David Gomez-Cabrero
| Snapshot Date: 2019-04-08 17:01:18 -0400 (Mon, 08 Apr 2019) |
| URL: https://git.bioconductor.org/packages/STATegRa |
| Branch: master |
| Last Commit: 0345138 |
| Last Changed Date: 2018-10-30 11:54:33 -0400 (Tue, 30 Oct 2018) |
| Back to Multiple platform build/check report for BioC 3.9 |
|
This page was generated on 2019-04-09 13:30:39 -0400 (Tue, 09 Apr 2019).
| Package 1564/1703 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||
| STATegRa 1.19.0 David Gomez-Cabrero
| malbec2 | Linux (Ubuntu 18.04.2 LTS) / x86_64 | OK | OK | OK | | ||||||
| tokay2 | Windows Server 2012 R2 Standard / x64 | OK | OK | OK | OK | | ||||||
| celaya2 | OS X 10.11.6 El Capitan / x86_64 | OK | OK | OK | OK | | ||||||
| merida2 | OS X 10.11.6 El Capitan / x86_64 | OK | OK | [ OK ] | OK |
| Package: STATegRa |
| Version: 1.19.0 |
| Command: /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/Library/Frameworks/R.framework/Versions/Current/Resources/library --no-vignettes --timings STATegRa_1.19.0.tar.gz |
| StartedAt: 2019-04-09 03:58:25 -0400 (Tue, 09 Apr 2019) |
| EndedAt: 2019-04-09 04:02:59 -0400 (Tue, 09 Apr 2019) |
| EllapsedTime: 274.9 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: STATegRa.Rcheck |
| Warnings: 0 |
##############################################################################
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###
### Running command:
###
### /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/Library/Frameworks/R.framework/Versions/Current/Resources/library --no-vignettes --timings STATegRa_1.19.0.tar.gz
###
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##############################################################################
* using log directory ‘/Users/biocbuild/bbs-3.9-bioc/meat/STATegRa.Rcheck’
* using R Under development (unstable) (2018-11-27 r75683)
* using platform: x86_64-apple-darwin15.6.0 (64-bit)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘STATegRa/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘STATegRa’ version ‘1.19.0’
* 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 R 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 ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU or elapsed time > 5s
user system elapsed
plotRes 7.269 0.457 7.810
plotVAF 6.908 0.392 7.351
omicsCompAnalysis 6.542 0.400 6.992
* 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 in ‘inst/doc’ ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: 1 NOTE
See
‘/Users/biocbuild/bbs-3.9-bioc/meat/STATegRa.Rcheck/00check.log’
for details.
STATegRa.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD INSTALL STATegRa ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/3.6/Resources/library’ * installing *source* package ‘STATegRa’ ... ** 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 * DONE (STATegRa)
STATegRa.Rcheck/tests/runTests.Rout
R Under development (unstable) (2018-11-27 r75683) -- "Unsuffered Consequences"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin15.6.0 (64-bit)
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 -- Tue Apr 9 04:02:53 2019
***********************************************
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
4.326 0.321 4.668
STATegRa.Rcheck/tests/STATEgRa_Example.omicsCLUST.Rout
R Under development (unstable) (2018-11-27 r75683) -- "Unsuffered Consequences"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin15.6.0 (64-bit)
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: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
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, append,
as.data.frame, basename, cbind, colMeans, colSums, colnames,
dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
intersect, is.unsorted, lapply, mapply, match, mget, order, paste,
pmax, pmax.int, pmin, pmin.int, rank, rbind, rowMeans, rowSums,
rownames, sapply, setdiff, sort, table, tapply, union, unique,
unsplit, which, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
>
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+ distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+ map.name = "id",
+ map.metadata = list(),
+ params = list())
>
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
>
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
>
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+ bioDistList = bioDistList,
+ weights=weights)
> length(bioDistWList)
[1] 4
>
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
>
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+ listDistW = bioDistWList,
+ method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], :
Cannot compute exact p-value with ties
4: In plot.window(...) :
relative range of values ( 0 * EPS) is small (axis 2)
5: In plot.window(...) :
relative range of values ( 0 * EPS) is small (axis 2)
6: In plot.window(...) :
relative range of values ( 0 * EPS) is small (axis 2)
7: In plot.window(...) :
relative range of values ( 0 * EPS) is small (axis 2)
>
> #############################################
> ## 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
35.676 1.023 36.989
STATegRa.Rcheck/tests/STATegRa_Example.omicsNPC.Rout
R Under development (unstable) (2018-11-27 r75683) -- "Unsuffered Consequences"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin15.6.0 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> rm(list = ls())
> require("STATegRa")
Loading required package: STATegRa
> # Load the data
> data("TCGA_BRCA_Batch_93")
> # Setting dataTypes
> dataTypes <- c("count", "count", "continuous")
> # Setting methods to combine pvalues
> combMethods = c("Fisher", "Liptak", "Tippett")
> # Setting number of permutations
> numPerms = 1000
> # Setting number of cores
> numCores = 1
> # Setting holistOmics to print out the steps that it performs.
