David Gomez-Cabrero
| Snapshot Date: 2026-05-21 13:45 -0400 (Thu, 21 May 2026) |
| git_url: https://git.bioconductor.org/packages/STATegRa |
| git_branch: devel |
| git_last_commit: 384a5d7 |
| git_last_commit_date: 2026-04-28 08:40:02 -0400 (Tue, 28 Apr 2026) |
| Back to Multiple platform build/check report for BioC 3.24: simplified long |
|
This page was generated on 2026-05-22 11:37 -0400 (Fri, 22 May 2026).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo2 | Linux (Ubuntu 24.04.4 LTS) | x86_64 | 4.6.0 RC (2026-04-17 r89917) -- "Because it was There" | 4936 |
| kjohnson3 | macOS 13.7.7 Ventura | arm64 | 4.6.0 Patched (2026-05-01 r89994) -- "Because it was There" | 4621 |
| 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 2152/2378 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| STATegRa 1.49.0 (landing page) David Gomez-Cabrero
| nebbiolo2 | Linux (Ubuntu 24.04.4 LTS) / x86_64 | OK | OK | OK | | ||||||||
| kjohnson3 | macOS 13.7.7 Ventura / arm64 | 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: 1.49.0 |
| 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_1.49.0.tar.gz |
| StartedAt: 2026-05-21 22:59:12 -0400 (Thu, 21 May 2026) |
| EndedAt: 2026-05-21 23:06:33 -0400 (Thu, 21 May 2026) |
| EllapsedTime: 440.2 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_1.49.0.tar.gz
###
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* using log directory ‘/Users/biocbuild/bbs-3.24-bioc/meat/STATegRa.Rcheck’
* using R version 4.6.0 Patched (2026-05-01 r89994)
* using platform: aarch64-apple-darwin23
* R was compiled by
Apple clang version 17.0.0 (clang-1700.3.19.1)
GNU Fortran (GCC) 14.2.0
* running under: macOS Tahoe 26.3.1
* using session charset: UTF-8
* current time: 2026-05-22 02:59:13 UTC
* using option ‘--no-vignettes’
* checking for file ‘STATegRa/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘STATegRa’ version ‘1.49.0’
* 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-3.24-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/Resources/library’ * installing *source* package ‘STATegRa’ ... ** this is package ‘STATegRa’ version ‘1.49.0’ ** 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 Patched (2026-05-01 r89994) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin23
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> BiocGenerics:::testPackage("STATegRa")
Common components
[1] 2
Distinctive components
[[1]]
[1] 0
[[2]]
[1] 0
Common components
[1] 2
Distinctive components
[[1]]
[1] 1
[[2]]
[1] 1
Common components
[1] 2
Distinctive components
[[1]]
[1] 2
[[2]]
[1] 2
RUNIT TEST PROTOCOL -- Thu May 21 23:06:30 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
1.156 0.052 1.206
STATegRa.Rcheck/tests/STATEgRa_Example.omicsCLUST.Rout
R version 4.6.0 Patched (2026-05-01 r89994) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin23
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> ###########################################
> ########### EXAMPLE OF THE OMICSCLUSTERING
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
>
> #############################################
> ## PART 1: CREATING a bioMap CLASS
> #############################################
> ####### This part creates or reads the map between features.
> ####### In the present example the map is downloaded from a resource.
> ####### then the class is created.
>
> #load("../data/STATegRa_S2.rda")
> data(STATegRa_S2)
>
> MAP.SYMBOL<-bioMap(name = "Symbol-miRNA",
+ metadata = list(type_v1="Gene",type_v2="miRNA",
+ source_database="targetscan.Hs.eg.db",
+ data_extraction="July2014"),
+ map=mapdata)
>
>
> #############################################
> ## PART 2: CREATING a bioDist CLASS
> #############################################
> ##### In the second part given a set of main features and surrogate feautres,
> ##### the profile of the main features is computed through the surrogate features.
