David Gomez-Cabrero
| Snapshot Date: 2026-04-01 13:40 -0400 (Wed, 01 Apr 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 -0400 (Wed, 29 Oct 2025) |
| Back to Multiple platform build/check report for BioC 3.23: simplified long |
|
This page was generated on 2026-04-02 11:36 -0400 (Thu, 02 Apr 2026).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo1 | Linux (Ubuntu 24.04.3 LTS) | x86_64 | 4.6.0 alpha (2026-03-30 r89742) | 4885 |
| kjohnson3 | macOS 13.7.7 Ventura | arm64 | 4.6.0 alpha (2026-03-28 r89739) | 4619 |
| 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 2149/2374 | 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: 2026-04-01 22:25:40 -0400 (Wed, 01 Apr 2026) |
| EndedAt: 2026-04-01 22:27:04 -0400 (Wed, 01 Apr 2026) |
| EllapsedTime: 83.9 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 version 4.6.0 alpha (2026-03-28 r89739)
* 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: 2026-04-02 02:25:40 UTC
* 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/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 version 4.6.0 alpha (2026-03-28 r89739)
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin23
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> BiocGenerics:::testPackage("STATegRa")
Common components
[1] 2
Distinctive components
[[1]]
[1] 0
[[2]]
[1] 0
Common components
[1] 2
Distinctive components
[[1]]
[1] 1
[[2]]
[1] 1
Common components
[1] 2
Distinctive components
[[1]]
[1] 2
[[2]]
[1] 2
RUNIT TEST PROTOCOL -- Wed Apr 1 22:26:59 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
1.394 0.076 1.479
STATegRa.Rcheck/tests/STATEgRa_Example.omicsCLUST.Rout
R version 4.6.0 alpha (2026-03-28 r89739)
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin23
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> ###########################################
> ########### EXAMPLE OF THE OMICSCLUSTERING
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
>
> #############################################
> ## PART 1: CREATING a bioMap CLASS
> #############################################
> ####### This part creates or reads the map between features.
> ####### In the present example the map is downloaded from a resource.
> ####### then the class is created.
>
> #load("../data/STATegRa_S2.rda")
> data(STATegRa_S2)
>
> MAP.SYMBOL<-bioMap(name = "Symbol-miRNA",
+ metadata = list(type_v1="Gene",type_v2="miRNA",
+ source_database="targetscan.Hs.eg.db",
+ data_extraction="July2014"),
+ map=mapdata)
>
>
> #############################################
> ## PART 2: CREATING a bioDist CLASS
> #############################################
> ##### In the second part given a set of main features and surrogate feautres,
> ##### the profile of the main features is computed through the surrogate features.
>
> # Load Data
> data(STATegRa_S1)
> #load("../data/STATegRa.S1.Rdata")
>
> ## Create ExpressionSets
> # source("../R/STATegRa_omicsPCA_classes_and_methods.R")
> # Block1 - Expression data
> mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname"))
>
> # Create Gene-gene distance computed through miRNA data
> bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1),
+ reference = "Var1",
+ mapping = MAP.SYMBOL,
+ surrogateData = miRNA.ds, ### miRNA data
+ referenceData = mRNA.ds, ### mRNA data
+ maxitems=2,
+ selectionRule="sd",
+ expfac=NULL,
+ aggregation = "sum",
+ distance = "spearman",
+ noMappingDist = 0,
+ filtering = NULL,
+ name = "mRNAbymiRNA")
>
> require(Biobase)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: generics
Attaching package: 'generics'
The following objects are masked from 'package:base':
as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
setequal, union
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
as.data.frame, basename, cbind, colnames, dirname, do.call,
duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
unsplit, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
>
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+ distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+ map.name = "id",
+ map.metadata = list(),
+ params = list())
>
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
>
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
>
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+ bioDistList = bioDistList,
+ weights=weights)
> length(bioDistWList)
[1] 4
>
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
>
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+ listDistW = bioDistWList,
+ method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
cannot compute exact p-value with ties
>
> #############################################
> ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
> #############################################
>
> ## IDH1
>
> IDH1.F<-bioDistFeature(Feature = "IDH1" ,
+ listDistW = bioDistWList,
+ threshold.cor=0.7)
> bioDistFeaturePlot(data=IDH1.F)
>
> ## PDGFRA
>
> #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.7)
> #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
>
> ## EGFR
> #EGFR.F<-bioDistFeature(Feature = "EGFR" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.7)
> #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
>
> ## MGMT
> #MGMT.F<-bioDistFeature(Feature = "MGMT" ,
> # listDistW = bioDistWList,
> # threshold.cor=0.5)
> #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
>
>
>
>
>
> proc.time()
user system elapsed
12.917 0.204 13.258
STATegRa.Rcheck/tests/STATegRa_Example.omicsNPC.Rout
R version 4.6.0 alpha (2026-03-28 r89739)
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
Compute pseudo p-values based on NULL distributions...
NPC p-values calculation...
>
> proc.time()
user system elapsed
20.677 0.254 21.107
STATegRa.Rcheck/tests/STATEgRa_Example.omicsPCA.Rout
R version 4.6.0 alpha (2026-03-28 r89739)
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 -0.0781574314 -0.0431501555
sample2 0.1192218361 0.0294089410
sample3 0.0531412146 -0.0746839885
sample4 -0.0292975189 -0.0005958969
sample5 -0.0202091759 0.0110463900
sample6 -0.1226089073 0.1053466726
sample7 -0.1078928074 -0.0322476534
sample8 -0.1782895365 0.1449364415
sample9 -0.0468698113 -0.0455174290
sample10 0.0036030493 0.0420111502
sample11 0.0035566489 -0.0566292660
sample12 -0.1006128913 0.0641380521
sample13 0.1174408291 0.0907488547
sample14 -0.0981203265 0.0617737769
sample15 -0.0085334272 -0.0087014294
