Back to Multiple platform build/check report for BioC 3.19: simplified long |
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This page was generated on 2024-05-31 17:03:16 -0400 (Fri, 31 May 2024).
Hostname | OS | Arch (*) | R version | Installed pkgs |
---|---|---|---|---|
nebbiolo1 | Linux (Ubuntu 22.04.3 LTS) | x86_64 | 4.4.0 (2024-04-24) -- "Puppy Cup" | 4753 |
kjohnson3 | macOS 13.6.5 Ventura | arm64 | 4.4.0 (2024-04-24) -- "Puppy Cup" | 4464 |
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 2083/2300 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
STATegRa 1.40.0 (landing page) David Gomez-Cabrero
| nebbiolo1 | Linux (Ubuntu 22.04.3 LTS) / x86_64 | OK | OK | OK | |||||||||
kjohnson3 | macOS 13.6.5 Ventura / arm64 | OK | OK | OK | OK | |||||||||
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: 1.40.0 |
Command: /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/home/biocbuild/bbs-3.19-bioc/R/site-library --timings STATegRa_1.40.0.tar.gz |
StartedAt: 2024-05-31 08:46:22 -0400 (Fri, 31 May 2024) |
EndedAt: 2024-05-31 08:50:56 -0400 (Fri, 31 May 2024) |
EllapsedTime: 273.8 seconds |
RetCode: 0 |
Status: OK |
CheckDir: STATegRa.Rcheck |
Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/home/biocbuild/bbs-3.19-bioc/R/site-library --timings STATegRa_1.40.0.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/home/biocbuild/bbs-3.19-bioc/meat/STATegRa.Rcheck’ * using R version 4.4.0 (2024-04-24) * using platform: x86_64-pc-linux-gnu * R was compiled by gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 GNU Fortran (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 * running under: Ubuntu 22.04.4 LTS * using session charset: UTF-8 * checking for file ‘STATegRa/DESCRIPTION’ ... OK * checking extension type ... Package * this is package ‘STATegRa’ version ‘1.40.0’ * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package ‘STATegRa’ can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking ‘build’ directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking loading without being on the library search path ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... NOTE modelSelection,list-numeric-character: no visible binding for global variable ‘components’ modelSelection,list-numeric-character: no visible binding for global variable ‘mylabel’ plotVAF,caClass: no visible binding for global variable ‘comp’ plotVAF,caClass: no visible binding for global variable ‘VAF’ plotVAF,caClass: no visible binding for global variable ‘block’ selectCommonComps,list-numeric: no visible binding for global variable ‘comps’ selectCommonComps,list-numeric: no visible binding for global variable ‘block’ selectCommonComps,list-numeric: no visible binding for global variable ‘comp’ selectCommonComps,list-numeric: no visible binding for global variable ‘ratio’ Undefined global functions or variables: VAF block comp components comps mylabel ratio * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... 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 * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘STATEgRa_Example.omicsCLUST.R’ Running ‘STATEgRa_Example.omicsPCA.R’ Running ‘STATegRa_Example.omicsNPC.R’ Running ‘runTests.R’ OK * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking re-building of vignette outputs ... OK * checking PDF version of manual ... OK * DONE Status: 1 NOTE See ‘/home/biocbuild/bbs-3.19-bioc/meat/STATegRa.Rcheck/00check.log’ for details.
STATegRa.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD INSTALL STATegRa ### ############################################################################## ############################################################################## * installing to library ‘/home/biocbuild/bbs-3.19-bioc/R/site-library’ * installing *source* package ‘STATegRa’ ... ** 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 4.4.0 (2024-04-24) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > BiocGenerics:::testPackage("STATegRa") Common components [1] 2 Distinctive components [[1]] [1] 0 [[2]] [1] 0 Common components [1] 2 Distinctive components [[1]] [1] 1 [[2]] [1] 1 Common components [1] 2 Distinctive components [[1]] [1] 2 [[2]] [1] 2 RUNIT TEST PROTOCOL -- Fri May 31 08:49:14 2024 *********************************************** Number of test functions: 4 Number of errors: 0 Number of failures: 0 1 Test Suite : STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures Number of test functions: 4 Number of errors: 0 Number of failures: 0 Warning messages: 1: In rownames(pData) == colnames(exprs) : longer object length is not a multiple of shorter object length 2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum", : Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2 3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num", : Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3 > > proc.time() user system elapsed 3.017 0.199 3.205
STATegRa.Rcheck/tests/STATEgRa_Example.omicsCLUST.Rout