>
> # Load Data
> data(STATegRa_S1)
> #load("../data/STATegRa.S1.Rdata")
>
> ## Create ExpressionSets
> # source("../R/STATegRa_omicsPCA_classes_and_methods.R")
> # Block1 - Expression data
> mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname"))
>
> # Create Gene-gene distance computed through miRNA data
> bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1),
+ reference = "Var1",
+ mapping = MAP.SYMBOL,
+ surrogateData = miRNA.ds, ### miRNA data
+ referenceData = mRNA.ds, ### mRNA data
+ maxitems=2,
+ selectionRule="sd",
+ expfac=NULL,
+ aggregation = "sum",
+ distance = "spearman",
+ noMappingDist = 0,
+ filtering = NULL,
+ name = "mRNAbymiRNA")
>
> require(Biobase)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: generics
Attaching package: 'generics'
The following objects are masked from 'package:base':
as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
setequal, union
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
as.data.frame, basename, cbind, colnames, dirname, do.call,
duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
unsplit, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
>
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+ distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+ map.name = "id",
+ map.metadata = list(),
+ params = list())
>
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
>
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
>
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+ bioDistList = bioDistList,
+ weights=weights)
> length(bioDistWList)
[1] 4
>
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
>
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+ listDistW = bioDistWList,
+ method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
cannot compute exact p-value with ties
>
> #############################################
> ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
> #############################################
>
> ## IDH1
>
> IDH1.F<-bioDistFeature(Feature = "IDH1" ,
+ listDistW = bioDistWList,
+ threshold.cor=0.7)
> bioDistFeaturePlot(data=IDH1.F)
>
> ## PDGFRA
>
> #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.7)
> #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
>
> ## EGFR
> #EGFR.F<-bioDistFeature(Feature = "EGFR" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.7)
> #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
>
> ## MGMT
> #MGMT.F<-bioDistFeature(Feature = "MGMT" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.5)
> #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
>
>
>
>
>
> proc.time()
user system elapsed
7.489 0.121 7.606
STATegRa.Rcheck/tests/STATegRa_Example.omicsNPC.Rout
R version 4.6.0 Patched (2026-05-01 r89994) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin23
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> rm(list = ls())
> require("STATegRa")
Loading required package: STATegRa
> # Load the data
> data("TCGA_BRCA_Batch_93")
> # Setting dataTypes
> dataTypes <- c("count", "count", "continuous")
> # Setting methods to combine pvalues
> combMethods = c("Fisher", "Liptak", "Tippett")
> # Setting number of permutations
> numPerms = 1000
> # Setting number of cores
> numCores = 1
> # Setting holistOmics to print out the steps that it performs.
> verbose = TRUE
> # Run holistOmics analysis.
> output <- omicsNPC(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes, combMethods = combMethods, numPerms = numPerms, numCores = numCores, verbose = verbose)
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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