sample16 -0.0783148685 0.1581293936
sample17 0.1483609900 0.0638581923
sample18 0.0963086262 0.0556639482
sample19 0.0217244103 -0.0720085591
sample20 0.0635636443 -0.0779653714
sample21 0.0201840299 0.1566391515
sample22 -0.0218268680 -0.0764105116
sample23 -0.0852042052 -0.0032687741
sample24 0.1287170501 0.1924545142
sample25 0.0430574128 -0.0456565134
sample26 0.1453896792 0.0541512819
sample27 0.0197488878 -0.1185657682
sample28 0.1025336264 0.0650685927
sample29 -0.0706018442 -0.0682988909
sample30 0.1295627582 -0.0066770570
sample31 -0.1147449134 0.1232686032
sample32 0.0374310911 0.0380176993
sample33 -0.0599515952 0.0136866202
sample34 0.0984200834 0.0375320454
sample35 0.0543098403 -0.0378106889
sample36 -0.1403625394 -0.0343757691
sample37 -0.0228941798 -0.0732848412
sample38 0.0222077300 -0.0962595254
sample39 0.0941738481 0.0215199442
sample40 -0.0643801073 -0.0687873062
sample41 0.0327638065 -0.1232188172
sample42 0.0500431832 -0.0292472804
sample43 0.0184498846 0.0233010634
sample44 -0.1487898906 0.1171356312
sample45 0.1050774113 0.1123202401
sample46 0.1151195767 -0.1094029248
sample47 0.0962593757 -0.0288464393
sample48 -0.0004837374 -0.0310276386
sample49 -0.1135207847 0.1213973493
sample50 0.0123553174 -0.1740743429
sample51 -0.0550529884 0.1258886134
sample52 -0.0499121236 0.0728544108
sample53 -0.1119773673 0.1588013114
sample54 0.0360055679 0.0228575481
sample55 -0.0210418980 0.0006731513
sample56 0.0434169214 0.0633126032
sample57 -0.0197824637 0.1150713079
sample58 -0.0030439884 0.0326097555
sample59 -0.0500253071 0.0129417309
sample60 -0.0184278618 0.0136083344
sample61 -0.0150299399 0.0635024813
sample62 0.0304763953 -0.0201320616
sample63 -0.1102252517 0.1285977038
sample64 -0.1552588110 0.0971168051
sample65 0.0058503051 0.0207115673
sample66 0.0025605324 0.0424320544
sample67 -0.1546634754 -0.0661718578
sample68 -0.0536369202 -0.0923684979
sample69 -0.0640330348 0.0081982695
sample70 -0.0163517707 -0.0663230222
sample71 0.0102537651 -0.1345920682
sample72 0.0654196105 -0.0196120880
sample73 0.1048556179 0.0220937245
sample74 -0.0123799457 0.0586114473
sample75 -0.0392077905 -0.0209755490
sample76 -0.0648953371 -0.0524764497
sample77 -0.1172922134 -0.0201186511
sample78 0.1463068013 0.0708473582
sample79 -0.0265211219 -0.1603305957
sample80 -0.0279737107 -0.0214205940
sample81 -0.0079211488 -0.0738450117
sample82 0.1544236522 -0.0361468267
sample83 0.0494211480 -0.0050050386
sample84 0.0259038466 -0.0346548800
sample85 -0.1116484309 -0.0031499324
sample86 0.1306483083 -0.0377216258
sample87 0.0554778214 -0.0459749049
sample88 0.0301623817 0.0382197471
sample89 0.1016866724 0.0694033061
sample90 -0.0086819862 -0.0201320091
sample91 -0.1578625259 -0.2097828685
sample92 -0.0170936877 -0.1655804643
sample93 0.0979806841 -0.0121512495
sample94 -0.0131484073 -0.0114932190
sample95 -0.0315682633 -0.0758858091
sample96 -0.0024125616 -0.0470134694
sample97 -0.0634545417 0.0270332364
sample98 0.0359374669 -0.0135488861
sample99 0.1009163241 0.1124781215
sample100 -0.0551753129 0.0246489276
sample101 0.0080118936 -0.1627367876
sample102 0.0046444204 0.0095634926
sample103 0.0472523221 -0.0940393477
sample104 -0.0198159506 -0.0591090504
sample105 0.0400237786 -0.0160911250
sample106 0.0923808387 0.0369017982
sample107 0.1019373966 0.0224953873
sample108 0.0877091653 -0.0128833926
sample109 -0.0864824455 -0.0900939093
sample110 0.1223115522 -0.0096085288
sample111 -0.0257354663 -0.0936166971
sample112 0.0765286618 0.0270346963
sample113 -0.0258803291 0.0377498402
sample114 -0.0021138888 -0.0882014422
sample115 -0.0303460286 -0.0723582793
sample116 -0.0780508499 -0.0685064512
sample117 -0.0536898169 -0.0911906086
sample118 -0.0666651180 -0.0236230503
sample119 -0.1021871614 -0.2324935533
sample120 -0.0750216573 0.0243379730
sample121 0.0756936373 0.0942950190
sample122 0.0259628020 0.0731988516
sample123 0.1037846284 -0.0369197567
sample124 -0.0611207985 0.0421725111
sample125 0.0738472722 0.0066950276
sample126 -0.0972916408 0.0762638782
sample127 -0.0824697616 -0.0096637230
sample128 0.1249407570 0.0929313795
sample129 0.0734067579 -0.0434363907
sample130 0.0003502028 -0.0309852605
sample131 -0.0930182797 0.0155936458
sample132 -0.0736222862 0.0733030922
sample133 0.0498397984 -0.0462437047
sample134 -0.1644873507 0.0720004797
sample135 0.0752297240 0.0003816724
sample136 -0.0227145707 -0.0495506789
sample137 -0.0564717346 -0.0288916884
sample138 -0.0255988139 -0.0610855564
sample139 -0.0621217790 0.0235806697
sample140 0.0604152606 -0.0435594449
sample141 -0.0246743985 0.0532649071
sample142 0.0409560278 0.0316280823
sample143 0.0077355217 -0.0476895963
sample144 -0.0173240814 -0.0156777753
sample145 -0.0485474620 0.1202771345
sample146 -0.0419645564 -0.0811282136
sample147 0.0977308412 -0.0274841423
sample148 -0.0368256224 0.0803979940
sample149 0.0072865814 -0.1532985374
sample150 -0.1020825287 0.0624773679
sample151 -0.0305399091 -0.0289276998
sample152 0.0533594793 -0.0638308616
sample153 0.0891627444 0.1799580819
sample154 0.0727557526 -0.0834161540
sample155 0.0880668621 -0.0220820529
sample156 0.0276561098 -0.0326625749
sample157 0.1155032196 0.0183615676
sample158 0.0281507549 -0.0104939114
sample159 -0.0663235749 0.0443837878
sample160 0.0302643907 0.0404264852
sample161 -0.0114715602 -0.0591024273
sample162 0.1337087050 0.1398135533
sample163 -0.1330124557 0.1688781467
sample164 0.0150336071 0.0028417054
sample165 -0.0076520278 -0.0164127954
sample166 -0.0367794430 0.0630662969
sample167 -0.1111988854 0.0030057663
sample168 0.0672981574 0.0446279561
sample169 0.0413005000 0.0224393242
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 0.0420515717 0.0867863042
sample2 0.0820828184 -0.0410978180
sample3 -0.0155898560 -0.0195182309
sample4 0.1001337027 -0.0410786851
sample5 0.0153465762 -0.0253259717
sample6 -0.0340327476 -0.0408223184
sample7 -0.0722579342 0.0002332374
sample8 0.0457497695 -0.0370016363
sample9 0.0086249925 0.0820184903
sample10 0.0423598024 -0.0083923375
sample11 -0.0022547755 0.0787766068
sample12 -0.0322106406 0.1479824717
sample13 0.0293888266 -0.0306748709
sample14 -0.0337483663 -0.0367506826
sample15 -0.0815538741 0.1275622632
sample16 -0.0508454491 0.0540604672
sample17 -0.0062597515 0.0041023703
sample18 -0.0705640587 -0.0351047590
sample19 0.0476842831 -0.0509598136
sample20 -0.0522961593 0.0715521984