R version 4.4.0 (2024-04-24) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ########################################### > ########### EXAMPLE OF THE OMICSCLUSTERING > ########################################### > require(STATegRa) Loading required package: STATegRa > > ############################################# > ## PART 1: CREATING a bioMap CLASS > ############################################# > ####### This part creates or reads the map between features. > ####### In the present example the map is downloaded from a resource. > ####### then the class is created. > > #load("../data/STATegRa_S2.rda") > data(STATegRa_S2) > > MAP.SYMBOL<-bioMap(name = "Symbol-miRNA", + metadata = list(type_v1="Gene",type_v2="miRNA", + source_database="targetscan.Hs.eg.db", + data_extraction="July2014"), + map=mapdata) > > > ############################################# > ## PART 2: CREATING a bioDist CLASS > ############################################# > ##### In the second part given a set of main features and surrogate feautres, > ##### the profile of the main features is computed through the surrogate features. > > # Load Data > data(STATegRa_S1) > #load("../data/STATegRa.S1.Rdata") > > ## Create ExpressionSets > # source("../R/STATegRa_omicsPCA_classes_and_methods.R") > # Block1 - Expression data > mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname")) > # Block2 - miRNA expression data > miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname")) > > # Create Gene-gene distance computed through miRNA data > bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1), + reference = "Var1", + mapping = MAP.SYMBOL, + surrogateData = miRNA.ds, ### miRNA data + referenceData = mRNA.ds, ### mRNA data + maxitems=2, + selectionRule="sd", + expfac=NULL, + aggregation = "sum", + distance = "spearman", + noMappingDist = 0, + filtering = NULL, + name = "mRNAbymiRNA") > > require(Biobase) Loading required package: Biobase Loading required package: BiocGenerics 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, intersect, is.unsorted, lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply, union, unique, unsplit, which.max, which.min Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. > > # Create Gene-gene distance through mRNA data > bioDistmRNA<-bioDistclass(name = "mRNAbymRNA", + distance = cor(t(exprs(mRNA.ds)),method="spearman"), + map.name = "id", + map.metadata = list(), + params = list()) > > ############################################# > ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList > ############################################# > > bioDistList<-list(bioDistmRNA,bioDistmiRNA) > weights<-matrix(0,4,2) > weights[,1]<-c(0,0.33,0.67,1) > weights[,2]<-c(1,0.67,0.33,0)# > > bioDistWList<-bioDistW(referenceFeatures = rownames(Block1), + bioDistList = bioDistList, + weights=weights) > length(bioDistWList) [1] 4 > > ############################################# > ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL > ############################################# > > bioDistWPlot(referenceFeatures = rownames(Block1) , + listDistW = bioDistWList, + method.cor="spearman") Warning messages: 1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], : Cannot compute exact p-value with ties 2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], : Cannot compute exact p-value with ties 3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], : Cannot compute exact p-value with ties 4: In plot.window(...) : axis(2, *): range of values ( 0) is small wrt |M| = 3e-09 --> not pretty() 5: In plot.window(...) : axis(2, *): range of values ( 0) is small wrt |M| = 3e-09 --> not pretty() 6: In plot.window(...) : axis(2, *): range of values ( 0) is small wrt |M| = 3e-09 --> not pretty() 7: In plot.window(...) : axis(2, *): range of values ( 0) is small wrt |M| = 3e-09 --> not pretty() > > ############################################# > ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE > ############################################# > > ## IDH1 > > IDH1.F<-bioDistFeature(Feature = "IDH1" , + listDistW = bioDistWList, + threshold.cor=0.7) > bioDistFeaturePlot(data=IDH1.F) > > ## PDGFRA > > #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" , > # listDistW = bioDistWList, > # threshold.cor=0.7) > #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png") > > ## EGFR > #EGFR.F<-bioDistFeature(Feature = "EGFR" , > # listDistW = bioDistWList, > # threshold.cor=0.7) > #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png") > > ## MGMT > #MGMT.F<-bioDistFeature(Feature = "MGMT" , > # listDistW = bioDistWList, > # threshold.cor=0.5) > #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png") > > > > > > proc.time() user system elapsed 22.233 0.572 22.796
STATegRa.Rcheck/tests/STATegRa_Example.omicsNPC.Rout
R version 4.4.0 (2024-04-24) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > rm(list = ls()) > require("STATegRa") Loading required package: STATegRa > # Load the data > data("TCGA_BRCA_Batch_93") > # Setting dataTypes > dataTypes <- c("count", "count", "continuous") > # Setting methods to combine pvalues > combMethods = c("Fisher", "Liptak", "Tippett") > # Setting number of permutations > numPerms = 1000 > # Setting number of cores > numCores = 1 > # Setting holistOmics to print out the steps that it performs. > verbose = TRUE > # Run holistOmics analysis. > output <- omicsNPC(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes, combMethods = combMethods, numPerms = numPerms, numCores = numCores, verbose = verbose) 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 59.164 0.396 59.551
STATegRa.Rcheck/tests/STATEgRa_Example.omicsPCA.Rout