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
16.017 0.143 16.157
STATegRa.Rcheck/tests/STATEgRa_Example.omicsPCA.Rout
R version 4.6.0 Patched (2026-05-01 r89994) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin23
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> ###########################################
> ########### EXAMPLE OF THE OMICSPCA
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
>
> # g_legend (not exported by STATegRa any more)
> ## code from https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs
> g_legend<-function(a.gplot){
+ tmp <- ggplot_gtable(ggplot_build(a.gplot))
+ leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
+ legend <- tmp$grobs[[leg]]
+ return(legend)}
>
> #########################
> ## PART 1. Load data
>
> ## Load data
> data(STATegRa_S3)
>
> ls()
[1] "Block1.PCA" "Block2.PCA" "ed.PCA" "g_legend"
>
> ## Create ExpressionSets
> # Block1 - Expression data
> B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname"))
>
> #########################
> ## PART 2. Model Selection
>
> require(grid)
Loading required package: grid
> require(gridExtra)
Loading required package: gridExtra
> require(ggplot2)
Loading required package: ggplot2
>
> ## Select the optimal components
> ms <- modelSelection(Input=list(B1,B2),Rmax=4,fac.sel="single%",varthreshold=0.03,center=TRUE,scale=TRUE,weight=TRUE)
Common components
[1] 2
Distinctive components
[[1]]
[1] 2
[[2]]
[1] 2
>
>
> #########################
> ## PART 3. Component Analysis
>
> ## 3.1 Component analysis of the three methods
> discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+ scale=TRUE,weight=TRUE)
> jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+ scale=TRUE,weight=TRUE)
> o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+ scale=TRUE,weight=TRUE)
>
> ## 3.2 Exploring scores structures
>
> # Exploring DISCO-SCA scores structure
> discoRes@scores$common ## Common scores
1 2
sample1 0.0781574355 -0.0431503292
sample2 -0.1192218483 0.0294086877
sample3 -0.0531412025 -0.0746839762
sample4 0.0292975067 -0.0005961850
sample5 0.0202091743 0.0110463472
sample6 0.1226089040 0.1053467763
sample7 0.1078928192 -0.0322474333
sample8 0.1782895191 0.1449363180
sample9 0.0468698107 -0.0455174283
sample10 -0.0036030562 0.0420110157
sample11 -0.0035566466 -0.0566292438
sample12 0.1006128925 0.0641381125
sample13 -0.1174408468 0.0907488078
sample14 0.0981203265 0.0617738789
sample15 0.0085334387 -0.0087012079
sample16 0.0783148613 0.1581295452
sample17 -0.1483609941 0.0638581905
sample18 -0.0963086208 0.0556641385
sample19 -0.0217244061 -0.0720087250
sample20 -0.0635636344 -0.0779652068
sample21 -0.0201840429 0.1566391104
sample22 0.0218268861 -0.0764103187
sample23 0.0852041947 -0.0032690713
sample24 -0.1287170919 0.1924540654
sample25 -0.0430574185 -0.0456567940
sample26 -0.1453896920 0.0541510847
sample27 -0.0197488677 -0.1185655227
sample28 -0.1025336388 0.0650684794
sample29 0.0706018589 -0.0682986735
sample30 -0.1295627416 -0.0066767020
sample31 0.1147449126 0.1232687655
sample32 -0.0374310805 0.0380179732
sample33 0.0599516113 0.0136868939
sample34 -0.0984200778 0.0375322141
sample35 -0.0543098327 -0.0378104165
sample36 0.1403625505 -0.0343753234
sample37 0.0228942021 -0.0732842396
sample38 -0.0222077151 -0.0962594120
sample39 -0.0941738508 0.0215198728
sample40 0.0643801298 -0.0687867247
sample41 -0.0327637947 -0.1232188142
sample42 -0.0500431833 -0.0292474340
sample43 -0.0184498776 0.0233011879
sample44 0.1487898540 0.1171350773
sample45 -0.1050774290 0.1123200041
sample46 -0.1151195617 -0.1094027940
sample47 -0.0962593676 -0.0288462609