sample21 0.0119124333 -0.0376093180
sample22 -0.0724391889 -0.0095624955
sample23 0.0992532217 0.0134288621
sample24 0.1595115265 0.0728661664
sample25 0.0920694000 -0.0749757403
sample26 0.0595539542 0.0848965921
sample27 -0.0826483893 -0.0086735233
sample28 0.0384787291 0.0440966782
sample29 -0.0777670238 0.1735308680
sample30 -0.1229471226 -0.0819005304
sample31 -0.0579848239 -0.0238644706
sample32 -0.0970393686 -0.0111426150
sample33 -0.1017588179 -0.0630442394
sample34 -0.0637923270 0.0377941806
sample35 -0.0789984088 -0.0229723067
sample36 -0.1224939368 -0.1274954717
sample37 -0.1798820220 -0.1673427085
sample38 -0.0466302791 0.0888161072
sample39 0.0168687498 0.0421533717
sample40 -0.1756391563 -0.1526642020
sample41 -0.0042368853 0.0004928865
sample42 0.0447850181 -0.0651505068
sample43 -0.0482308730 -0.0253529200
sample44 0.1986712719 -0.0545778268
sample45 0.0741834823 0.0054703104
sample46 -0.0478770365 -0.0007071883
sample47 -0.0608187981 0.0481622758
sample48 0.1381489918 0.0578287543
sample49 0.0530518239 -0.1405532980
sample50 0.0173802857 0.1602389716
sample51 -0.0462563087 0.0303473856
sample52 -0.0280066377 0.0280388403
sample53 -0.0667623891 0.0237702087
sample54 -0.0121833978 -0.0521354312
sample55 -0.0182395958 0.0221328460
sample56 0.0001254506 0.0030907326
sample57 -0.0316677468 0.0530190278
sample58 -0.0393918716 -0.0297798690
sample59 -0.1278291296 -0.0546527733
sample60 -0.1486985518 0.1069156810
sample61 -0.0793123591 0.0569796630
sample62 -0.1172800619 -0.0149198267
sample63 0.0028724971 0.1300519772
sample64 -0.0237366115 0.1073287717
sample65 0.0126534730 0.0589808404
sample66 0.0468194019 -0.0771072797
sample67 -0.1494264096 -0.0769860003
sample68 -0.0977959894 -0.0577350838
sample69 -0.0403087262 0.0156042189
sample70 -0.0221529939 0.0315441033
sample71 0.0546436762 -0.0272396472
sample72 -0.1107487508 -0.0537319170
sample73 -0.0906761364 0.0579966758
sample74 -0.0586556254 0.0121421749
sample75 -0.0390492859 0.0349282890
sample76 0.0022961190 -0.1676558772
sample77 0.0232096148 -0.2067302826
sample78 0.0929753786 -0.0434939680
sample79 0.1619499243 -0.0378114448
sample80 -0.0680364961 0.1424663621
sample81 0.0530785294 -0.0358350905
sample82 -0.0266821382 -0.0577445037
sample83 -0.1517235049 -0.0448554067
sample84 0.0570967653 -0.0273813328
sample85 -0.1086290048 -0.1228119139
sample86 -0.0833859289 -0.0442914816
sample87 -0.0022017891 -0.0943906832
sample88 0.0078223869 -0.1140506568
sample89 -0.0611058327 -0.0094585077
sample90 -0.0022927809 -0.0936253976
sample91 -0.0433586732 0.3205982957
sample92 0.1815338017 -0.0334680533
sample93 -0.0267630297 0.0614429082
sample94 -0.0181877306 0.0605090449
sample95 0.0720377008 -0.0013045743
sample96 0.0559715667 -0.0118791490
sample97 0.0217410796 0.0195414098
sample98 -0.0379176953 0.0588357180
sample99 0.0792425556 -0.0151273987
sample100 -0.0222116790 -0.0023321402
sample101 0.0387231604 0.1224226230
sample102 0.2094613942 -0.0516442987
sample103 -0.0138479595 0.0301052022
sample104 0.0807987874 -0.0162719033
sample105 0.0520493434 -0.1229665250
sample106 0.0192612666 -0.0185238236
sample107 -0.0319017245 0.0405123337
sample108 0.0140691337 0.0163421365
sample109 0.1831931645 0.0613007287
sample110 0.0292790763 -0.0199849127
sample111 0.1423253695 0.0327340127
sample112 -0.0426333264 -0.0029083380
sample113 0.0771903960 0.0268733494
sample114 0.0241642983 -0.0184080414
sample115 0.1959016840 0.0460130377
sample116 0.1394476816 -0.0530806015
sample117 0.1672362918 -0.1386536648
sample118 0.0448344537 -0.0117621998
sample119 0.0910390403 0.2217433335
sample120 0.0331391924 -0.0057274560
sample121 -0.0307576155 0.1392506554
sample122 0.0839779986 -0.0291994600
sample123 -0.0239649868 -0.0642163667
sample124 0.0909150058 0.0130419330
sample125 0.0065350649 -0.1092631829
sample126 -0.0935312721 0.1368284176
sample127 -0.0035387553 0.0292755652
sample128 0.0660294274 0.1018566172
sample129 -0.0693637895 -0.0695421599
sample130 -0.0008492933 -0.0669704309
sample131 -0.0431024283 0.0174064935
sample132 0.0637039066 0.0029374584
sample133 0.0289495448 -0.0390818862
sample134 -0.0446204324 0.0456334548
sample135 -0.0712336845 0.0521635076
sample136 -0.0596270007 0.0197299450
sample137 -0.0793151477 -0.0380628154
sample138 0.0973549263 -0.0454218399
sample139 -0.0539905514 -0.1534327283
sample140 -0.0850826062 0.0955814695
sample141 0.0192680944 -0.0554450124
sample142 0.0672261357 -0.0461321046
sample143 0.0303730970 -0.0519260272
sample144 0.0089364896 0.0145814905
sample145 0.0638767705 0.0122258240
sample146 -0.0585854916 0.0063083488
sample147 -0.0894133035 -0.1124615536
sample148 0.0216365449 -0.0615967202
sample149 0.0515422959 -0.0839903511
sample150 -0.0568284505 -0.0124468855
sample151 0.0789532816 -0.0261831306
sample152 0.0330754729 0.1306443559
sample153 0.1751928371 0.1497731725
sample154 -0.0421422943 -0.0037010085
sample155 -0.0680177035 0.0095711354
sample156 -0.0388910319 0.1057563039
sample157 -0.0314769527 0.0561367470
sample158 -0.0329620318 0.0353947381
sample159 0.0398415627 -0.1007373859
sample160 -0.0424939413 0.0108496226
sample161 0.0888372021 -0.0679700297
sample162 0.0027473849 0.1237843795
sample163 0.0126102574 0.0725434250
sample164 0.0566779544 -0.0458324280
sample165 0.0315336546 -0.0236362399
sample166 0.0612057046 -0.0425233167
sample167 -0.0142729888 0.0179308300
sample168 0.0169502663 -0.0769617946
sample169 -0.0675080708 0.0131505432
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 -0.0012329657 1.635717e-01
sample2 -0.0724350098 6.021252e-03
sample3 -0.0188460439 1.080036e-01
sample4 0.0390145278 -3.114182e-04
sample5 0.1774811628 2.996385e-02
sample6 -0.0451444457 3.455858e-02
sample7 -0.0226466223 7.020167e-03
sample8 -0.1033680280 9.856773e-03
sample9 0.1350011778 -8.979098e-02
sample10 0.1259887221 5.097852e-02
sample11 0.0979788404 -7.086534e-02
sample12 -0.0863019126 8.620317e-02
sample13 -0.1381401132 -1.828007e-01
sample14 -0.0615073878 2.642803e-02
sample15 0.0381598959 3.101664e-02
sample16 -0.0048776779 -1.271846e-03
sample17 -0.0788480992 1.547553e-02
sample18 -0.0884188790 3.795486e-02
sample19 0.0703044422 1.084004e-01
sample20 -0.0025585483 -7.975874e-02
sample21 0.0941601591 4.126740e-02
sample22 -0.0550273393 7.806744e-02
sample23 0.0679495304 4.102005e-02
sample24 -0.1310962859 -1.649309e-01