> verbose = TRUE
> # Run holistOmics analysis.
> output <- omicsNPC(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes, combMethods = combMethods, numPerms = numPerms, numCores = numCores, verbose = verbose)
Compute initial statistics on data
Building NULL distributions by permuting data
Compute pseudo p-values based on NULL distributions...
NPC p-values calculation...
>
> proc.time()
user system elapsed
101.277 2.119 104.397
STATegRa.Rcheck/tests/STATEgRa_Example.omicsPCA.Rout
R Under development (unstable) (2018-11-27 r75683) -- "Unsuffered Consequences"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin15.6.0 (64-bit)
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 0.0781574296 -0.0431501209
sample2 -0.1192218343 0.0294089861
sample3 -0.0531412175 -0.0746839875
sample4 0.0292975212 -0.0005958480
sample5 0.0202091767 0.0110463948
sample6 0.1226089074 0.1053466563
sample7 0.1078928052 -0.0322476908
sample8 0.1782895389 0.1449364649
sample9 0.0468698127 -0.0455174336
sample10 -0.0036030480 0.0420111724
sample11 -0.0035566483 -0.0566292732
sample12 0.1006128901 0.0641380460
sample13 -0.1174408253 0.0907488605
sample14 0.0981203260 0.0617737610
sample15 0.0085334251 -0.0087014671
sample16 0.0783148697 0.1581293677
sample17 -0.1483609898 0.0638581944
sample18 -0.0963086278 0.0556639178
sample19 -0.0217244115 -0.0720085291
sample20 -0.0635636455 -0.0779654017
sample21 -0.0201840276 0.1566391578
sample22 0.0218268641 -0.0764105419
sample23 0.0852042071 -0.0032687229
sample24 -0.1287170422 0.1924545900
sample25 -0.0430574121 -0.0456564643
sample26 -0.1453896773 0.0541513182
sample27 -0.0197488911 -0.1185658111
sample28 -0.1025336241 0.0650686122
sample29 0.0706018416 -0.0682989273
sample30 -0.1295627614 -0.0066771178
sample31 0.1147449127 0.1232685780
sample32 -0.0374310930 0.0380176520
sample33 0.0599515911 0.0136865775
sample34 -0.0984200846 0.0375320169
sample35 -0.0543098407 -0.0378107390
sample36 0.1403625389 -0.0343758519
sample37 0.0228941769 -0.0732849496
sample38 -0.0222077331 -0.0962595424
sample39 -0.0941738477 0.0215199573
sample40 0.0643801041 -0.0687874101
sample41 -0.0327638086 -0.1232188175
sample42 -0.0500431839 -0.0292472515
sample43 -0.0184498860 0.0233010421
sample44 0.1487898970 0.1171357267
sample45 -0.1050774084 0.1123202814
sample46 -0.1151195795 -0.1094029465
sample47 -0.0962593770 -0.0288464705
sample48 0.0004837409 -0.0310275724
sample49 0.1135207870 0.1213973742
sample50 -0.0123553195 -0.1740743297
sample51 0.0550529905 0.1258885816
sample52 0.0499121265 0.0728543823
sample53 0.1119773677 0.1588012784
sample54 -0.0360055679 0.0228575413
sample55 0.0210418985 0.0006731370
sample56 -0.0434169194 0.0633125979
sample57 0.0197824676 0.1150712762
sample58 0.0030439877 0.0326097361
sample59 0.0500253049 0.0129416614
sample60 0.0184278625 0.0136082398
sample61 0.0150299411 0.0635024304
sample62 -0.0304763992 -0.0201321155
sample63 0.1102252529 0.1285977111