sample48 0.0004837211 -0.0310280367
sample49 0.1135207688 0.1213972108
sample50 -0.0123553044 -0.1740744179
sample51 0.0550529814 0.1258887739
sample52 0.0499121165 0.0728545377
sample53 0.1119773631 0.1588015080
sample54 -0.0360055673 0.0228575828
sample55 0.0210418997 0.0006732150
sample56 -0.0434169284 0.0633126162
sample57 0.0197824521 0.1150714504
sample58 0.0030439912 0.0326098705
sample59 0.0500253208 0.0129421277
sample60 0.0184278691 0.0136088418
sample61 0.0150299406 0.0635027459
sample62 -0.0304763775 -0.0201317328
sample63 0.1102252417 0.1285976768
sample64 0.1552588056 0.0971168747
sample65 -0.0058503070 0.0207115143
sample66 -0.0025605394 0.0424319115
sample67 0.1546634911 -0.0661713278
sample68 0.0536369393 -0.0923681908
sample69 0.0640330432 0.0081983571
sample70 0.0163517837 -0.0663230010
sample71 -0.0102537594 -0.1345922365
sample72 -0.0654195941 -0.0196117668
sample73 -0.1048556069 0.0220939645
sample74 0.0123799508 0.0586116035
sample75 0.0392077983 -0.0209754413
sample76 0.0648953406 -0.0524764283
sample77 0.1172922137 -0.0201186997
sample78 -0.1463068203 0.0708470831
sample79 0.0265211138 -0.1603310544
sample80 0.0279737205 -0.0214203997
sample81 0.0079211509 -0.0738451830
sample82 -0.1544236470 -0.0361467510
sample83 -0.0494211261 -0.0050046113
sample84 -0.0259038495 -0.0346550514
sample85 0.1116484422 -0.0031495695
sample86 -0.1306482947 -0.0377213896
sample87 -0.0554778192 -0.0459748742
sample88 -0.0301623905 0.0382197750
sample89 -0.1016866702 0.0694034768
sample90 0.0086819915 -0.0201320187
sample91 0.1578625433 -0.2097827255
sample92 0.0170936755 -0.1655809680
sample93 -0.0979806783 -0.0121511992
sample94 0.0131484141 -0.0114932024
sample95 0.0315682623 -0.0758860320
sample96 0.0024125620 -0.0470136600
sample97 0.0634545421 0.0270331315
sample98 -0.0359374592 -0.0135487988
sample99 -0.1009163419 0.1124778659
sample100 0.0551753148 0.0246489839
sample101 -0.0080118835 -0.1627369309
sample102 -0.0046444458 0.0095628717
sample103 -0.0472523129 -0.0940393105
sample104 0.0198159442 -0.0591092719
sample105 -0.0400237821 -0.0160912756
sample106 -0.0923808454 0.0369017447
sample107 -0.1019373914 0.0224954422
sample108 -0.0877091651 -0.0128834509
sample109 0.0864824298 -0.0900944476
sample110 -0.1223115560 -0.0096086164
sample111 0.0257354587 -0.0936171344
sample112 -0.0765286590 0.0270348186
sample113 0.0258803174 0.0377496069
sample114 0.0021138966 -0.0882015380
sample115 0.0303460099 -0.0723588582
sample116 0.0780508341 -0.0685068205
sample117 0.0536898024 -0.0911910681
sample118 0.0666651123 -0.0236231624
sample119 0.1021871648 -0.2324938075
sample120 0.0750216548 0.0243378501
sample121 -0.0756936430 0.0942950977
sample122 -0.0259628164 0.0731985913
sample123 -0.1037846225 -0.0369196857
sample124 0.0611207868 0.0421722194
sample125 -0.0738472714 0.0066950010
sample126 0.0972916495 0.0762641153
sample127 0.0824697667 -0.0096637432
sample128 -0.1249407727 0.0929311563
sample129 -0.0734067441 -0.0434361969
sample130 -0.0003501970 -0.0309852751
sample131 0.0930182837 0.0155937769
sample132 0.0736222767 0.0733028729
sample133 -0.0498397984 -0.0462437828
sample134 0.1644873473 0.0720006409
sample135 -0.0752297157 0.0003818742
sample136 0.0227145856 -0.0495505320
sample137 0.0564717495 -0.0288914688
sample138 0.0255988078 -0.0610858425
sample139 0.0621217821 0.0235808645
sample140 -0.0604152464 -0.0435592095
sample141 0.0246743951 0.0532648339
sample142 -0.0409560399 0.0316278983
sample143 -0.0077355244 -0.0476896530
sample144 0.0173240830 -0.0156778097
sample145 0.0485474384 0.1202769971