sample25 0.0113585278 4.426863e-02
sample26 -0.1402945949 -2.016544e-02
sample27 0.0261561174 -1.588433e-03
sample28 -0.0724198744 -5.850593e-02
sample29 -0.0330058531 -2.060818e-03
sample30 -0.0228752569 2.015431e-02
sample31 -0.0635067988 6.670333e-02
sample32 0.0685099628 4.955273e-02
sample33 -0.0777765234 1.272078e-01
sample34 0.0157842388 3.024314e-02
sample35 -0.0529632724 -1.500972e-01
sample36 0.0070900824 -2.025307e-01
sample37 -0.0442420532 -1.802089e-01
sample38 -0.0781511268 3.676421e-02
sample39 0.0120331835 3.388841e-02
sample40 -0.0473292009 -1.471561e-01
sample41 0.0228189447 2.673555e-02
sample42 -0.0245360256 7.960866e-02
sample43 0.1036362785 8.229577e-02
sample44 -0.1012228825 -7.049453e-02
sample45 0.0013731982 2.450910e-02
sample46 -0.0558509987 -2.947375e-03
sample47 -0.0380481167 -4.554173e-02
sample48 0.0784342098 -4.888981e-02
sample49 -0.0605164002 1.162354e-02
sample50 0.0530079328 2.737933e-02
sample51 0.1514646502 -5.678345e-02
sample52 0.1860935236 -1.246717e-01
sample53 -0.0064177143 2.700993e-02
sample54 0.0697038326 2.308389e-02
sample55 0.1633577038 -1.366441e-02
sample56 0.1011485082 -4.682205e-02
sample57 0.1730374201 -1.609603e-01
sample58 -0.0071384720 1.666955e-02
sample59 -0.0030461675 -3.005285e-02
sample60 0.0215835185 -2.665877e-01
sample61 0.1510583638 -1.002385e-01
sample62 -0.0925533951 4.845842e-02
sample63 -0.0596311830 4.137022e-02
sample64 -0.0449225815 2.600576e-03
sample65 0.0939383746 4.406909e-02
sample66 0.1063400725 5.709992e-02
sample67 -0.0201589933 -2.361727e-01
sample68 0.0037203249 -2.418388e-02
sample69 -0.0645161219 1.155622e-01
sample70 -0.1013440008 1.351789e-01
sample71 -0.0016467838 2.976842e-02
sample72 0.0328893021 2.835858e-02
sample73 0.0275080023 5.148186e-02
sample74 0.1341719658 7.895280e-02
sample75 0.0951575671 3.943184e-02
sample76 -0.0864721938 -3.034991e-02
sample77 -0.1035749557 2.545353e-02
sample78 -0.1575644172 -4.939595e-02
sample79 0.0189137137 -4.874679e-02
sample80 0.1384140602 -4.264545e-05
sample81 -0.0118846445 6.357932e-02
sample82 -0.1675308168 -3.533911e-02
sample83 -0.0065673419 7.812611e-02
sample84 0.1486891614 3.109057e-02
sample85 -0.0532724395 -7.417884e-02
sample86 -0.1138477334 1.915990e-05
sample87 0.0432864001 -6.080472e-02
sample88 0.0433450371 -1.402491e-01
sample89 0.0331205745 1.395401e-02
sample90 -0.0607412821 8.610414e-02
sample91 -0.0566272522 -1.303747e-01
sample92 -0.0359582438 -1.061604e-01
sample93 -0.0433646372 4.443635e-02
sample94 -0.0477291310 1.059574e-01
sample95 -0.0249595751 3.980525e-02
sample96 0.0035219010 9.293928e-02
sample97 -0.0066048780 1.527231e-01
sample98 0.0020366812 5.579550e-02
sample99 -0.0886616148 3.728224e-02
sample100 -0.1091259143 3.560420e-02
sample101 -0.0739726436 4.317999e-02
sample102 0.0574461140 2.783913e-02
sample103 0.0142731051 -9.705551e-03
sample104 0.0710395236 -4.068351e-02
sample105 0.0980831341 3.452952e-02
sample106 -0.0254259329 -3.628984e-02
sample107 -0.0160653477 9.173394e-02
sample108 -0.0200987664 2.379692e-02
sample109 -0.0389780618 -1.692359e-02
sample110 -0.0326304851 -2.988110e-02
sample111 0.0676937585 6.038212e-02
sample112 0.0167883419 -5.336937e-03
sample113 0.0969217002 2.757603e-02
sample114 -0.0026398338 9.209157e-02
sample115 -0.0308047294 -1.603823e-02
sample116 -0.1240307132 -1.273000e-01
sample117 0.0334729117 -5.392711e-02
sample118 -0.1037152897 -6.252431e-02
sample119 -0.1064176552 -1.196202e-01
sample120 -0.0771355108 1.004933e-01
sample121 -0.0129350782 -3.181976e-02
sample122 0.0847492254 5.568325e-02
sample123 -0.0041336775 -7.693177e-03
sample124 -0.0583458023 8.396388e-02
sample125 0.0634844582 5.232540e-02
sample126 -0.0662580975 1.091733e-01
sample127 -0.0865024610 1.094176e-01
sample128 -0.0627817477 1.470962e-02
sample129 -0.0336276448 4.007859e-02
sample130 -0.0293517750 8.046117e-02
sample131 -0.0469197661 2.209752e-03
sample132 -0.0241740715 1.248598e-01
sample133 0.0907303223 -1.466700e-02
sample134 -0.0350842076 -7.539662e-02
sample135 0.0001333403 -9.185374e-03
sample136 -0.0335876054 9.860274e-02
sample137 -0.0640148905 7.554471e-02
sample138 0.0060964860 1.742762e-02
sample139 -0.0592084457 -5.614969e-02
sample140 0.0427985937 1.099552e-02
sample141 0.0618796355 9.301037e-02
sample142 0.0898554458 -3.573418e-02
sample143 0.0817389241 -8.880524e-02
sample144 0.0787754783 3.821392e-02
sample145 0.1085821568 -1.569476e-01
sample146 -0.0589557916 4.373361e-02
sample147 -0.0495330442 -7.277195e-03
sample148 0.1161592769 -9.079088e-03
sample149 -0.0121579400 -7.788373e-02
sample150 -0.0314512545 -3.520212e-02
sample151 0.0575382180 1.945352e-02
sample152 -0.0494542072 -7.025537e-02
sample153 -0.0941332775 -2.153297e-01
sample154 -0.0335931985 -2.078728e-02
sample155 0.0690457646 2.780410e-02
sample156 0.1039901622 6.292525e-02
sample157 -0.0408645797 -8.065515e-03
sample158 0.1018105310 -7.816872e-03
sample159 -0.0281730550 1.207206e-02
sample160 0.1643052995 -2.978103e-03
sample161 0.0374329281 -8.524611e-02
sample162 -0.0804535366 -8.349756e-02
sample163 -0.0743228016 1.406224e-02
sample164 0.1208806005 2.139460e-02
sample165 0.1608115923 -2.025192e-02
sample166 -0.0425944661 2.660713e-02
sample167 -0.0226849478 4.464282e-02
sample168 -0.0180735605 7.466150e-04
sample169 0.0190779000 -2.645402e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
[,1] [,2]
sample1 -0.0572060227 -1.729087e-02
sample2 0.0875245208 1.112588e-02
sample3 0.0403482602 -3.168994e-02
sample4 -0.0218345996 4.052760e-06
sample5 -0.0150905011 4.795041e-03
sample6 -0.0924362933 4.511003e-02
sample7 -0.0793066751 -1.243823e-02
sample8 -0.1342997187 6.215220e-02
sample9 -0.0338886944 -1.854401e-02
sample10 0.0020547173 1.749421e-02
sample11 0.0037275602 -2.364116e-02
sample12 -0.0753094533 2.772698e-02
sample13 0.0856160091 3.679963e-02
sample14 -0.0737457307 2.668452e-02
sample15 -0.0062111746 -3.554864e-03
sample16 -0.0602355268 6.675115e-02
sample17 0.1086768843 2.524534e-02
sample18 0.0702999472 2.231671e-02
sample19 0.0173785882 -3.024846e-02
sample20 0.0484173812 -3.310904e-02
sample21 0.0124657042 6.517144e-02
sample22 -0.0140989936 -3.159137e-02
sample23 -0.0627028403 -5.393710e-04
sample24 0.0919972100 7.909297e-02
sample25 0.0326998483 -1.945206e-02
sample26 0.1064741246 2.120849e-02
sample27 0.0166058995 -4.964993e-02
sample28 0.0743504770 2.614211e-02
sample29 -0.0511008491 -2.782647e-02