sample64 0.1552588117 0.0971167944
sample65 -0.0058503047 0.0207115761
sample66 -0.0025605311 0.0424320784
sample67 0.1546634742 -0.0661719553
sample68 0.0536369171 -0.0923685518
sample69 0.0640330323 0.0081982586
sample70 0.0163517671 -0.0663230204
sample71 -0.0102537663 -0.1345920384
sample72 -0.0654196134 -0.0196121435
sample73 -0.1048556201 0.0220936841
sample74 0.0123799447 0.0586114200
sample75 0.0392077892 -0.0209755682
sample76 0.0648953363 -0.0524764532
sample77 0.1172922127 -0.0201186409
sample78 -0.1463067983 0.0708474071
sample79 0.0265211239 -0.1603305182
sample80 0.0279737096 -0.0214206296
sample81 0.0079211480 -0.0738449804
sample82 -0.1544236535 -0.0361468378
sample83 -0.0494211525 -0.0050051104
sample84 -0.0259038457 -0.0346548524
sample85 0.1116484292 -0.0031499965
sample86 -0.1306483111 -0.0377216647
sample87 -0.0554778212 -0.0459749129
sample88 -0.0301623790 0.0382197375
sample89 -0.1016866728 0.0694032763
sample90 0.0086819844 -0.0201320044
sample91 0.1578625238 -0.2097828945
sample92 0.0170936905 -0.1655803795
sample93 -0.0979806855 -0.0121512560
sample94 0.0131484052 -0.0114932180
sample95 0.0315682632 -0.0758857692
sample96 0.0024125609 -0.0470134341
sample97 0.0634545405 0.0270332587
sample98 -0.0359374686 -0.0135488995
sample99 -0.1009163217 0.1124781682
sample100 0.0551753118 0.0246489208
sample101 -0.0080118958 -0.1627367600
sample102 -0.0046444159 0.0095635994
sample103 -0.0472523235 -0.0940393545
sample104 0.0198159523 -0.0591090145
sample105 -0.0400237779 -0.0160911003
sample106 -0.0923808374 0.0369018068
sample107 -0.1019373983 0.0224953806
sample108 -0.0877091655 -0.0128833815
sample109 0.0864824483 -0.0900938158
sample110 -0.1223115514 -0.0096085139
sample111 0.0257354675 -0.0936166212
sample112 -0.0765286622 0.0270346747
sample113 0.0258803314 0.0377498795
sample114 0.0021138868 -0.0882014233
sample115 0.0303460320 -0.0723581790
sample116 0.0780508531 -0.0685063886
sample117 0.0536898199 -0.0911905317
sample118 0.0666651191 -0.0236230307
sample119 0.1021871614 -0.2324935098
sample120 0.0750216568 0.0243379982
sample121 -0.0756936362 0.0942950052
sample122 -0.0259627996 0.0731988964
sample123 -0.1037846295 -0.0369197693
sample124 0.0611207997 0.0421725647
sample125 -0.0738472725 0.0066950320
sample126 0.0972916383 0.0762638418
sample127 0.0824697596 -0.0096637149
sample128 -0.1249407546 0.0929314199
sample129 -0.0734067608 -0.0434364227
sample130 -0.0003502045 -0.0309852556
sample131 0.0930182788 0.0155936241
sample132 0.0736222868 0.0733031338
sample133 -0.0498397979 -0.0462436934
sample134 0.1644873517 0.0720004507
sample135 -0.0752297253 0.0003816375
sample136 0.0227145672 -0.0495507011
sample137 0.0564717311 -0.0288917233
sample138 0.0255988149 -0.0610855068
sample139 0.0621217786 0.0235806352
sample140 -0.0604152630 -0.0435594857
sample141 0.0246743986 0.0532649209
sample142 -0.0409560251 0.0316281113