R version 4.4.0 (2024-04-24) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ########################################### > ########### EXAMPLE OF THE OMICSPCA > ########################################### > require(STATegRa) Loading required package: STATegRa > > # g_legend (not exported by STATegRa any more) > ## code from https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs > g_legend<-function(a.gplot){ + tmp <- ggplot_gtable(ggplot_build(a.gplot)) + leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box") + legend <- tmp$grobs[[leg]] + return(legend)} > > ######################### > ## PART 1. Load data > > ## Load data > data(STATegRa_S3) > > ls() [1] "Block1.PCA" "Block2.PCA" "ed.PCA" "g_legend" > > ## Create ExpressionSets > # Block1 - Expression data > B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname")) > # Block2 - miRNA expression data > B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname")) > > ######################### > ## PART 2. Model Selection > > require(grid) Loading required package: grid > require(gridExtra) Loading required package: gridExtra > require(ggplot2) Loading required package: ggplot2 > > ## Select the optimal components > ms <- modelSelection(Input=list(B1,B2),Rmax=4,fac.sel="single%",varthreshold=0.03,center=TRUE,scale=TRUE,weight=TRUE) Common components [1] 2 Distinctive components [[1]] [1] 2 [[2]] [1] 2 > > > ######################### > ## PART 3. Component Analysis > > ## 3.1 Component analysis of the three methods > discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=2,Rspecific=c(2,2),center=TRUE, + scale=TRUE,weight=TRUE) > jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=2,Rspecific=c(2,2),center=TRUE, + scale=TRUE,weight=TRUE) > o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=2,Rspecific=c(2,2),center=TRUE, + scale=TRUE,weight=TRUE) > > ## 3.2 Exploring scores structures > > # Exploring DISCO-SCA scores structure > discoRes@scores$common ## Common scores 1 2 sample1 0.0781574360 -0.0431503107 sample2 -0.1192218471 0.0294087143 sample3 -0.0531412033 -0.0746839792 sample4 0.0292975082 -0.0005961553 sample5 0.0202091748 0.0110463494 sample6 0.1226089041 0.1053467646 sample7 0.1078928179 -0.0322474565 sample8 0.1782895206 0.1449363316 sample9 0.0468698108 -0.0455174267 sample10 -0.0036030550 0.0420110281 sample11 -0.0035566468 -0.0566292446 sample12 0.1006128927 0.0641381090 sample13 -0.1174408461 0.0907488154 sample14 0.0981203263 0.0617738677 sample15 0.0085334377 -0.0087012297 sample16 0.0783148618 0.1581295303 sample17 -0.1483609938 0.0638581910 sample18 -0.0963086216 0.0556641175 sample19 -0.0217244058 -0.0720087103 sample20 -0.0635636357 -0.0779652220 sample21 -0.0201840415 0.1566391126 sample22 0.0218268844 -0.0764103399 sample23 0.0852041964 -0.0032690402 sample24 -0.1287170883 0.1924541179 sample25 -0.0430574176 -0.0456567662 sample26 -0.1453896908 0.0541511088 sample27 -0.0197488698 -0.1185655494 sample28 -0.1025336378 0.0650684936 sample29 0.0706018576 -0.0682986927 sample30 -0.1295627437 -0.0066767419 sample31 0.1147449125 0.1232687476 sample32 -0.0374310816 0.0380179423 sample33 0.0599516097 0.0136868625 sample34 -0.0984200783 0.0375321962 sample35 -0.0543098343 -0.0378104439 sample36 0.1403625480 -0.0343753710 sample37 0.0228941983 -0.0732843047 sample38 -0.0222077164 -0.0962594221 sample39 -0.0941738503 0.0215198806 sample40 0.0643801262 -0.0687867877 sample41 -0.0327637955 -0.1232188151 sample42 -0.0500431829 -0.0292474198 sample43 -0.0184498780 0.0233011721 sample44 0.1487898575 0.1171351375 sample45 -0.1050774271 0.1123200290 sample46 -0.1151195633 -0.1094028076 sample47 -0.0962593687 -0.0288462782 sample48 0.0004837232 -0.0310279930 sample49 0.1135207701 0.1213972233 sample50 -0.0123553049 -0.1740744074 sample51 0.0550529819 0.1258887568 sample52 0.0499121169 0.0728545247 sample53 0.1119773632 0.1588014874 sample54 -0.0360055674 0.0228575770 sample55 0.0210418997 0.0006732074 sample56 -0.0434169278 0.0633126144 sample57 0.0197824527 0.1150714367 sample58 0.0030439908 0.0326098574 sample59 0.0500253188 0.0129420846 sample60 0.0184278671 0.0136087929 sample61 0.0150299401 0.0635027187 sample62 -0.0304763796 -0.0201317681 sample63 0.1102252429 0.1285976826 sample64 0.1552588061 0.0971168702 sample65 -0.0058503064 0.0207115198 sample66 -0.0025605384 0.0424319235 sample67 0.1546634881 -0.0661713827 sample68 0.0536369370 -0.0923682244 sample69 0.0640330426 0.0081983473 sample70 0.0163517829 -0.0663230034 sample71 -0.0102537596 -0.1345922195 sample72 -0.0654195959 -0.0196118030 sample73 -0.1048556079 0.0220939390 sample74 0.0123799506 0.0586115849 sample75 0.0392077978 -0.0209754533 sample76 0.0648953398 -0.0524764327 sample77 0.1172922134 -0.0201186979 sample78 -0.1463068188 0.0708471134 sample79 0.0265211151 -0.1603310056 sample80 0.0279737198 -0.0214204187 sample81 0.0079211511 -0.0738451661 sample82 -0.1544236480 -0.0361467587 sample83 -0.0494211285 -0.0050046589 sample84 -0.0259038487 -0.0346550354 sample85 0.1116484401 -0.0031496093 sample86 -0.1306482965 -0.0377214150 sample87 -0.0554778197 -0.0459748792 sample88 -0.0301623904 0.0382197710 sample89 -0.1016866706 0.0694034577 sample90 0.0086819911 -0.0201320201 sample91 0.1578625417 -0.2097827318 sample92 0.0170936769 -0.1655809131 sample93 -0.0979806788 -0.0121512037 sample94 0.0131484139 -0.0114932038 sample95 0.0315682628 -0.0758860084 sample96 0.0024125625 -0.0470136410 sample97 0.0634545427 0.0270331414 sample98 -0.0359374598 -0.0135488077 sample99 -0.1009163401 0.1124778931 sample100 