sample146 0.0419645719 -0.0811280520
sample147 -0.0977308278 -0.0274838738
sample148 0.0368256129 0.0803979466
sample149 -0.0072865775 -0.1532986601
sample150 0.1020825292 0.0624775549
sample151 0.0305399026 -0.0289279316
sample152 -0.0533594809 -0.0638309595
sample153 -0.0891627891 0.1799575978
sample154 -0.0727557411 -0.0834160303
sample155 -0.0880668509 -0.0220818651
sample156 -0.0276561003 -0.0326624870
sample157 -0.1155032177 0.0183616460
sample158 -0.0281507504 -0.0104938137
sample159 0.0663235670 0.0443836814
sample160 -0.0302643889 0.0404266146
sample161 0.0114715514 -0.0591026565
sample162 -0.1337087204 0.1398135417
sample163 0.1330124403 0.1688781061
sample164 -0.0150336134 0.0028415397
sample165 0.0076520246 -0.0164128736
sample166 0.0367794313 0.0630661140
sample167 0.1111988884 0.0030058024
sample168 -0.0672981630 0.0446279074
sample169 -0.0413004945 0.0224395276
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 -0.0420513763 0.0867863153
sample2 -0.0820829000 -0.0410977846
sample3 0.0155901069 -0.0195182450
sample4 -0.1001337226 -0.0410786472
sample5 -0.0153466241 -0.0253259642
sample6 0.0340324175 -0.0408223201
sample7 0.0722580199 0.0002332070
sample8 -0.0457502252 -0.0370016038
sample9 -0.0086248656 0.0820184896
sample10 -0.0423599237 -0.0083923168
sample11 0.0022549478 0.0787766006
sample12 0.0322105230 0.1479824660
sample13 -0.0293891333 -0.0306748508
sample14 0.0337481686 -0.0367506889
sample15 0.0815539529 0.1275622316
sample16 0.0508449916 0.0540604647
sample17 0.0062595915 0.0041023739
sample18 0.0705639085 -0.0351047805
sample19 -0.0476840615 -0.0509598033
sample20 0.0522964034 0.0715521705
sample21 -0.0119129036 -0.0376092970
sample22 0.0724394336 -0.0095625311
sample23 -0.0992532106 0.0134288995
sample24 -0.1595120895 0.0728662462
sample25 -0.0920692765 -0.0749757106
sample26 -0.0595540612 0.0848966194
sample27 0.0826487374 -0.0086735668
sample28 -0.0384789038 0.0440966991
sample29 0.0777672843 0.1735308316
sample30 0.1229471344 -0.0819005780
sample31 0.0579844580 -0.0238644797
sample32 0.0970392655 -0.0111426475
sample33 0.1017587857 -0.0630442767
sample34 0.0637922495 0.0377941602
sample35 0.0789984814 -0.0229723404
sample36 0.1224939178 -0.1274955206
sample37 0.1798821269 -0.1673427836
sample38 0.0466306153 0.0888160791
sample39 -0.0168687776 0.0421533800
sample40 0.1756392576 -0.1526642750
sample41 0.0042372602 0.0004928719
sample42 -0.0447849267 -0.0651504934
sample43 0.0482308125 -0.0253529356
sample44 -0.1986716703 -0.0545777395
sample45 -0.0741837933 0.0054703498
sample46 0.0478773771 -0.0007072182
sample47 0.0608189035 0.0481622496
sample48 -0.1381488965 0.0578288035
sample49 -0.0530522433 -0.1405532653
sample50 -0.0173797044 0.1602389600
sample51 0.0462559156 0.0303473821
sample52 0.0280063806 0.0280388385
sample53 0.0667619218 0.0237702004
sample54 0.0121833167 -0.0521354333
sample55 0.0182395854 0.0221328399
sample56 -0.0001256507 0.0030907396
sample57 0.0316673689 0.0530190289
sample58 0.0393917684 -0.0297798805
sample59 0.1278290565 -0.0546528199
sample60 0.1486984758 0.1069156271
sample61 0.0793121538 0.0569796406
sample62 0.1172801387 -0.0149198735
sample63 -0.0028728274 0.1300519917
sample64 0.0237363489 0.1073287732
sample65 -0.0126535055 0.0589808475
sample66 -0.0468195476 -0.0771072574
sample67 0.1494264947 -0.0769860627
sample68 0.0977962277 -0.0577351301
sample69 0.0403087355 0.0156042042
sample70 0.0221532434 0.0315440874
sample71 -0.0546432785 -0.0272396408
sample72 0.1107488023 -0.0537319609
sample73 0.0906761179 0.0579966437
sample74 0.0586554678 0.0121421592
sample75 0.0390493610 0.0349282724