sample143 -0.0077355202 -0.0476895905
sample144 0.0173240812 -0.0156777698
sample145 0.0485474676 0.1202771522
sample146 0.0419645531 -0.0811282392
sample147 -0.0977308437 -0.0274841883
sample148 0.0368256246 0.0803979997
sample149 -0.0072865816 -0.1532985183
sample150 0.1020825287 0.0624773353
sample151 0.0305399104 -0.0289276604
sample152 -0.0533594786 -0.0638308451
sample153 -0.0891627354 0.1799581622
sample154 -0.0727557545 -0.0834161753
sample155 -0.0880668639 -0.0220820858
sample156 -0.0276561114 -0.0326625900
sample157 -0.1155032200 0.0183615548
sample158 -0.0281507553 -0.0104939302
sample159 0.0663235760 0.0443838067
sample160 -0.0302643903 0.0404264598
sample161 0.0114715626 -0.0591023908
sample162 -0.1337087020 0.1398135550
sample163 0.1330124580 0.1688781557
sample164 -0.0150336057 0.0028417322
sample165 0.0076520291 -0.0164127854
sample166 0.0367794447 0.0630663298
sample167 0.1111988845 0.0030057617
sample168 -0.0672981565 0.0446279645
sample169 -0.0413005007 0.0224392881
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 0.0420516076 0.0867863019
sample2 0.0820828047 -0.0410978242
sample3 -0.0155898114 -0.0195182284
sample4 0.1001336988 -0.0410786920
sample5 0.0153465669 -0.0253259730
sample6 -0.0340328041 -0.0408223184
sample7 -0.0722579193 0.0002332430
sample8 0.0457496914 -0.0370016427
sample9 0.0086250128 0.0820184908
sample10 0.0423597811 -0.0083923413
sample11 -0.0022547469 0.0787766083
sample12 -0.0322106588 0.1479824723
sample13 0.0293887725 -0.0306748746
sample14 -0.0337483999 -0.0367506818
sample15 -0.0815538599 0.1275622690
sample16 -0.0508455279 0.0540604672
sample17 -0.0062597783 0.0041023694
sample18 -0.0705640837 -0.0351047554
sample19 0.0476843219 -0.0509598155
sample20 -0.0522961177 0.0715522038
sample21 0.0119123517 -0.0376093222
sample22 -0.0724391454 -0.0095624890
sample23 0.0992532236 0.0134288552
sample24 0.1595114287 0.0728661516
sample25 0.0920694215 -0.0749757457
sample26 0.0595539369 0.0848965869
sample27 -0.0826483294 -0.0086735150
sample28 0.0384786990 0.0440966743
sample29 -0.0777669779 0.1735308748
sample30 -0.1229471205 -0.0819005217
sample31 -0.0579848860 -0.0238644695
sample32 -0.0970393864 -0.0111426091
sample33 -0.1017588217 -0.0630442329
sample34 -0.0637923400 0.0377941842
sample35 -0.0789983976 -0.0229723001
sample36 -0.1224939430 -0.1274954623
sample37 -0.1798820062 -0.1673426942
sample38 -0.0466302197 0.0888161124
sample39 0.0168687455 0.0421533701
sample40 -0.1756391408 -0.1526641882
sample41 -0.0042368204 0.0004928894
sample42 0.0447850348 -0.0651505094
sample43 -0.0482308834 -0.0253529172
sample44 0.1986712027 -0.0545778431
sample45 0.0741834289 0.0054703030
sample46 -0.0478769773 -0.0007071825
sample47 -0.0608187798 0.0481622808
sample48 0.1381490074 0.0578287455
sample49 0.0530517514 -0.1405533044
sample50 0.0173803867 0.1602389741
sample51 -0.0462563782 0.0303473862
sample52 -0.0280066847 0.0280388409