0.0551753145 0.0246489784 sample101 -0.0080118839 -0.1627369130 sample102 -0.0046444425 0.0095629362 sample103 -0.0472523137 -0.0940393139 sample104 0.0198159451 -0.0591092486 sample105 -0.0400237815 -0.0160912634 sample106 -0.0923808449 0.0369017505 sample107 -0.1019373917 0.0224954360 sample108 -0.0877091650 -0.0128834446 sample109 0.0864824320 -0.0900943880 sample110 -0.1223115557 -0.0096086070 sample111 0.0257354604 -0.0936170882 sample112 -0.0765286594 0.0270348053 sample113 0.0258803190 0.0377496313 sample114 0.0021138963 -0.0882015293 sample115 0.0303460124 -0.0723587948 sample116 0.0780508353 -0.0685067793 sample117 0.0536898040 -0.0911910213 sample118 0.0666651126 -0.0236231489 sample119 0.1021871647 -0.2324937732 sample120 0.0750216554 0.0243378627 sample121 -0.0756936425 0.0942950925 sample122 -0.0259628145 0.0731986170 sample123 -0.1037846233 -0.0369196947 sample124 0.0611207884 0.0421722505 sample125 -0.0738472714 0.0066950003 sample126 0.0972916488 0.0762640922 sample127 0.0824697665 -0.0096637409 sample128 -0.1249407709 0.0929311824 sample129 -0.0734067457 -0.0434362194 sample130 -0.0003501974 -0.0309852757 sample131 0.0930182831 0.0155937638 sample132 0.0736222782 0.0733028953 sample133 -0.0498397983 -0.0462437759 sample134 0.1644873472 0.0720006262 sample135 -0.0752297167 0.0003818536 sample136 0.0227145844 -0.0495505482 sample137 0.0564717480 -0.0288914933 sample138 0.0255988087 -0.0610858131 sample139 0.0621217810 0.0235808419 sample140 -0.0604152477 -0.0435592333 sample141 0.0246743958 0.0532648389 sample142 -0.0409560386 0.0316279166 sample143 -0.0077355243 -0.0476896476 sample144 0.0173240832 -0.0156778068 sample145 0.0485474404 0.1202770131 sample146 0.0419645704 -0.0811280691 sample147 -0.0977308297 -0.0274839045 sample148 0.0368256138 0.0803979496 sample149 -0.0072865780 -0.1532986476 sample150 0.1020825287 0.0624775355 sample151 0.0305399037 -0.0289279080 sample152 -0.0533594806 -0.0638309452 sample153 -0.0891627851 0.1799576560 sample154 -0.0727557424 -0.0834160432 sample155 -0.0880668519 -0.0220818861 sample156 -0.0276561007 -0.0326624960 sample157 -0.1155032180 0.0183616390 sample158 -0.0281507507 -0.0104938243 sample159 0.0663235677 0.0443836909 sample160 -0.0302643890 0.0404265995 sample161 0.0114715522 -0.0591026326 sample162 -0.1337087192 0.1398135470 sample163 0.1330124417 0.1688781126 sample164 -0.0150336124 0.0028415551 sample165 0.0076520252 -0.0164128669 sample166 0.0367794325 0.0630661328 sample167 0.1111988883 0.0030057987 sample168 -0.0672981626 0.0446279110 sample169 -0.0413004954 0.0224395062 > discoRes@scores$dist[[1]] ## Distinctive scores for Block 1 1 2 sample1 -0.0420513982 0.0867863144 sample2 -0.0820828908 -0.0410977905 sample3 0.0155900810 -0.0195182412 sample4 -0.1001337193 -0.0410786537 sample5 -0.0153466176 -0.0253259656 sample6 0.0340324541 -0.0408223214 sample7 0.0722580109 0.0002332125 sample8 -0.0457501756 -0.0370016116 sample9 -0.0086248810 0.0820184900 sample10 -0.0423599098 -0.0083923207 sample11 0.0022549274 0.0787766022 sample12 0.0322105325 0.1479824662 sample13 -0.0293891015 -0.0306748558 sample14 0.0337481907 -0.0367506888 sample15 0.0815539418 0.1275622372 sample16 0.0508450394 0.0540604628 sample17 0.0062596080 0.0041023726 sample18 0.0705639251 -0.0351047774 sample19 -0.0476840832 -0.0509598037 sample20 0.0522963750 0.0715521762 sample21 -0.0119128519 -0.0376093026 sample22 0.0724394079 -0.0095625238 sample23 -0.0992532115 0.0134288932 sample24 -0.1595120326 0.0728662297 sample25 -0.0920692877 -0.0749757148 sample26 -0.0595540526 0.0848966142 sample27 0.0826487005 -0.0086735577 sample28 -0.0384788870 0.0440966947 sample29 0.0777672523 0.1735308386 sample30 0.1229471348 -0.0819005696 sample31 0.0579844981 -0.0238644799 sample32 0.0970392773 -0.0111426423 sample33 0.1017587912 -0.0630442703 sample34 0.0637922569 0.0377941633 sample35 0.0789984730 -0.0229723345 sample36 0.1224939220 -0.1274955127 sample37 0.1798821185 -0.1673427704 sample38 0.0466305771 0.0888160854 sample39 -0.0168687756 0.0421533785 sample40 0.1756392495 -0.1526642623 sample41 0.0042372202 0.0004928762 sample42 -0.0447849347 -0.0651504950 sample43 0.0482308203 -0.0253529330 sample44 -0.1986716268 -0.0545777564 sample45 -0.0741837602 0.0054703418 sample46 0.0478773403 -0.0007072114 sample47 0.0608188905 0.0481622544 sample48 -0.1381489080 0.0578287954 sample49 -0.0530521950 -0.1405532727 sample50 -0.0173797700 0.1602389645 sample51 0.0462559572 0.0303473807 sample52 0.0280064075 0.0280388373 sample53 0.0667619716 0.0237701995 sample54 0.0121833269 -0.0521354332 sample55 0.0182395865 0.0221328408 sample56 -0.0001256294 0.0030907375 sample57 0.0316674078 0.0530190266 sample58 0.0393917802 -0.0297798790 sample59 0.1278290656 -0.0546528124 sample60 0.1486984800 0.1069156352 sample61 0.0793121744 0.0569796432 sample62 0.1172801308 -0.0149198651 sample63 -0.0028727949 0.1300519874 sample64 0.0237363747 0.1073287714 sample65 -0.0126535028 0.0589808462 sample66 -0.0468195295 -0.0771072616 sample67 0.1494264863 -0.0769860521 sample68 0.0977962036 -0.0577351211 sample69 0.0403087347 0.0156042068 sample70 0.0221532164 0.0315440914 sample71 -0.0546433203 -0.0272396398 sample72 0.1107487982 -0.0537319530 sample73 0.0906761187 0.0579966491 sample74 0.0586554853 0.0121421612 sample75 0.0390493527 0.0349282756 sample76 -0.0022960427 -0.1676558805 sample77 -0.0232096271 -0.2067302769 sample78 -0.0929755676 -0.0434939344 sample79 -0.1619495281 -0.0378114058 sample80 0.0680365869 0.1424663396 sample81 -0.0530783298 -0.0358350792 sample82 0.0266822325 -0.0577445154 sample83 0.1517235292 -0.0448554545 sample84 -0.0570966797 -0.0273813170 sample85 0.1086289471 -0.1228119482 sample86 0.0833860349 -0.0442915109 sample87 0.0022018696 -0.0943906872 sample88 -0.0078225576 -0.1140506516 sample89 0.0611056589 -0.0094585218 sample90 0.0022928286 -0.0936253998 sample91 0.0433592819 0.3205982667 sample92 -0.1815334006 -0.0334680087 sample93 0.0267631050 0.0614428987 sample94 0.0181878074 0.0605090383 sample95 -0.0720374927 -0.0013045572 sample96 -0.0559714234 -0.0118791348 sample97 -0.0217411136 0.0195414187 sample98 0.0379177672 0.0588357052 sample99 -0.0792428329 -0.0151273660 sample100 0.0222116218 -0.0023321456 sample101 -0.0387226762 0.1224226231 sample102 -0.2094614313 -0.0516442322 sample103 0.0138482195 0.0301051910 sample104 -0.0807986522 -0.0162718821 sample105 -0.0520493317 -0.1229665095 sample106 -0.0192613677 -0.0185238151 sample107 0.0319017123 0.0405123253 sample108 -0.0140690781 0.0163421398 sample109 -0.1831929185 0.0613007795 sample110 -0.0292790497 -0.0199849044 sample111 -0.1423251032 0.0327340507 sample112 0.0426332600 -0.0029083494 sample113 -0.0771904885 0.0268733767 sample114 -0.0241640481 -0.0184080401 sample115 -0.1959014839 0.0460130937 sample116 -0.1394475505 -0.0530805632 sample117 -0.1672361151 -0.1386536190 sample118 -0.0448344121 -0.0117621878 sample119 -0.0910383911 0.2217433447 sample120 -0.0331392376 -0.0057274439 sample121 0.0307574107 0.1392506525 sample122 -0.0839781892 -0.0291994281 sample123 0.0239650738 -0.0642163770 sample124 -0.0909150958 0.0130419645 sample125 -0.0065350993 -0.1092631801 sample126 0.0935311334 0.1368283939 sample127 0.0035388129 0.0292755633 sample128 -0.0660296208 0.1018566443 sample129 0.0693639023 -0.0695421849 sample130 0.0008493758 -0.0669704335 sample131 0.0431023855 0.0174064811 sample132 -0.0637040751 0.0029374838 sample133 -0.0289494379 -0.0390818803 sample134 0.0446202217 0.0456334460 sample135 0.0712337056 0.0521634853 sample136 0.0596271618 0.0197299227 sample137 0.0793152283 -0.0380628425 sample138 -0.0973547778 -0.0454218138 sample139 0.0539904236 -0.1534327436 sample140 0.0850827583 0.0955814398 sample141 -0.0192682355 -0.0554450022 sample142 -0.0672262431 -0.0461320810 sample143 -0.0303730127 -0.0519260210 sample144 -0.0089364401 0.0145814925 sample145 -0.0638771347 0.0122258530 sample146 0.0585857176 0.0063083245 sample147 0.0894133523 -0.1124615839 sample148 -0.0216367892 -0.0615967072 sample149 -0.0515419348 -0.0839903463 sample150 0.0568282633 -0.0124468988 sample151 -0.0789532149 -0.0261831078 sample152 -0.0330752723 0.1306443613 sample153 -0.1751933048 0.1497732399 sample154 0.0421425182 -0.0037010279 sample155 0.0680177775 0.0095711126 sample156 0.0388911644 0.1057562897 sample157 0.0314769343 0.0561367383 sample158 0.0329620663 0.0353947272 sample159 -0.0398417142 -0.1007373703 sample160 0.0424938315 0.0108496126 sample161 -0.0888370915 -0.0679700061 sample162 -0.0027477178 0.1237843902 sample163 -0.0126106881 0.0725434411 sample164 -0.0566779758 -0.0458324097 sample165 -0.0315336331 -0.0236362308 sample166 -0.0612058797 -0.0425232930 sample167 0.0142729868 0.0179308258 sample168 -0.0169504000 -0.0769617861 sample169 0.0675080124 0.0131505237 > discoRes@scores$dist[[2]] ## Distinctive scores for Block 2 1 2 sample1 -0.0012329704 -1.635717e-01 sample2 -0.0724350148 -6.021283e-03 sample3 -0.0188460430 -1.080036e-01 sample4 0.0390145225 3.113938e-04 sample5 0.1774811612 -2.996385e-02 sample6 -0.0451444473 -3.455859e-02 sample7 -0.0226466179 -7.020136e-03 sample8 -0.1033680346 -9.856814e-03 sample9 0.1350011799 8.979099e-02 sample10 0.1259887178 -5.097855e-02 sample11 0.0979788431 7.086535e-02 sample12 -0.0863019148 -8.620317e-02 sample13 -0.1381401133 1.828007e-01 sample14 -0.0615073882 -2.642803e-02 sample15 0.0381598995 -3.101662e-02 sample16 -0.0048776795 1.271820e-03 sample17 -0.0788481005 -1.547555e-02 sample18 -0.0884188771 -3.795486e-02 sample19 0.0703044396 -1.084004e-01 sample20 -0.0025585420 7.975877e-02 sample21 0.0941601537 -4.126745e-02 sample22 -0.0550273349 -7.806740e-02 sample23 0.0679495243 -4.102007e-02 sample24 -0.1310962963 1.649309e-01 sample25 0.0113585235 -4.426864e-02 sample26 -0.1402945991 2.016541e-02 sample27 0.0261561250 1.588484e-03 sample28 -0.0724198770 5.850590e-02 sample29 -0.0330058477 2.060859e-03 sample30 -0.0228752500 -2.015428e-02 sample31 -0.0635068003 -6.670335e-02 sample32 0.0685099662 -4.955272e-02 sample33 -0.0777765207 -1.272078e-01 sample34 0.0157842407 -3.024314e-02 sample35 -0.0529632642 1.500972e-01 sample36 0.0070900939 2.025308e-01 sample37 -0.0442420378 1.802089e-01 sample38 -0.0781511227 -3.676417e-02 sample39 0.0120331814 -3.388842e-02 sample40 -0.0473291866 1.471562e-01 sample41 0.0228189477 -2.673552e-02 sample42 -0.0245360285 -7.960867e-02 sample43 0.1036362790 -8.229577e-02 sample44 -0.1012228950 7.049446e-02 sample45 0.0013731910 -2.450915e-02 sample46 -0.0558509931 2.947413e-03 sample47 -0.0380481118 4.554175e-02 sample48 0.0784342040 4.888978e-02 sample49 -0.0605164062 -1.162358e-02 sample50 0.0530079354 -2.737929e-02 sample51 0.1514646503 5.678343e-02 sample52 0.1860935254 1.246717e-01 sample53 -0.0064177156 -2.700995e-02 sample54 