sample76 -0.0022960375 -0.1676558818
sample77 -0.0232096340 -0.2067302760
sample78 -0.0929755901 -0.0434939264
sample79 -0.1619494815 -0.0378114003
sample80 0.0680366010 0.1424663348
sample81 -0.0530783072 -0.0358350784
sample82 0.0266822435 -0.0577445185
sample83 0.1517235306 -0.0448554647
sample84 -0.0570966709 -0.0273813146
sample85 0.1086289374 -0.1228119548
sample86 0.0833860472 -0.0442915177
sample87 0.0022018771 -0.0943906887
sample88 -0.0078225803 -0.1140506494
sample89 0.0611056379 -0.0094585237
sample90 0.0022928315 -0.0936254009
sample91 0.0433593639 0.3205982580
sample92 -0.1815333527 -0.0334680020
sample93 0.0267631158 0.0614428963
sample94 0.0181878177 0.0605090364
sample95 -0.0720374681 -0.0013045551
sample96 -0.0559714072 -0.0118791330
sample97 -0.0217411182 0.0195414206
sample98 0.0379177770 0.0588357021
sample99 -0.0792428662 -0.0151273578
sample100 0.0222116150 -0.0023321463
sample101 -0.0387226149 0.1224226202
sample102 -0.2094614375 -0.0516442186
sample103 0.0138482515 0.0301051871
sample104 -0.0807986366 -0.0162718786
sample105 -0.0520493341 -0.1229665069
sample106 -0.0192613797 -0.0185238128
sample107 0.0319017117 0.0405123234
sample108 -0.0140690709 0.0163421400
sample109 -0.1831928873 0.0613007885
sample110 -0.0292790464 -0.0199849030
sample111 -0.1423250711 0.0327340567
sample112 0.0426332521 -0.0029083514
sample113 -0.0771904995 0.0268733828
sample114 -0.0241640192 -0.0184080417
sample115 -0.1959014586 0.0460131040
sample116 -0.1394475351 -0.0530805560
sample117 -0.1672360975 -0.1386536109
sample118 -0.0448344068 -0.0117621854
sample119 -0.0910383066 0.2217433437
sample120 -0.0331392436 -0.0057274412
sample121 0.0307573903 0.1392506535
sample122 -0.0839782134 -0.0291994206
sample123 0.0239650828 -0.0642163799
sample124 -0.0909151067 0.0130419714
sample125 -0.0065351067 -0.1092631798
sample126 0.0935311198 0.1368283901
sample127 0.0035388201 0.0292755625
sample128 -0.0660296408 0.1018566512
sample129 0.0693639140 -0.0695421910
sample130 0.0008493836 -0.0669704348
sample131 0.0431023808 0.0174064790
sample132 -0.0637040961 0.0029374899
sample133 -0.0289494263 -0.0390818799
sample134 0.0446201979 0.0456334460
sample135 0.0712337097 0.0521634806
sample136 0.0596271810 0.0197299171
sample137 0.0793152366 -0.0380628486
sample138 -0.0973547614 -0.0454218095
sample139 0.0539904047 -0.1534327460
sample140 0.0850827789 0.0955814329
sample141 -0.0192682548 -0.0554449995
sample142 -0.0672262572 -0.0461320756
sample143 -0.0303730038 -0.0519260203
sample144 -0.0089364344 0.0145814925
sample145 -0.0638771776 0.0122258616
sample146 0.0585857447 0.0063083180
sample147 0.0894133557 -0.1124615907
sample148 -0.0216368206 -0.0615967031
sample149 -0.0515418932 -0.0839903476
sample150 0.0568282407 -0.0124469001
sample151 -0.0789532080 -0.0261831036
sample152 -0.0330752441 0.1306443615
sample153 -0.1751933550 0.1497732573
sample154 0.0421425453 -0.0037010334
sample155 0.0680177864 0.0095711074
sample156 0.0388911823 0.1057562860
sample157 0.0314769341 0.0561367366
sample158 0.0329620711 0.0353947248
sample159 -0.0398417352 -0.1007373662
sample160 0.0424938181 0.0108496113
sample161 -0.0888370797 -0.0679700020
sample162 -0.0027477533 0.1237843947
sample163 -0.0126107379 0.0725434474
sample164 -0.0566779801 -0.0458324060
sample165 -0.0315336317 -0.0236362291
sample166 -0.0612059019 -0.0425232871
sample167 0.0142729866 0.0179308250
sample168 -0.0169504178 -0.0769617838
sample169 0.0675080060 0.0131505202
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 -0.0012329716 1.635717e-01
sample2 -0.0724350162 6.021285e-03
sample3 -0.0188460429 1.080036e-01