sample53 -0.0667624694 0.0237702098
sample54 -0.0121834122 -0.0521354309
sample55 -0.0182395988 0.0221328473
sample56 0.0001254148 0.0030907314
sample57 -0.0316678149 0.0530190278
sample58 -0.0393918893 -0.0297798670
sample59 -0.1278291427 -0.0546527648
sample60 -0.1486985677 0.1069156914
sample61 -0.0793123965 0.0569796673
sample62 -0.1172800475 -0.0149198183
sample63 0.0028724414 0.1300519740
sample64 -0.0237366562 0.1073287712
sample65 0.0126534675 0.0589808390
sample66 0.0468193764 -0.0771072839
sample67 -0.1494263979 -0.0769859883
sample68 -0.0977959488 -0.0577350751
sample69 -0.0403087228 0.0156042212
sample70 -0.0221529485 0.0315441060
sample71 0.0546437452 -0.0272396481
sample72 -0.1107487420 -0.0537319089
sample73 -0.0906761389 0.0579966816
sample74 -0.0586556526 0.0121421776
sample75 -0.0390492730 0.0349282921
sample76 0.0022961327 -0.1676558762
sample77 0.0232096116 -0.2067302839
sample78 0.0929753425 -0.0434939758
sample79 0.1619499999 -0.0378114525
sample80 -0.0680364784 0.1424663672
sample81 0.0530785684 -0.0358350927
sample82 -0.0266821194 -0.0577445010
sample83 -0.1517234997 -0.0448553962
sample84 0.0570967808 -0.0273813360
sample85 -0.1086290175 -0.1228119063
sample86 -0.0833859077 -0.0442914749
sample87 -0.0022017752 -0.0943906819
sample88 0.0078223512 -0.1140506579
sample89 -0.0611058665 -0.0094585049
sample90 -0.0022927712 -0.0936253971
sample91 -0.0433585537 0.3205983033
sample92 0.1815338782 -0.0334680621
sample93 -0.0267630138 0.0614429103
sample94 -0.0181877138 0.0605090462
sample95 0.0720377416 -0.0013045777
sample96 0.0559715952 -0.0118791520
sample97 0.0217410748 0.0195414074
sample98 -0.0379176803 0.0588357208
sample99 0.0792425030 -0.0151274066
sample100 -0.0222116889 -0.0023321393
sample101 0.0387232561 0.1224226237
sample102 0.2094613864 -0.0516443134
sample103 -0.0138479091 0.0301052052
sample104 0.0807988124 -0.0162719075
sample105 0.0520493442 -0.1229665282
sample106 0.0192612468 -0.0185238256
sample107 -0.0319017254 0.0405123353
sample108 0.0140691451 0.0163421358
sample109 0.1831932126 0.0613007179
sample110 0.0292790813 -0.0199849144
sample111 0.1423254214 0.0327340048
sample112 -0.0426333393 -0.0029083355
sample113 0.0771903778 0.0268733433
sample114 0.0241643475 -0.0184080413
sample115 0.1959017232 0.0460130257
sample116 0.1394477062 -0.0530806095
sample117 0.1672363240 -0.1386536744
sample118 0.0448344618 -0.0117622024
sample119 0.0910391672 0.2217433322
sample120 0.0331391852 -0.0057274590
sample121 -0.0307576541 0.1392506556
sample122 0.0839779614 -0.0291994674
sample123 -0.0239649705 -0.0642163642
sample124 0.0909149897 0.0130419256
sample125 0.0065350574 -0.1092631835
sample126 -0.0935312962 0.1368284222
sample127 -0.0035387422 0.0292755654
sample128 0.0660293916 0.1018566107
sample129 -0.0693637676 -0.0695421542
sample130 -0.0008492768 -0.0669704303
sample131 -0.0431024361 0.0174064960
sample132 0.0637038754 0.0029374522
sample133 0.0289495643 -0.0390818871