0.0697038325 -2.308389e-02 sample55 0.1633577049 1.366442e-02 sample56 0.1011485075 4.682203e-02 sample57 0.1730374217 1.609603e-01 sample58 -0.0071384710 -1.666955e-02 sample59 -0.0030461603 3.005288e-02 sample60 0.0215835309 2.665878e-01 sample61 0.1510583681 1.002385e-01 sample62 -0.0925533891 -4.845839e-02 sample63 -0.0596311878 -4.137025e-02 sample64 -0.0449225834 -2.600590e-03 sample65 0.0939383724 -4.406910e-02 sample66 0.1063400680 -5.709995e-02 sample67 -0.0201589791 2.361728e-01 sample68 0.0037203330 2.418393e-02 sample69 -0.0645161223 -1.155622e-01 sample70 -0.1013440006 -1.351789e-01 sample71 -0.0016467837 -2.976840e-02 sample72 0.0328893082 -2.835855e-02 sample73 0.0275080056 -5.148185e-02 sample74 0.1341719658 -7.895280e-02 sample75 0.0951575688 -3.943183e-02 sample76 -0.0864721916 3.034993e-02 sample77 -0.1035749565 -2.545353e-02 sample78 -0.1575644226 4.939591e-02 sample79 0.0189137102 4.874679e-02 sample80 0.1384140640 4.266657e-05 sample81 -0.0118846465 -6.357931e-02 sample82 -0.1675308133 3.533913e-02 sample83 -0.0065673349 -7.812607e-02 sample84 0.1486891587 -3.109058e-02 sample85 -0.0532724321 7.417887e-02 sample86 -0.1138477277 -1.913174e-05 sample87 0.0432864030 6.080473e-02 sample88 0.0433450387 1.402490e-01 sample89 0.0331205759 -1.395401e-02 sample90 -0.0607412829 -8.610413e-02 sample91 -0.0566272432 1.303748e-01 sample92 -0.0359582471 1.061604e-01 sample93 -0.0433646363 -4.443634e-02 sample94 -0.0477291319 -1.059574e-01 sample95 -0.0249595778 -3.980525e-02 sample96 0.0035218974 -9.293928e-02 sample97 -0.0066048829 -1.527231e-01 sample98 0.0020366824 -5.579549e-02 sample99 -0.0886616225 -3.728229e-02 sample100 -0.1091259145 -3.560419e-02 sample101 -0.0739726426 -4.317996e-02 sample102 0.0574461021 -2.783918e-02 sample103 0.0142731084 9.705578e-03 sample104 0.0710395216 4.068350e-02 sample105 0.0980831315 -3.452954e-02 sample106 -0.0254259339 3.628982e-02 sample107 -0.0160653483 -9.173395e-02 sample108 -0.0200987672 -2.379692e-02 sample109 -0.0389780693 1.692358e-02 sample110 -0.0326304856 2.988109e-02 sample111 0.0676937521 -6.038213e-02 sample112 0.0167883437 5.336937e-03 sample113 0.0969216944 -2.757606e-02 sample114 -0.0026398345 -9.209155e-02 sample115 -0.0308047379 1.603821e-02 sample116 -0.1240307164 1.273000e-01 sample117 0.0334729064 5.392709e-02 sample118 -0.1037152904 6.252431e-02 sample119 -0.1064176526 1.196203e-01 sample120 -0.0771355152 -1.004933e-01 sample121 -0.0129350787 3.181974e-02 sample122 0.0847492181 -5.568330e-02 sample123 -0.0041336748 7.693189e-03 sample124 -0.0583458100 -8.396391e-02 sample125 0.0634844571 -5.232541e-02 sample126 -0.0662580971 -1.091732e-01 sample127 -0.0865024628 -1.094176e-01 sample128 -0.0627817540 -1.470967e-02 sample129 -0.0336276404 -4.007857e-02 sample130 -0.0293517755 -8.046116e-02 sample131 -0.0469197644 -2.209741e-03 sample132 -0.0241740793 -1.248599e-01 sample133 0.0907303224 1.466700e-02 sample134 -0.0350842060 7.539662e-02 sample135 0.0001333442 9.185389e-03 sample136 -0.0335876029 -9.860271e-02 sample137 -0.0640148869 -7.554468e-02 sample138 0.0060964822 -1.742763e-02 sample139 -0.0592084420 5.614970e-02 sample140 0.0427985990 -1.099549e-02 sample141 0.0618796315 -9.301039e-02 sample142 0.0898554423 3.573415e-02 sample143 0.0817389255 8.880524e-02 sample144 0.0787754774 -3.821391e-02 sample145 0.1085821531 1.569476e-01 sample146 -0.0589557873 -4.373357e-02 sample147 -0.0495330380 7.277221e-03 sample148 0.1161592740 9.079060e-03 sample149 -0.0121579370 7.788376e-02 sample150 -0.0314512525 3.520213e-02 sample151 0.0575382141 -1.945353e-02 sample152 -0.0494542063 7.025538e-02 sample153 -0.0941332877 2.153296e-01 sample154 -0.0335931936 2.078731e-02 sample155 0.0690457684 -2.780409e-02 sample156 0.1039901637 -6.292524e-02 sample157 -0.0408645783 8.065515e-03 sample158 0.1018105331 7.816880e-03 sample159 -0.0281730584 -1.207207e-02 sample160 0.1643053008 2.978098e-03 sample161 0.0374329267 8.524610e-02 sample162 -0.0804535389 8.349752e-02 sample163 -0.0743228073 -1.406228e-02 sample164 0.1208805972 -2.139461e-02 sample165 0.1608115914 2.025191e-02 sample166 -0.0425944715 -2.660716e-02 sample167 -0.0226849482 -4.464281e-02 sample168 -0.0180735622 -7.466333e-04 sample169 0.0190779035 2.645403e-02 > # Exploring O2PLS scores structure > o2plsRes@scores$common[[1]] ## Common scores for Block 1 [,1] [,2] sample1 -0.0572060227 -1.729087e-02 sample2 0.0875245208 1.112588e-02 sample3 0.0403482602 -3.168994e-02 sample4 -0.0218345996 4.052760e-06 sample5 -0.0150905011 4.795041e-03 sample6 -0.0924362933 4.511003e-02 sample7 -0.0793066751 -1.243823e-02 sample8 -0.1342997187 6.215220e-02 sample9 -0.0338886944 -1.854401e-02 sample10 0.0020547173 1.749421e-02 sample11 0.0037275602 -2.364116e-02 sample12 -0.0753094533 2.772698e-02 sample13 0.0856160091 3.679963e-02 sample14 -0.0737457307 2.668452e-02 sample15 -0.0062111746 -3.554864e-03 sample16 -0.0602355268 6.675115e-02 sample17 0.1086768843 2.524534e-02 sample18 0.0702999472 2.231671e-02 sample19 0.0173785882 -3.024846e-02 sample20 0.0484173812 -3.310904e-02 sample21 0.0124657042 6.517144e-02 sample22 -0.0140989936 -3.159137e-02 sample23 -0.0627028403 -5.393710e-04 sample24 0.0919972100 7.909297e-02 sample25 0.0326998483 -1.945206e-02 sample26 0.1064741246 2.120849e-02 sample27 0.0166058995 -4.964993e-02 sample28 0.0743504770 2.614211e-02 sample29 -0.0511008491 -2.782647e-02 sample30 