sample30 0.0962250842 -3.974893e-03
sample31 -0.0869563008 5.250819e-02
sample32 0.0271858919 1.552005e-02
sample33 -0.0448364581 6.243160e-03
sample34 0.0718415218 1.469396e-02
sample35 0.0403086451 -1.632629e-02
sample36 -0.1036402827 -1.304320e-02
sample37 -0.0159385744 -3.036525e-02
sample38 0.0182198369 -4.034805e-02
sample39 0.0690363619 8.058350e-03
sample40 -0.0467312750 -2.810325e-02
sample41 0.0263674438 -5.171216e-02
sample42 0.0374578960 -1.268634e-02
sample43 0.0132336869 9.536642e-03
sample44 -0.1119154428 5.028683e-02
sample45 0.0759639367 4.587903e-02
sample46 0.0871885519 -4.670385e-02
sample47 0.0721490571 -1.288540e-02
sample48 0.0005086144 -1.290565e-02
sample49 -0.0858177028 5.173760e-02
sample50 0.0118992665 -7.276215e-02
sample51 -0.0426446855 5.306205e-02
sample52 -0.0381605826 3.086785e-02
sample53 -0.0855757630 6.730043e-02
sample54 0.0261723092 9.184260e-03
sample55 -0.0156418304 4.682404e-04
sample56 0.0307831193 2.597550e-02
sample57 -0.0157242103 4.829381e-02
sample58 -0.0031174404 1.359898e-02
sample59 -0.0373001859 5.868397e-03
sample60 -0.0142609099 5.831654e-03
sample61 -0.0122255144 2.663579e-02
sample62 0.0228002942 -8.692265e-03
sample63 -0.0833127581 5.473229e-02
sample64 -0.1166548159 4.196500e-02
sample65 0.0038808902 8.568590e-03
sample66 0.0011561811 1.766612e-02
sample67 -0.1129311062 -2.608702e-02
sample68 -0.0382526429 -3.804045e-02
sample69 -0.0476502440 4.003241e-03
sample70 -0.0110329882 -2.752719e-02
sample71 0.0096850282 -5.627056e-02
sample72 0.0487124704 -8.800131e-03
sample73 0.0773058132 8.239864e-03
sample74 -0.0102488176 2.454957e-02
sample75 -0.0286613976 -8.387293e-03
sample76 -0.0472655595 -2.129315e-02
sample77 -0.0865043074 -7.296820e-03
sample78 0.1070293698 2.818346e-02
sample79 -0.0165060681 -6.659721e-02
sample80 -0.0206765949 -8.712112e-03
sample81 -0.0050943615 -3.079175e-02
sample82 0.1153622361 -1.647054e-02
sample83 0.0367979217 -2.538114e-03
sample84 0.0199463070 -1.468961e-02
sample85 -0.0827122185 -2.709824e-04
sample86 0.0969487314 -1.699897e-02
sample87 0.0421957457 -1.965953e-02
sample88 0.0215934743 1.566050e-02
sample89 0.0751559502 2.811652e-02
sample90 -0.0057328000 -8.283795e-03
sample91 -0.1134005268 -8.603522e-02
sample92 -0.0101689918 -6.894992e-02
sample93 0.0725967502 -6.003176e-03
sample94 -0.0096878852 -4.693081e-03
sample95 -0.0223502239 -3.139636e-02
sample96 -0.0013232863 -1.963604e-02
sample97 -0.0476541710 1.183660e-02
sample98 0.0269546160 -5.978398e-03
sample99 0.0728179461 4.597884e-02
sample100 -0.0413398038 1.079347e-02
sample101 0.0087536994 -6.796076e-02
sample102 0.0032509529 3.932612e-03
sample103 0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105 0.0294940465 -7.140722e-03
sample106 0.0686472054 1.462895e-02
sample107 0.0748635927 8.401339e-03
sample108 0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110 0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112 0.0570870472 1.066018e-02
sample113 -0.0200110554 1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586 1.089843e-02
sample121 0.0542443861 3.861344e-02
sample122 0.0178575357 3.027138e-02
sample123 0.0775020581 -1.636852e-02
sample124 -0.0460701050 1.814758e-02
sample125 0.0543846585 2.075898e-03
sample126 -0.0729417144 3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128 0.0908136899 3.758801e-02
sample129 0.0552445878 -1.879062e-02
sample130 0.0007128089 -1.294308e-02
sample131 -0.0693311345 7.357082e-03
sample132 -0.0556565156 3.126995e-02
sample133 0.0375870104 -1.977240e-02
sample134 -0.1229130924 3.159495e-02
sample135 0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201 1.040683e-02
sample140 0.0452288969 -1.876279e-02
sample141 -0.0189142561 2.247042e-02
sample142 0.0297545566 1.280524e-02
sample143 0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491 5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147 0.0726083535 -1.239968e-02
sample148 -0.0284795794 3.389732e-02
sample149 0.0082261455 -6.399305e-02
sample150 -0.0765013197 2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152 0.0403422737 -2.714879e-02
sample153 0.0629117719 7.425085e-02
sample154 0.0551622927 -3.548984e-02
sample155 0.0654439133 -1.005306e-02
sample156 0.0209310714 -1.390213e-02
sample157 0.0851522597 6.577150e-03
sample158 0.0208354599 -4.663078e-03
sample159 -0.0498794349 1.913257e-02
sample160 0.0216074437 1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162 0.0963663017 5.705881e-02
sample163 -0.1009542191 7.174224e-02
sample164 0.0109881996 1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357 2.673084e-02
sample167 -0.0825048036 2.278863e-03
sample168 0.0486147429 1.793843e-02
sample169 0.0302506727 8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
[,1] [,2]
sample1 -0.0621842115 -1.364509e-02
sample2 0.0944623785 9.720892e-03
sample3 0.0406196267 -2.236338e-02
sample4 -0.0229316496 -3.932487e-04
sample5 -0.0157330047 3.231033e-03
sample6 -0.0945794025 3.120720e-02
sample7 -0.0854427118 -1.052880e-02
sample8 -0.1376625920 4.286608e-02
sample9 -0.0377115311 -1.415134e-02
sample10 0.0035244506 1.280825e-02
sample11 0.0016639987 -1.717895e-02
sample12 -0.0781403168 1.884368e-02
sample13 0.0938400516 2.838858e-02
sample14 -0.0759839772 1.810989e-02
sample15 -0.0068340837 -2.705361e-03
sample16 -0.0590150849 4.757848e-02
sample17 0.1178805097 2.040526e-02
sample18 0.0767858320 1.756604e-02
sample19 0.0157112113 -2.172867e-02
sample20 0.0485318300 -2.327033e-02
sample21 0.0185928176 4.777095e-02
sample22 -0.0191358702 -2.329775e-02
sample23 -0.0672994194 -1.535656e-03
sample24 0.1047476642 5.935707e-02
sample25 0.0329844953 -1.358036e-02
sample26 0.1154952052 1.741529e-02
sample27 0.0133849853 -3.590922e-02
sample28 0.0821554039 2.042376e-02
sample29 -0.0567643690 -2.123848e-02
sample30 0.1016073931 -1.134728e-03
sample31 -0.0880396372 3.670548e-02
sample32 0.0300363338 1.182406e-02
sample33 -0.0467252272 3.739254e-03
sample34 0.0783666394 1.203777e-02
sample35 0.0424227097 -1.118559e-02
sample36 -0.1107646166 -1.143464e-02
sample37 -0.0191667664 -2.246060e-02
sample38 0.0155968095 -2.909621e-02
sample39 0.0746847148 7.148218e-03
sample40 -0.0517028178 -2.137267e-02
sample41 0.0234979494 -3.723018e-02
sample42 0.0388797356 -8.557228e-03
sample43 0.0149555568 7.210002e-03
sample44 -0.1150305613 3.461805e-02
sample45 0.0846146236 3.486020e-02
sample46 0.0884426404 -3.246853e-02