sample134 -0.0446204734 0.0456334563
sample135 -0.0712336799 0.0521635126
sample136 -0.0596269682 0.0197299500
sample137 -0.0793151312 -0.0380628095
sample138 0.0973549548 -0.0454218454
sample139 -0.0539905776 -0.1534327250
sample140 -0.0850825761 0.0955814764
sample141 0.0192680671 -0.0554450151
sample142 0.0672261135 -0.0461321098
sample143 0.0303731113 -0.0519260281
sample144 0.0089364990 0.0145814902
sample145 0.0638766977 0.0122258173
sample146 -0.0585854470 0.0063083545
sample147 -0.0894132946 -0.1124615468
sample148 0.0216364961 -0.0615967234
sample149 0.0515423644 -0.0839903513
sample150 -0.0568284870 -0.0124468829
sample151 0.0789532940 -0.0261831355
sample152 0.0330755126 0.1306443551
sample153 0.1751927465 0.1497731569
sample154 -0.0421422509 -0.0037010037
sample155 -0.0680176892 0.0095711406
sample156 -0.0388910055 0.1057563072
sample157 -0.0314769554 0.0561367489
sample158 -0.0329620257 0.0353947406
sample159 0.0398415328 -0.1007373897
sample160 -0.0424939637 0.0108496247
sample161 0.0888372218 -0.0679700344
sample162 0.0027473214 0.1237843766
sample163 0.0126101754 0.0725434203
sample164 0.0566779491 -0.0458324320
sample165 0.0315336571 -0.0236362417
sample166 0.0612056709 -0.0425233223
sample167 -0.0142729886 0.0179308307
sample168 0.0169502400 -0.0769617966
sample169 -0.0675080823 0.0131505475
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 -0.0012329644 -1.635717e-01
sample2 -0.0724350070 -6.021244e-03
sample3 -0.0188460445 -1.080036e-01
sample4 0.0390145295 3.114241e-04
sample5 0.1774811636 -2.996384e-02
sample6 -0.0451444437 -3.455857e-02
sample7 -0.0226466248 -7.020175e-03
sample8 -0.1033680241 -9.856763e-03
sample9 0.1350011755 8.979098e-02
sample10 0.1259887245 -5.097852e-02
sample11 0.0979788380 7.086533e-02
sample12 -0.0863019110 -8.620317e-02
sample13 -0.1381401121 1.828007e-01
sample14 -0.0615073870 -2.642803e-02
sample15 0.0381598945 -3.101665e-02
sample16 -0.0048776752 1.271854e-03
sample17 -0.0788480969 -1.547553e-02
sample18 -0.0884188780 -3.795486e-02
sample19 0.0703044427 -1.084004e-01
sample20 -0.0025585519 7.975873e-02
sample21 0.0941601638 -4.126739e-02
sample22 -0.0550273416 -7.806745e-02
sample23 0.0679495323 -4.102005e-02
sample24 -0.1310962797 1.649310e-01
sample25 0.0113585290 -4.426862e-02
sample26 -0.1402945922 2.016544e-02
sample27 0.0261561130 1.588420e-03
sample28 -0.0724198724 5.850594e-02
sample29 -0.0330058565 2.060807e-03
sample30 -0.0228752585 -2.015431e-02
sample31 -0.0635067964 -6.670333e-02
sample32 0.0685099628 -4.955273e-02
sample33 -0.0777765236 -1.272079e-01
sample34 0.0157842393 -3.024314e-02
sample35 -0.0529632763 1.500972e-01
sample36 0.0070900764 2.025307e-01
sample37 -0.0442420605 1.802088e-01
sample38 -0.0781511295 -3.676422e-02
sample39 0.0120331851 -3.388841e-02
sample40 -0.0473292078 1.471561e-01
sample41 0.0228189420 -2.673556e-02
sample42 -0.0245360243 -7.960866e-02
