333342 9.185358e-03
sample136 -0.0335876114 -9.860278e-02
sample137 -0.0640148980 -7.554474e-02
sample138 0.0060964879 -1.742761e-02
sample139 -0.0592084532 5.614967e-02
sample140 0.0427985812 -1.099555e-02
sample141 0.0618796516 -9.301035e-02
sample142 0.0898554553 3.573422e-02
sample143 0.0817389139 8.880524e-02
sample144 0.0787754794 -3.821391e-02
sample145 0.1085821670 1.569477e-01
sample146 -0.0589558066 -4.373366e-02
sample147 -0.0495330550 7.277163e-03
sample148 0.1161592890 9.079126e-03
sample149 -0.0121579622 7.788370e-02
sample150 -0.0314512564 3.520212e-02
sample151 0.0575382225 -1.945351e-02
sample152 -0.0494542173 7.025536e-02
sample153 -0.0941332492 2.153298e-01
sample154 -0.0335932144 2.078724e-02
sample155 0.0690457589 -2.780412e-02
sample156 0.1039901590 -6.292527e-02
sample157 -0.0408645782 8.065515e-03
sample158 0.1018105263 7.816869e-03
sample159 -0.0281730453 -1.207204e-02
sample160 0.1643053015 2.978117e-03
sample161 0.0374329219 8.524611e-02
sample162 -0.0804535200 8.349760e-02
sample163 -0.0743227787 -1.406219e-02
sample164 0.1208806087 -2.139457e-02
sample165 0.1608115914 2.025193e-02
sample166 -0.0425944499 -2.660710e-02
sample167 -0.0226849480 -4.464283e-02
sample168 -0.0180735507 -7.465952e-04
sample169 0.0190778954 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
4.546 0.251 5.110
STATegRa.Rcheck/STATegRa-Ex.timings
| name | user | system | elapsed | |
| STATegRaUsersGuide | 0.000 | 0.000 | 0.001 | |
| STATegRa_data | 0.119 | 0.005 | 0.127 | |
| STATegRa_data_TCGA_BRCA | 0.001 | 0.001 | 0.001 | |
| bioDist | 0.162 | 0.007 | 0.178 | |
| bioDistFeature | 0.134 | 0.008 | 0.146 | |
| bioDistFeaturePlot | 0.144 | 0.009 | 0.158 | |
| bioDistW | 0.140 | 0.008 | 0.151 | |
| bioDistWPlot | 0.136 | 0.006 | 0.145 | |
| bioMap | 0.001 | 0.000 | 0.001 | |
| combiningMappings | 0.003 | 0.000 | 0.004 | |
| createOmicsExpressionSet | 0.064 | 0.001 | 0.070 | |
| getInitialData | 0.237 | 0.069 | 0.328 | |
| getLoadings | 0.240 | 0.111 | 0.383 | |
| getMethodInfo | 0.292 | 0.077 | 0.372 | |
| getPreprocessing | 0.298 | 0.327 | 0.646 | |
| getScores | 0.299 | 0.095 | 0.439 | |
| getVAF | 0.217 | 0.069 | 0.286 | |
| holistOmics | 0.001 | 0.000 | 0.001 | |
| modelSelection | 0.660 | 0.631 | 1.311 | |
| omicsCompAnalysis | 0.671 | 0.149 | 0.882 | |
| omicsNPC | 0.001 | 0.001 | 0.003 | |
| plotRes | 1.159 | 0.178 | 1.512 | |
| plotVAF | 1.156 | 0.167 | 1.463 | |