sample47 0.0748644971 -8.083045e-03
sample48 -0.0012033198 -9.403647e-03
sample49 -0.0872662737 3.616245e-02
sample50 0.0066941314 -5.284863e-02
sample51 -0.0411777630 3.791830e-02
sample52 -0.0379355780 2.180834e-02
sample53 -0.0851639886 4.751761e-02
sample54 0.0288006248 7.184424e-03
sample55 -0.0164920835 5.919925e-05
sample56 0.0355115616 1.951043e-02
sample57 -0.0141146068 3.492409e-02
sample58 -0.0015636132 9.862883e-03
sample59 -0.0390656483 3.590929e-03
sample60 -0.0139454780 3.963030e-03
sample61 -0.0106410274 1.919705e-02
sample62 0.0236748439 -5.922677e-03
sample63 -0.0846790877 3.839102e-02
sample64 -0.1202581015 2.846469e-02
sample65 0.0050548584 6.328644e-03
sample66 0.0028013072 1.291807e-02
sample67 -0.1231623009 -2.112565e-02
sample68 -0.0437782161 -2.845072e-02
sample69 -0.0501199692 2.053469e-03
sample70 -0.0140278645 -2.027157e-02
sample71 0.0057489505 -4.085977e-02
sample72 0.0511212704 -5.522408e-03
sample73 0.0828141409 7.431582e-03
sample74 -0.0085959456 1.772951e-02
sample75 -0.0312180394 -6.636869e-03
sample76 -0.0519051781 -1.640191e-02
sample77 -0.0925924762 -6.907800e-03
sample78 0.1163971046 2.251122e-02
sample79 -0.0240906926 -4.887766e-02
sample80 -0.0221327065 -6.730703e-03
sample81 -0.0072114968 -2.254399e-02
sample82 0.1204416674 -9.907422e-03
sample83 0.0386739485 -1.171663e-03
sample84 0.0195988488 -1.033806e-02
sample85 -0.0877680171 -1.725057e-03
sample86 0.1023541048 -1.062501e-02
sample87 0.0425213089 -1.356865e-02
sample88 0.0244788514 1.180820e-02
sample89 0.0804276691 2.188588e-02
sample90 -0.0074639871 -6.140721e-03
sample91 -0.1278832404 -6.485140e-02
sample92 -0.0162199697 -5.048358e-02
sample93 0.0769344893 -3.045135e-03
sample94 -0.0104345587 -3.593172e-03
sample95 -0.0260058453 -2.330475e-02
sample96 -0.0025018700 -1.433516e-02
sample97 -0.0492358305 7.774183e-03
sample98 0.0279220220 -3.862141e-03
sample99 0.0813921923 3.487339e-02
sample100 -0.0428797405 7.112807e-03
sample101 0.0032855240 -4.940743e-02
sample102 0.0038439317 2.938008e-03
sample103 0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105 0.0314853405 -4.656633e-03
sample106 0.0726456731 1.192390e-02
sample107 0.0807342975 7.508627e-03
sample108 0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110 0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112 0.0599954082 8.820317e-03
sample113 -0.0195006577 1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131 6.937326e-03
sample121 0.0613899126 2.915307e-02
sample122 0.0218424338 2.241775e-02
sample123 0.0809008460 -1.051759e-02
sample124 -0.0472109313 1.239887e-02
sample125 0.0583180947 2.521167e-03
sample126 -0.0753941872 2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128 0.1000212216 2.908091e-02
sample129 0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877 4.091672e-03
sample132 -0.0566219024 2.179861e-02
sample133 0.0384172955 -1.372840e-02
sample134 -0.1280862736 2.077912e-02
sample135 0.0592633273 6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861 6.729304e-03
sample140 0.0468926306 -1.285498e-02
sample141 -0.0186248693 1.605439e-02
sample142 0.0328031246 9.887746e-03
sample143 0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310 3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147 0.0765025670 -7.714769e-03
sample148 -0.0276016641 2.420589e-02
sample149 0.0027545308 -4.653007e-02
sample150 -0.0792296010 1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152 0.0409796547 -1.907063e-02
sample153 0.0734301757 5.528780e-02
sample154 0.0557740684 -2.487723e-02
sample155 0.0689436560 -6.127635e-03
sample156 0.0212272938 -9.747423e-03
sample157 0.0911931194 6.355708e-03
sample158 0.0220840645 -3.016357e-03
sample159 -0.0513244242 1.304175e-02
sample160 0.0246213576 1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162 0.1078802043 4.337260e-02
sample163 -0.1017965082 5.047171e-02
sample164 0.0119430799 9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180 1.899222e-02
sample167 -0.0872832229 1.516582e-04
sample168 0.0540714512 1.397701e-02
sample169 0.0328432652 7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
[,1] [,2]
sample1 0.0133684846 2.195848e-02
sample2 0.0254157197 -1.058416e-02
sample3 -0.0049551479 -4.840017e-03
sample4 0.0310390570 -1.063929e-02
sample5 0.0046941318 -6.488426e-03
sample6 -0.0107406753 -1.026702e-02
sample7 -0.0225157631 2.624712e-04
sample8 0.0141320952 -9.505821e-03
sample9 0.0029681280 2.078210e-02
sample10 0.0131729174 -2.275042e-03
sample11 -0.0004164298 1.994019e-02
sample12 -0.0095211620 3.759883e-02
sample13 0.0091018604 -7.953956e-03
sample14 -0.0106557524 -9.181659e-03
sample15 -0.0249924121 3.262724e-02
sample16 -0.0156216400 1.375700e-02
sample17 -0.0019382446 1.073994e-03
sample18 -0.0221072481 -8.703592e-03
sample19 0.0146917619 -1.311712e-02
sample20 -0.0160353760 1.826290e-02
sample21 0.0035947899 -9.616341e-03
sample22 -0.0225060762 -2.532589e-03
sample23 0.0310000683 3.033060e-03
sample24 0.0499544372 1.809450e-02
sample25 0.0284442301 -1.932558e-02
sample26 0.0188220043 2.146985e-02
sample27 -0.0257763219 -1.999228e-03
sample28 0.0120888648 1.125834e-02
sample29 -0.0236482520 4.426726e-02
sample30 -0.0385486305 -2.055935e-02
sample31 -0.0181539336 -5.877838e-03
sample32 -0.0302630460 -2.607192e-03
sample33 -0.0319565715 -1.562628e-02
sample34 -0.0197970124 9.906813e-03
sample35 -0.0247412713 -5.434440e-03
sample36 -0.0386259060 -3.190394e-02
sample37 -0.0566199273 -4.192574e-02
sample38 -0.0142060273 2.259644e-02
sample39 0.0053589035 1.076485e-02
sample40 -0.0552546493 -3.819896e-02
sample41 -0.0013089975 9.278818e-05
sample42 0.0137252142 -1.664652e-02
sample43 -0.0151259626 -6.290953e-03
sample44 0.0617391754 -1.442883e-02
sample45 0.0231410886 1.163143e-03
sample46 -0.0148898209 -1.384176e-04
sample47 -0.0187252536 1.221690e-02
sample48 0.0432839432 1.416671e-02
sample49 0.0160818605 -3.588745e-02
sample50 0.0059333545 4.067003e-02
sample51 -0.0142914866 7.776270e-03
sample52 -0.0086339952 7.208917e-03
sample53 -0.0207386980 6.272432e-03
sample54 -0.0039856719 -1.316934e-02
sample55 -0.0056217017 5.692315e-03
sample56 0.0000123292 8.978290e-04
sample57 -0.0095805555 1.324253e-02
sample58 -0.0124160295 -7.326376e-03
sample59 -0.0400195442 -1.349736e-02
sample60 -0.0460063358 2.770091e-02
sample61 -0.0245266456 1.470710e-02
sample62 -0.0366022783 -3.437352e-03