sample43 0.1036362793 -8.229577e-02
sample44 -0.1012228775 7.049455e-02
sample45 0.0013732030 -2.450908e-02
sample46 -0.0558510019 2.947365e-03
sample47 -0.0380481187 4.554172e-02
sample48 0.0784342109 4.888982e-02
sample49 -0.0605163965 -1.162353e-02
sample50 0.0530079291 -2.737935e-02
sample51 0.1514646516 5.678346e-02
sample52 0.1860935231 1.246717e-01
sample53 -0.0064177117 -2.700992e-02
sample54 0.0697038334 -2.308388e-02
sample55 0.1633577032 1.366442e-02
sample56 0.1011485094 4.682206e-02
sample57 0.1730374203 1.609603e-01
sample58 -0.0071384717 -1.666955e-02
sample59 -0.0030461700 3.005284e-02
sample60 0.0215835129 2.665877e-01
sample61 0.1510583626 1.002385e-01
sample62 -0.0925533970 -4.845843e-02
sample63 -0.0596311797 -4.137021e-02
sample64 -0.0449225801 -2.600573e-03
sample65 0.0939383759 -4.406908e-02
sample66 0.1063400751 -5.709992e-02
sample67 -0.0201590011 2.361727e-01
sample68 0.0037203203 2.418387e-02
sample69 -0.0645161213 -1.155622e-01
sample70 -0.1013440013 -1.351789e-01
sample71 -0.0016467858 -2.976843e-02
sample72 0.0328893002 -2.835859e-02
sample73 0.0275080023 -5.148186e-02
sample74 0.1341719671 -7.895280e-02
sample75 0.0951575662 -3.943185e-02
sample76 -0.0864721956 3.034991e-02
sample77 -0.1035749559 -2.545354e-02
sample78 -0.1575644137 4.939596e-02
sample79 0.0189137120 4.874679e-02
sample80 0.1384140583 4.264095e-05
sample81 -0.0118846446 -6.357932e-02
sample82 -0.1675308179 3.533911e-02
sample83 -0.0065673436 -7.812611e-02
sample84 0.1486891620 -3.109057e-02
sample85 -0.0532724429 7.417883e-02
sample86 -0.1138477353 -1.916715e-05
sample87 0.0432863985 6.080472e-02
sample88 0.0433450366 1.402491e-01
sample89 0.0331205757 -1.395400e-02
sample90 -0.0607412817 -8.610414e-02
sample91 -0.0566272605 1.303747e-01
sample92 -0.0359582460 1.061604e-01
sample93 -0.0433646371 -4.443635e-02
sample94 -0.0477291304 -1.059574e-01
sample95 -0.0249595754 -3.980525e-02
sample96 0.0035219019 -9.293928e-02
sample97 -0.0066048753 -1.527231e-01
sample98 0.0020366809 -5.579550e-02
sample99 -0.0886616097 -3.728223e-02
sample100 -0.1091259139 -3.560420e-02
sample101 -0.0739726464 -4.318000e-02
sample102 0.0574461184 -2.783911e-02
sample103 0.0142731026 9.705544e-03
sample104 0.0710395230 4.068351e-02
sample105 0.0980831353 -3.452952e-02
sample106 -0.0254259317 3.628985e-02
sample107 -0.0160653462 -9.173394e-02
sample108 -0.0200987658 -2.379692e-02
sample109 -0.0389780613 1.692360e-02
sample110 -0.0326304847 2.988110e-02
sample111 0.0676937594 -6.038212e-02
sample112 0.0167883421 5.336938e-03
sample113 0.0969217027 -2.757602e-02
sample114 -0.0026398344 -9.209158e-02
sample115 -0.0308047280 1.603824e-02
sample116 -0.1240307142 1.273000e-01
sample117 0.0334729116 5.392711e-02
sample118 -0.1037152905 6.252431e-02
sample119 -0.1064176613 1.196202e-01
sample120 -0.0771355087 -1.004932e-01
sample121 -0.0129350765 3.181977e-02
sample122 0.0847492295 -5.568324e-02
sample123 -0.0041336785 7.693174e-03