sample4 0.0390145216 -3.113912e-04
sample5 0.1774811606 2.996386e-02
sample6 -0.0451444484 3.455859e-02
sample7 -0.0226466166 7.020133e-03
sample8 -0.1033680367 9.856817e-03
sample9 0.1350011812 -8.979099e-02
sample10 0.1259887164 5.097855e-02
sample11 0.0979788444 -7.086535e-02
sample12 -0.0863019160 8.620317e-02
sample13 -0.1381401134 -1.828007e-01
sample14 -0.0615073887 2.642803e-02
sample15 0.0381599001 3.101662e-02
sample16 -0.0048776810 -1.271818e-03
sample17 -0.0788481017 1.547555e-02
sample18 -0.0884188776 3.795486e-02
sample19 0.0703044391 1.084004e-01
sample20 -0.0025585399 -7.975877e-02
sample21 0.0941601512 4.126745e-02
sample22 -0.0550273338 7.806740e-02
sample23 0.0679495230 4.102008e-02
sample24 -0.1310962993 -1.649308e-01
sample25 0.0113585227 4.426864e-02
sample26 -0.1402946006 -2.016540e-02
sample27 0.0261561274 -1.588488e-03
sample28 -0.0724198779 -5.850590e-02
sample29 -0.0330058460 -2.060862e-03
sample30 -0.0228752490 2.015427e-02
sample31 -0.0635068017 6.670335e-02
sample32 0.0685099662 4.955272e-02
sample33 -0.0777765208 1.272078e-01
sample34 0.0157842404 3.024314e-02
sample35 -0.0529632616 -1.500972e-01
sample36 0.0070900977 -2.025308e-01
sample37 -0.0442420332 -1.802089e-01
sample38 -0.0781511214 3.676417e-02
sample39 0.0120331804 3.388842e-02
sample40 -0.0473291824 -1.471562e-01
sample41 0.0228189490 2.673552e-02
sample42 -0.0245360293 7.960867e-02
sample43 0.1036362785 8.229577e-02
sample44 -0.1012228976 -7.049445e-02
sample45 0.0013731884 2.450915e-02
sample46 -0.0558509913 -2.947417e-03
sample47 -0.0380481106 -4.554175e-02
sample48 0.0784342034 -4.888978e-02
sample49 -0.0605164082 1.162359e-02
sample50 0.0530079371 2.737929e-02
sample51 0.1514646498 -5.678343e-02
sample52 0.1860935259 -1.246717e-01
sample53 -0.0064177170 2.700995e-02
sample54 0.0697038321 2.308389e-02
sample55 0.1633577052 -1.366442e-02
sample56 0.1011485070 -4.682203e-02
sample57 0.1730374220 -1.609603e-01
sample58 -0.0071384711 1.666955e-02
sample59 -0.0030461588 -3.005288e-02
sample60 0.0215835346 -2.665878e-01
sample61 0.1510583690 -1.002385e-01
sample62 -0.0925533881 4.845839e-02
sample63 -0.0596311898 4.137025e-02
sample64 -0.0449225842 2.600591e-03
sample65 0.0939383715 4.406910e-02
sample66 0.1063400665 5.709995e-02
sample67 -0.0201589743 -2.361728e-01
sample68 0.0037203356 -2.418394e-02
sample69 -0.0645161229 1.155622e-01
sample70 -0.1013440007 1.351789e-01
sample71 -0.0016467828 2.976840e-02
sample72 0.0328893093 2.835855e-02
sample73 0.0275080056 5.148185e-02
sample74 0.1341719650 7.895281e-02
sample75 0.0951575692 3.943183e-02
sample76 -0.0864721905 -3.034993e-02
sample77 -0.1035749564 2.545353e-02
sample78 -0.1575644243 -4.939591e-02
sample79 0.0189137111 -4.874679e-02
sample80 0.1384140649 -4.266683e-05
sample81 -0.0118846466 6.357931e-02
sample82 -0.1675308125 -3.533913e-02
sample83 -0.0065673340 7.812606e-02
sample84 0.1486891583 3.109058e-02
sample85 -0.0532724300 -7.417887e-02
sample86 -0.1138477265 1.912787e-05
sample87 0.0432864041 -6.080473e-02
sample88 0.0433450394 -1.402490e-01
sample89 0.0331205753 1.395401e-02
sample90 -0.0607412833 8.610413e-02
sample91 -0.0566272388 -1.303748e-01
sample92 -0.0359582459 -1.061604e-01
sample93 -0.0433646365 4.443634e-02
sample94 -0.0477291325 1.059574e-01
sample95 -0.0249595779 3.980525e-02
sample96 0.0035218966 9.293928e-02
sample97 -0.0066048848 1.527231e-01
sample98 0.0020366823 5.579549e-02
sample99 -0.0886616252 3.728230e-02
sample100 -0.1091259148 3.560419e-02
sample101 -0.0739726414 4.317996e-02
sample102 0.0574460996 2.783919e-02
sample103 0.0142731098 -9.705580e-03