sample63 0.0013742171 3.288796e-02
sample64 -0.0070599859 2.739588e-02
sample65 0.0041201911 1.498268e-02
sample66 0.0143173351 -1.968812e-02
sample67 -0.0467477531 -1.929938e-02
sample68 -0.0306751978 -1.436184e-02
sample69 -0.0125317217 4.130407e-03
sample70 -0.0068071487 8.080857e-03
sample71 0.0169170264 -7.027348e-03
sample72 -0.0346909749 -1.333770e-02
sample73 -0.0280506153 1.493843e-02
sample74 -0.0182611498 3.294697e-03
sample75 -0.0120563964 8.974612e-03
sample76 0.0001437236 -4.253184e-02
sample77 0.0065330299 -5.252886e-02
sample78 0.0288278141 -1.127782e-02
sample79 0.0503961481 -1.023318e-02
sample80 -0.0207693429 3.648391e-02
sample81 0.0163562768 -9.074596e-03
sample82 -0.0084317129 -1.478976e-02
sample83 -0.0474097918 -1.103126e-02
sample84 0.0177181395 -7.191197e-03
sample85 -0.0342718548 -3.082360e-02
sample86 -0.0261671791 -1.089491e-02
sample87 -0.0009486358 -2.411514e-02
sample88 0.0020528931 -2.894615e-02
sample89 -0.0189361111 -2.638639e-03
sample90 -0.0009863658 -2.390075e-02
sample91 -0.0124352695 8.153234e-02
sample92 0.0564264106 -8.909537e-03
sample93 -0.0081461774 1.570851e-02
sample94 -0.0054896581 1.547251e-02
sample95 0.0224073150 -4.374348e-04
sample96 0.0173528924 -3.050441e-03
sample97 0.0067948115 5.008237e-03
sample98 -0.0116030825 1.498764e-02
sample99 0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101 0.0124923691 3.091503e-02
sample102 0.0650835386 -1.367400e-02
sample103 -0.0042741828 7.855985e-03
sample104 0.0250591040 -4.171938e-03
sample105 0.0157516368 -3.121990e-02
sample106 0.0060593853 -5.101693e-03
sample107 -0.0098329626 1.044506e-02
sample108 0.0044269853 4.142036e-03
sample109 0.0572473486 1.517542e-02
sample110 0.0090474827 -5.119868e-03
sample111 0.0444263015 7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113 0.0241047399 6.706740e-03
sample114 0.0074558775 -4.728652e-03
sample115 0.0611851433 1.117210e-02
sample116 0.0432646951 -1.380556e-02
sample117 0.0516750066 -3.575617e-02
sample118 0.0139942100 -3.279138e-03
sample119 0.0291722987 5.587946e-02
sample120 0.0103515853 -1.690016e-03
sample121 -0.0091396331 3.552116e-02
sample122 0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124 0.0283466326 3.127845e-03
sample125 0.0016472378 -2.770692e-02
sample126 -0.0286529417 3.489336e-02
sample127 -0.0010224500 7.483214e-03
sample128 0.0209049296 2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968 4.637390e-03
sample132 0.0198526786 5.723983e-04
sample133 0.0088812957 -9.988115e-03
sample134 -0.0137484514 1.172591e-02
sample135 -0.0220314568 1.347465e-02
sample136 -0.0185173353 5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138 0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762 2.456863e-02
sample141 0.0058369763 -1.420854e-02
sample142 0.0207886071 -1.188764e-02
sample143 0.0092832598 -1.324238e-02
sample144 0.0028442140 3.627979e-03
sample145 0.0199749569 2.862202e-03
sample146 -0.0182236697 1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148 0.0065435868 -1.572917e-02
sample149 0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151 0.0245166984 -6.888241e-03
sample152 0.0107259913 3.314630e-02
sample153 0.0550963965 3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574 2.642246e-03
sample156 -0.0117803505 2.698265e-02
sample157 -0.0096167165 1.433840e-02
sample158 -0.0101754772 9.137620e-03
sample159 0.0120662931 -2.565236e-02
sample160 -0.0132238202 2.916023e-03
sample161 0.0274491966 -1.748284e-02
sample162 0.0012482909 3.152261e-02
sample163 0.0042031315 1.830701e-02
sample164 0.0174896157 -1.175915e-02
sample165 0.0097517662 -6.119019e-03
sample166 0.0190134679 -1.121582e-02
sample167 -0.0044140836 4.665585e-03
sample168 0.0049689168 -1.941822e-02
sample169 -0.0209802098 3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
[,1] [,2]
sample1 -0.0515543627 -0.0305856787
sample2 -0.0144993256 0.0236342950
sample3 -0.0371833108 -0.0140263348
sample4 0.0068945388 -0.0132539692
sample5 0.0215035333 -0.0663338101
sample6 -0.0187055152 0.0088773016
sample7 -0.0061521552 0.0064029054
sample8 -0.0210874459 0.0334652901
sample9 0.0516865043 -0.0291142799
sample10 0.0059440366 -0.0527217447
sample11 0.0393010793 -0.0200624712
sample12 -0.0420837100 0.0131331362
sample13 0.0333252565 0.0818552509
sample14 -0.0190062644 0.0160202175
sample15 -0.0030968049 -0.0189230681
sample16 -0.0004452158 0.0018880102
sample17 -0.0185848615 0.0240170131
sample18 -0.0273093598 0.0230213640
sample19 -0.0217761111 -0.0445894441
sample20 0.0245820821 0.0159812738
sample21 0.0034527644 -0.0400016054
sample22 -0.0340789054 0.0039289109
sample23 -0.0010344929 -0.0310161212
sample24 0.0289468503 0.0760962436
sample25 -0.0119098496 -0.0122798760
sample26 -0.0181001057 0.0517892852
sample27 0.0050465417 -0.0086515844
sample28 0.0057491502 0.0358830107
sample29 -0.0051104246 0.0116605117
sample30 -0.0103085904 0.0039678538
sample31 -0.0319929858 0.0090606113
sample32 -0.0036232521 -0.0328202010
sample33 -0.0534742153 0.0024751837
sample34 -0.0067495749 -0.0111000311
sample35 0.0378745721 0.0465929296
sample36 0.0647886800 0.0359987924
sample37 0.0488441236 0.0492906912
sample38 -0.0251514062 0.0197110110
sample39 -0.0085428066 -0.0105117852
sample40 0.0379324087 0.0440810741
sample41 -0.0044199152 -0.0128820644
sample42 -0.0292553573 -0.0067045265
sample43 -0.0077829155 -0.0510178219
sample44 0.0045122248 0.0479660309
sample45 -0.0074444298 -0.0051116726
sample46 -0.0088025512 0.0196186661
sample47 0.0076696301 0.0215947965
sample48 0.0290108585 -0.0175568376
sample49 -0.0141754858 0.0184717099
sample50 0.0006282201 -0.0233054373
sample51 0.0441995177 -0.0410022921
sample52 0.0715329391 -0.0399499475
sample53 -0.0095954087 -0.0029140909
sample54 0.0048933768 -0.0281884386
sample55 0.0327325487 -0.0532290012
sample56 0.0323068984 -0.0256595538
sample57 0.0806603122 -0.0286748097
sample58 -0.0064792049 -0.0006945349
sample59 0.0088958941 0.0067389649
sample60 0.0874124612 0.0431964341
sample61 0.0577604571 -0.0326112099
sample62 -0.0313318464 0.0224391756
sample63 -0.0233625220 0.0125110562
sample64 -0.0086426068 0.0148770341
sample65 0.0025256193 -0.0404466327
sample66 0.0006014071 -0.0471576264
sample67 0.0706087042 0.0516228406
sample68 0.0082301011 0.0033109509
sample69 -0.0475076743 0.0001452708
sample70 -0.0600773716 0.0089986962
sample71 -0.0096321627 -0.0050761187