sample124 -0.0583457988 -8.396388e-02
sample125 0.0634844594 -5.232540e-02
sample126 -0.0662580964 -1.091733e-01
sample127 -0.0865024603 -1.094176e-01
sample128 -0.0627817436 -1.470961e-02
sample129 -0.0336276464 -4.007860e-02
sample130 -0.0293517748 -8.046117e-02
sample131 -0.0469197669 -2.209755e-03
sample132 -0.0241740673 -1.248598e-01
sample133 0.0907303217 1.466700e-02
sample134 -0.0350842082 7.539662e-02
sample135 0.0001333392 9.185371e-03
sample136 -0.0335876067 -9.860275e-02
sample137 -0.0640148919 -7.554471e-02
sample138 0.0060964864 -1.742762e-02
sample139 -0.0592084469 5.614968e-02
sample140 0.0427985915 -1.099552e-02
sample141 0.0618796382 -9.301037e-02
sample142 0.0898554475 3.573419e-02
sample143 0.0817389224 8.880524e-02
sample144 0.0787754784 -3.821392e-02
sample145 0.1085821588 1.569477e-01
sample146 -0.0589557943 -4.373362e-02
sample147 -0.0495330461 7.277188e-03
sample148 0.1161592791 9.079097e-03
sample149 -0.0121579438 7.788372e-02
sample150 -0.0314512548 3.520212e-02
sample151 0.0575382188 -1.945352e-02
sample152 -0.0494542089 7.025537e-02
sample153 -0.0941332721 2.153298e-01
sample154 -0.0335932013 2.078727e-02
sample155 0.0690457635 -2.780411e-02
sample156 0.1039901615 -6.292526e-02
sample157 -0.0408645795 8.065516e-03
sample158 0.1018105302 7.816871e-03
sample159 -0.0281730533 -1.207205e-02
sample160 0.1643052998 2.978105e-03
sample161 0.0374329272 8.524611e-02
sample162 -0.0804535336 8.349757e-02
sample163 -0.0743227975 -1.406223e-02
sample164 0.1208806020 -2.139459e-02
sample165 0.1608115922 2.025192e-02
sample166 -0.0425944633 -2.660713e-02
sample167 -0.0226849479 -4.464282e-02
sample168 -0.0180735588 -7.466099e-04
sample169 0.0190778991 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)
>
> # 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
16.685 0.760 17.582
STATegRa.Rcheck/STATegRa-Ex.timings
| name | user | system | elapsed | |
| STATegRaUsersGuide | 0.001 | 0.000 | 0.002 | |
| STATegRa_data | 0.263 | 0.021 | 0.287 | |
| STATegRa_data_TCGA_BRCA | 0.003 | 0.001 | 0.004 | |
| bioDist | 0.614 | 0.033 | 0.651 | |
| bioDistFeature | 0.599 | 0.030 | 0.635 | |
| bioDistFeaturePlot | 0.437 | 0.042 | 0.490 | |
| bioDistW | 0.466 | 0.032 | 0.503 | |
| bioDistWPlot | 0.482 | 0.028 | 0.515 | |
| bioMap | 0.004 | 0.002 | 0.005 | |
| combiningMappings | 0.015 | 0.001 | 0.016 | |
| createOmicsExpressionSet | 0.169 | 0.007 | 0.178 | |
| getInitialData | 0.876 | 0.251 | 1.137 | |
| getLoadings | 0.999 | 0.277 | 1.294 | |
| getMethodInfo | 0.803 | 0.189 | 1.007 | |
| getPreprocessing | 1.471 | 0.577 | 2.064 | |
| getScores | 0.875 | 0.198 | 1.099 | |
| getVAF | 0.977 | 0.158 | 1.143 | |
| holistOmics | 0.004 | 0.001 | 0.005 | |
| modelSelection | 2.779 | 1.328 | 4.158 | |
| omicsCompAnalysis | 6.542 | 0.400 | 6.992 | |
| omicsNPC | 0.004 | 0.002 | 0.005 | |
| plotRes | 7.269 | 0.457 | 7.810 | |
| plotVAF | 6.908 | 0.392 | 7.351 | |