sample104 0.0710395219 -4.068350e-02
sample105 0.0980831309 3.452954e-02
sample106 -0.0254259344 -3.628982e-02
sample107 -0.0160653493 9.173395e-02
sample108 -0.0200987675 2.379692e-02
sample109 -0.0389780698 -1.692357e-02
sample110 -0.0326304857 -2.988109e-02
sample111 0.0676937513 6.038214e-02
sample112 0.0167883437 -5.336938e-03
sample113 0.0969216929 2.757606e-02
sample114 -0.0026398344 9.209155e-02
sample115 -0.0308047388 -1.603820e-02
sample116 -0.1240307157 -1.273000e-01
sample117 0.0334729065 -5.392709e-02
sample118 -0.1037152899 -6.252431e-02
sample119 -0.1064176494 -1.196203e-01
sample120 -0.0771355167 1.004933e-01
sample121 -0.0129350795 -3.181974e-02
sample122 0.0847492157 5.568330e-02
sample123 -0.0041336741 -7.693190e-03
sample124 -0.0583458121 8.396392e-02
sample125 0.0634844564 5.232541e-02
sample126 -0.0662580981 1.091732e-01
sample127 -0.0865024635 1.094176e-01
sample128 -0.0627817563 1.470967e-02
sample129 -0.0336276396 4.007856e-02
sample130 -0.0293517758 8.046116e-02
sample131 -0.0469197641 2.209740e-03
sample132 -0.0241740819 1.248599e-01
sample133 0.0907303228 -1.466700e-02
sample134 -0.0350842056 -7.539662e-02
sample135 0.0001333449 -9.185391e-03
sample136 -0.0335876025 9.860271e-02
sample137 -0.0640148863 7.554467e-02
sample138 0.0060964819 1.742763e-02
sample139 -0.0592084410 -5.614970e-02
sample140 0.0427986002 1.099549e-02
sample141 0.0618796298 9.301039e-02
sample142 0.0898554414 -3.573415e-02
sample143 0.0817389267 -8.880524e-02
sample144 0.0787754772 3.821392e-02
sample145 0.1085821524 -1.569476e-01
sample146 -0.0589557859 4.373357e-02
sample147 -0.0495330368 -7.277225e-03
sample148 0.1161592729 -9.079058e-03
sample149 -0.0121579348 -7.788376e-02
sample150 -0.0314512523 -3.520213e-02
sample151 0.0575382136 1.945353e-02
sample152 -0.0494542053 -7.025538e-02
sample153 -0.0941332902 -2.153296e-01
sample154 -0.0335931920 -2.078731e-02
sample155 0.0690457690 2.780408e-02
sample156 0.1039901638 6.292524e-02
sample157 -0.0408645784 -8.065516e-03
sample158 0.1018105336 -7.816880e-03
sample159 -0.0281730593 1.207208e-02
sample160 0.1643053007 -2.978097e-03
sample161 0.0374329274 -8.524610e-02
sample162 -0.0804535403 -8.349751e-02
sample163 -0.0743228096 1.406228e-02
sample164 0.1208805964 2.139462e-02
sample165 0.1608115915 -2.025191e-02
sample166 -0.0425944731 2.660717e-02
sample167 -0.0226849483 4.464281e-02
sample168 -0.0180735631 7.466341e-04
sample169 0.0190779041 -2.645403e-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
4.543 0.081 4.625
STATegRa.Rcheck/STATegRa-Ex.timings
| name | user | system | elapsed | |
| STATegRaUsersGuide | 0.000 | 0.001 | 0.000 | |
| STATegRa_data | 0.091 | 0.002 | 0.094 | |
| STATegRa_data_TCGA_BRCA | 0.001 | 0.000 | 0.000 | |
| bioDist | 0.134 | 0.005 | 0.139 | |
| bioDistFeature | 0.110 | 0.004 | 0.114 | |
| bioDistFeaturePlot | 0.125 | 0.004 | 0.129 | |
| bioDistW | 0.123 | 0.004 | 0.128 | |
| bioDistWPlot | 0.121 | 0.004 | 0.126 | |
| bioMap | 0.001 | 0.000 | 0.002 | |
| combiningMappings | 0.004 | 0.000 | 0.004 | |
| createOmicsExpressionSet | 0.052 | 0.000 | 0.053 | |
| getInitialData | 0.254 | 0.004 | 0.259 | |
| getLoadings | 0.254 | 0.014 | 0.267 | |
| getMethodInfo | 0.274 | 0.003 | 0.277 | |
| getPreprocessing | 0.271 | 0.155 | 0.429 | |
| getScores | 0.288 | 0.005 | 0.292 | |
| getVAF | 0.226 | 0.001 | 0.228 | |
| holistOmics | 0.000 | 0.001 | 0.001 | |
| modelSelection | 0.614 | 0.429 | 1.047 | |
| omicsCompAnalysis | 1.879 | 0.003 | 1.882 | |
| omicsNPC | 0.001 | 0.000 | 0.001 | |
| plotRes | 2.216 | 0.005 | 2.222 | |
| plotVAF | 1.906 | 0.008 | 1.915 | |