sample72 -0.0031773546 -0.0166221542
sample73 -0.0113700517 -0.0191726684
sample74 -0.0014179662 -0.0608101325
sample75 0.0041911740 -0.0399981269
sample76 -0.0055326449 0.0353114263
sample77 -0.0260214459 0.0305731380
sample78 -0.0119267436 0.0632236007
sample79 0.0186017239 0.0027402910
sample80 0.0241047889 -0.0472697181
sample81 -0.0220288317 -0.0079577210
sample82 -0.0180751258 0.0639051029
sample83 -0.0256671713 -0.0125898269
sample84 0.0161392598 -0.0567222449
sample85 0.0139988188 0.0322763454
sample86 -0.0198382995 0.0389225776
sample87 0.0266270281 -0.0032979996
sample88 0.0515677078 0.0117902495
sample89 0.0014022125 -0.0140510488
sample90 -0.0375949749 0.0044004551
sample91 0.0310397965 0.0440610926
sample92 0.0270570567 0.0324380452
sample93 -0.0215009202 0.0063993941
sample94 -0.0415702912 -0.0037692077
sample95 -0.0168416047 0.0010019120
sample96 -0.0285582661 -0.0187991000
sample97 -0.0490843868 -0.0266760748
sample98 -0.0171579033 -0.0112897471
sample99 -0.0271316525 0.0232395583
sample100 -0.0301789816 0.0305498693
sample101 -0.0264371151 0.0170723968
sample102 0.0012767734 -0.0248949597
sample103 0.0055214687 -0.0030040587
sample104 0.0251346074 -0.0165212671
sample105 0.0062424215 -0.0400309901
sample106 0.0069768684 0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679 0.0023637162
sample109 -0.0014762845 0.0165583675
sample110 0.0036971063 0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112 0.0046098120 -0.0048009350
sample113 0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453 0.0135805368
sample116 0.0183575759 0.0665377581
sample117 0.0227640036 -0.0012287760
sample118 0.0015695248 0.0472617382
sample119 0.0190084932 0.0590034062
sample120 -0.0449645755 0.0072755697
sample121 0.0077307184 0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123 0.0016959300 0.0028593594
sample124 -0.0365091615 0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408 0.0019165544
sample127 -0.0494064872 0.0088209044
sample128 -0.0155454766 0.0186819802
sample129 -0.0184340400 0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422 0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133 0.0204029276 -0.0282209049
sample134 0.0175513332 0.0262883962
sample135 0.0029009925 0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323 0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139 0.0073103935 0.0308956174
sample140 0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142 0.0268670676 -0.0239229634
sample143 0.0421049126 -0.0110888235
sample144 0.0017253664 -0.0341766012
sample145 0.0681741320 -0.0073526377
sample146 -0.0239965222 0.0118396767
sample147 -0.0063453522 0.0183130585
sample148 0.0230825251 -0.0379753037
sample149 0.0223298673 0.0188909118
sample150 0.0055709108 0.0174179009
sample151 0.0039177786 -0.0233533275
sample152 0.0134325667 0.0302344591
sample153 0.0511990309 0.0730230140
sample154 0.0006698324 0.0154177486
sample155 0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599 0.0154934573
sample158 0.0201775524 -0.0332982124
sample159 -0.0086909001 0.0073496711
sample160 0.0295437331 -0.0555734536
sample161 0.0332754288 0.0033779619
sample162 0.0121954537 0.0433540412
sample163 -0.0173490933 0.0227219128
sample164 0.0143374783 -0.0453542590
sample165 0.0343612593 -0.0511194536
sample166 -0.0157536004 0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919 0.0060747155
sample169 0.0116231468 -0.0015112800
>
> ## 3.3 Plotting VAF
>
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
>
> # JIVE plotVAF
> plotVAF(jiveRes)
>
>
> #########################
> ## PART 4. Plot Results
>
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
Warning message:
`aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.
ℹ The deprecated feature was likely used in the STATegRa package.
Please report the issue to the authors.
>
> # Scores for common part. JIVE
> plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
>
> # Scores for common part. O2PLS.
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for common part. O2PLS.
> plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common",
+ combined=TRUE,block=NULL,color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
>
> # Scores for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for distinctive part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual",
+ combined=TRUE,block=NULL,color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
> # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block)
> p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for common and distinctive part. DISCO (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Loadings for common part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> # Loadings for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> # Combined plot for loadings from common and distinctive part (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
>
> ## Plot scores and loadings togheter: Common components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> ## Plot scores and loadings togheter: Common components O2PLS
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> ## Plot scores and loadings togheter: Distintive components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
>
>
> proc.time()
user system elapsed
5.828 0.128 5.977
STATegRa.Rcheck/STATegRa-Ex.timings
| name | user | system | elapsed | |
| STATegRaUsersGuide | 0.001 | 0.000 | 0.001 | |
| STATegRa_data | 0.101 | 0.004 | 0.104 | |
| STATegRa_data_TCGA_BRCA | 0.001 | 0.001 | 0.001 | |
| bioDist | 0.161 | 0.007 | 0.168 | |
| bioDistFeature | 0.142 | 0.010 | 0.159 | |
| bioDistFeaturePlot | 0.135 | 0.007 | 0.143 | |
| bioDistW | 0.135 | 0.007 | 0.142 | |
| bioDistWPlot | 0.123 | 0.006 | 0.129 | |
| bioMap | 0.001 | 0.001 | 0.003 | |
| combiningMappings | 0.004 | 0.001 | 0.005 | |
| createOmicsExpressionSet | 0.055 | 0.002 | 0.059 | |
| getInitialData | 0.275 | 0.008 | 0.286 | |
| getLoadings | 0.266 | 0.016 | 0.282 | |
| getMethodInfo | 0.318 | 0.005 | 0.325 | |
| getPreprocessing | 0.355 | 0.219 | 0.587 | |
| getScores | 0.322 | 0.006 | 0.328 | |
| getVAF | 0.250 | 0.002 | 0.252 | |
| holistOmics | 0.001 | 0.000 | 0.002 | |
| modelSelection | 0.692 | 0.551 | 1.262 | |
| omicsCompAnalysis | 1.945 | 0.010 | 1.974 | |
| omicsNPC | 0.001 | 0.001 | 0.001 | |
| plotRes | 2.358 | 0.009 | 2.372 | |
| plotVAF | 2.093 | 0.008 | 2.114 | |