0.0962250842 -3.974893e-03 sample31 -0.0869563008 5.250819e-02 sample32 0.0271858919 1.552005e-02 sample33 -0.0448364581 6.243160e-03 sample34 0.0718415218 1.469396e-02 sample35 0.0403086451 -1.632629e-02 sample36 -0.1036402827 -1.304320e-02 sample37 -0.0159385744 -3.036525e-02 sample38 0.0182198369 -4.034805e-02 sample39 0.0690363619 8.058350e-03 sample40 -0.0467312750 -2.810325e-02 sample41 0.0263674438 -5.171216e-02 sample42 0.0374578960 -1.268634e-02 sample43 0.0132336869 9.536642e-03 sample44 -0.1119154428 5.028683e-02 sample45 0.0759639367 4.587903e-02 sample46 0.0871885519 -4.670385e-02 sample47 0.0721490571 -1.288540e-02 sample48 0.0005086144 -1.290565e-02 sample49 -0.0858177028 5.173760e-02 sample50 0.0118992665 -7.276215e-02 sample51 -0.0426446855 5.306205e-02 sample52 -0.0381605826 3.086785e-02 sample53 -0.0855757630 6.730043e-02 sample54 0.0261723092 9.184260e-03 sample55 -0.0156418304 4.682404e-04 sample56 0.0307831193 2.597550e-02 sample57 -0.0157242103 4.829381e-02 sample58 -0.0031174404 1.359898e-02 sample59 -0.0373001859 5.868397e-03 sample60 -0.0142609099 5.831654e-03 sample61 -0.0122255144 2.663579e-02 sample62 0.0228002942 -8.692265e-03 sample63 -0.0833127581 5.473229e-02 sample64 -0.1166548159 4.196500e-02 sample65 0.0038808902 8.568590e-03 sample66 0.0011561811 1.766612e-02 sample67 -0.1129311062 -2.608702e-02 sample68 -0.0382526429 -3.804045e-02 sample69 -0.0476502440 4.003241e-03 sample70 -0.0110329882 -2.752719e-02 sample71 0.0096850282 -5.627056e-02 sample72 0.0487124704 -8.800131e-03 sample73 0.0773058132 8.239864e-03 sample74 -0.0102488176 2.454957e-02 sample75 -0.0286613976 -8.387293e-03 sample76 -0.0472655595 -2.129315e-02 sample77 -0.0865043074 -7.296820e-03 sample78 0.1070293698 2.818346e-02 sample79 -0.0165060681 -6.659721e-02 sample80 -0.0206765949 -8.712112e-03 sample81 -0.0050943615 -3.079175e-02 sample82 0.1153622361 -1.647054e-02 sample83 0.0367979217 -2.538114e-03 sample84 0.0199463070 -1.468961e-02 sample85 -0.0827122185 -2.709824e-04 sample86 0.0969487314 -1.699897e-02 sample87 0.0421957457 -1.965953e-02 sample88 0.0215934743 1.566050e-02 sample89 0.0751559502 2.811652e-02 sample90 -0.0057328000 -8.283795e-03 sample91 -0.1134005268 -8.603522e-02 sample92 -0.0101689918 -6.894992e-02 sample93 0.0725967502 -6.003176e-03 sample94 -0.0096878852 -4.693081e-03 sample95 -0.0223502239 -3.139636e-02 sample96 -0.0013232863 -1.963604e-02 sample97 -0.0476541710 1.183660e-02 sample98 0.0269546160 -5.978398e-03 sample99 0.0728179461 4.597884e-02 sample100 -0.0413398038 1.079347e-02 sample101 0.0087536994 -6.796076e-02 sample102 0.0032509529 3.932612e-03 sample103 0.0360342395 -3.973263e-02 sample104 -0.0141722563 -2.453107e-02 sample105 0.0294940465 -7.140722e-03 sample106 0.0686472054 1.462895e-02 sample107 0.0748635927 8.401339e-03 sample108 0.0650175850 -6.211942e-03 sample109 -0.0628017242 -3.681224e-02 sample110 0.0905513691 -5.169053e-03 sample111 -0.0176679473 -3.884777e-02 sample112 0.0570870472 1.066018e-02 sample113 -0.0200110554 1.596044e-02 sample114 -0.0001474542 -3.679272e-02 sample115 -0.0213333038 -2.991667e-02 sample116 -0.0567675453 -2.785636e-02 sample117 -0.0379865990 -3.752078e-02 sample118 -0.0484878786 -9.173691e-03 sample119 -0.0713511831 -9.598634e-02 sample120 -0.0555093586 1.089843e-02 sample121 0.0542443861 3.861344e-02 sample122 0.0178575357 3.027138e-02 sample123 0.0775020581 -1.636852e-02 sample124 -0.0460701050 1.814758e-02 sample125 0.0543846585 2.075898e-03 sample126 -0.0729417144 3.276659e-02 sample127 -0.0609509157 -3.270814e-03 sample128 0.0908136899 3.758801e-02 sample129 0.0552445878 -1.879062e-02 sample130 0.0007128089 -1.294308e-02 sample131 -0.0693311345 7.357082e-03 sample132 -0.0556565156 3.126995e-02 sample133 0.0375870104 -1.977240e-02 sample134 -0.1229130924 3.159495e-02 sample135 0.0555550315 -5.563250e-04 sample136 -0.0159768414 -2.046339e-02 sample137 -0.0412337694 -1.151652e-02 sample138 -0.0180604476 -2.526505e-02 sample139 -0.0465649201 1.040683e-02 sample140 0.0452288969 -1.876279e-02 sample141 -0.0189142561 2.247042e-02 sample142 0.0297545566 1.280524e-02 sample143 0.0064292003 -1.997706e-02 sample144 -0.0124284903 -6.369733e-03 sample145 -0.0377141491 5.066743e-02 sample146 -0.0296240067 -3.344465e-02 sample147 0.0726083535 -1.239968e-02 sample148 -0.0284795794 3.389732e-02 sample149 0.0082261455 -6.399305e-02 sample150 -0.0765013197 2.704021e-02 sample151 -0.0220567356 -1.178159e-02 sample152 0.0403422737 -2.714879e-02 sample153 0.0629117719 7.425085e-02 sample154 0.0551622927 -3.548984e-02 sample155 0.0654439133 -1.005306e-02 sample156 0.0209310714 -1.390213e-02 sample157 0.0851522597 6.577150e-03 sample158 0.0208354599 -4.663078e-03 sample159 -0.0498794349 1.913257e-02 sample160 0.0216074437 1.656579e-02 sample161 -0.0075742328 -2.455676e-02 sample162 0.0963663017 5.705881e-02 sample163 -0.1009542191 7.174224e-02 sample164 0.0109881996 1.026806e-03 sample165 -0.0053146157 -6.772855e-03 sample166 -0.0275757357 2.673084e-02 sample167 -0.0825048036 2.278863e-03 sample168 0.0486147429 1.793843e-02 sample169 0.0302506727 8.984253e-03 > o2plsRes@scores$common[[2]] ## Common scores for Block 2 [,1] [,2] sample1 -0.0621842115 -1.364509e-02 sample2 0.0944623785 9.720892e-03 sample3 0.0406196267 -2.236338e-02 sample4 -0.0229316496 -3.932487e-04 sample5 -0.0157330047 3.231033e-03 sample6 -0.0945794025 3.120720e-02 sample7 -0.0854427118 -1.052880e-02 sample8 -0.1376625920 4.286608e-02 sample9 -0.0377115311 -1.415134e-02 sample10 0.0035244506 1.280825e-02 sample11 0.0016639987 -1.717895e-02 sample12 -0.0781403168 1.884368e-02 sample13 0.0938400516 2.838858e-02 sample14 -0.0759839772 1.810989e-02 sample15 -0.0068340837 -2.705361e-03 sample16 -0.0590150849 4.757848e-02 sample17 0.1178805097 2.040526e-02 sample18 0.0767858320 1.756604e-02 sample19 0.0157112113 -2.172867e-02 sample20 0.0485318300 -2.327033e-02 sample21 0.0185928176 4.777095e-02 sample22 -0.0191358702 -2.329775e-02 sample23 -0.0672994194 -1.535656e-03 sample24 0.1047476642 5.935707e-02 sample25 0.0329844953 -1.358036e-02 sample26 0.1154952052 1.741529e-02 sample27 0.0133849853 -3.590922e-02 sample28 0.0821554039 2.042376e-02 sample29 -0.0567643690 -2.123848e-02 sample30 0.1016073931 -1.134728e-03 sample31 -0.0880396372 3.670548e-02 sample32 0.0300363338 1.182406e-02 sample33 -0.0467252272 3.739254e-03 sample34 0.0783666394 1.203777e-02 sample35 0.0424227097 -1.118559e-02 sample36 -0.1107646166 -1.143464e-02 sample37 -0.0191667664 -2.246060e-02 sample38 0.0155968095 -2.909621e-02 sample39 0.0746847148 7.148218e-03 sample40 -0.0517028178 -2.137267e-02 sample41 0.0234979494 -3.723018e-02 sample42 0.0388797356 -8.557228e-03 sample43 0.0149555568 7.210002e-03 sample44 -0.1150305613 3.461805e-02 sample45 0.0846146236 3.486020e-02 sample46 0.0884426404 -3.246853e-02 sample47 0.0748644971 -8.083045e-03 sample48 -0.0012033198 -9.403647e-03 sample49 -0.0872662737 3.616245e-02 sample50 0.0066941314 -5.284863e-02 sample51 -0.0411777630 3.791830e-02 sample52 -0.0379355780 2.180834e-02 sample53 -0.0851639886 4.751761e-02 sample54 0.0288006248 7.184424e-03 sample55 -0.0164920835 5.919925e-05 sample56 0.0355115616 1.951043e-02 sample57 -0.0141146068 3.492409e-02 sample58 -0.0015636132 9.862883e-03 sample59 -0.0390656483 3.590929e-03 sample60 -0.0139454780 3.963030e-03 sample61 -0.0106410274 1.919705e-02 sample62 0.0236748439 -5.922677e-03 sample63 -0.0846790877 3.839102e-02 sample64 -0.1202581015 2.846469e-02 sample65 0.0050548584 6.328644e-03 sample66 0.0028013072 1.291807e-02 sample67 -0.1231623009 -2.112565e-02 sample68 -0.0437782161 -2.845072e-02 sample69 -0.0501199692 2.053469e-03 sample70 -0.0140278645 -2.027157e-02 sample71 0.0057489505 -4.085977e-02 sample72 0.0511212704 -5.522408e-03 sample73 0.0828141409 7.431582e-03 sample74 -0.0085959456 1.772951e-02 sample75 -0.0312180394 -6.636869e-03 sample76 -0.0519051781 -1.640191e-02 sample77 -0.0925924762 -6.907800e-03 sample78 0.1163971046 2.251122e-02 sample79 -0.0240906926 -4.887766e-02 sample80 -0.0221327065 -6.730703e-03 sample81 -0.0072114968 -2.254399e-02 sample82 0.1204416674 -9.907422e-03 sample83 0.0386739485 -1.171663e-03 sample84 0.0195988488 -1.033806e-02 sample85 -0.0877680171 -1.725057e-03 sample86 0.1023541048 -1.062501e-02 sample87 0.0425213089 -1.356865e-02 sample88 0.0244788514 1.180820e-02 sample89 0.0804276691 2.188588e-02 sample90 -0.0074639871 -6.140721e-03 sample91 -0.1278832404 -6.485140e-02 sample92 -0.0162199697 -5.048358e-02 sample93 0.0769344893 -3.045135e-03 sample94 -0.0104345587 -3.593172e-03 sample95 -0.0260058453 -2.330475e-02 sample96 -0.0025018700 -1.433516e-02 sample97 -0.0492358305 7.774183e-03 sample98 0.0279220220 -3.862141e-03 sample99 0.0813921923 3.487339e-02 sample100 -0.0428797405 7.112807e-03 sample101 0.0032855240 -4.940743e-02 sample102 0.0038439317 2.938008e-03 sample103 0.0358511139 -2.831881e-02 sample104 -0.0162784000 -1.815061e-02 sample105 0.0314853405 -4.656633e-03 sample106 0.0726456731 1.192390e-02 sample107 0.0807342975 7.508627e-03 sample108 0.0688338003 -3.336161e-03 sample109 -0.0694151950 -2.800146e-02 sample110 0.0961218924 -2.111997e-03 sample111 -0.0217900036 -2.864702e-02 sample112 0.0599954082 8.820317e-03 sample113 -0.0195006577 1.128215e-02 sample114 -0.0032126533 -2.682851e-02 sample115 -0.0251101087 -2.221077e-02 sample116 -0.0625141551 -2.137258e-02 sample117 -0.0440473375 -2.806256e-02 sample118 -0.0532042630 -7.590494e-03 sample119 -0.0848603028 -7.133574e-02 sample120 -0.0588832131 6.937326e-03 sample121 0.0613899126 2.915307e-02 sample122 0.0218424338 2.241775e-02 sample123 0.0809008460 -1.051759e-02 sample124 -0.0472109313 1.239887e-02 sample125 0.0583180947 2.521167e-03 sample126 -0.0753941872 2.256455e-02 sample127 -0.0649774209 -3.496964e-03 sample128 0.1000212216 2.908091e-02 sample129 0.0568033049 -1.269016e-02 sample130 -0.0002370832 -9.419675e-03 sample131 -0.0727030877 4.091672e-03 sample132 -0.0566219024 2.179861e-02 sample133 0.0384172955 -1.372840e-02 sample134 -0.1280862736 2.077912e-02 sample135 0.0592633273 6.106685e-04 sample136 -0.0187635410 -1.521173e-02 sample137 -0.0449958970 -9.152840e-03 sample138 -0.0211348699 -1.875415e-02 sample139 -0.0482882861 6.729304e-03 sample140 0.0468926306 -1.285498e-02 sample141 -0.0186248693 1.605439e-02 sample142 0.0328031246 9.887746e-03 sample143 0.0052919839 -1.445666e-02 sample144 -0.0140067923 -4.867248e-03 sample145 -0.0361804310 3.625323e-02 sample146 -0.0345286735 -2.493652e-02 sample147 0.0765025670 -7.714769e-03 sample148 -0.0276016641 2.420589e-02 sample149 0.0027545308 -4.653007e-02 sample150 -0.0792296010 1.831289e-02 sample151 -0.0245894512 -8.991738e-03 sample152 0.0409796547 -1.907063e-02 sample153 0.0734301757 5.528780e-02 sample154 0.0557740684 -2.487723e-02 sample155 0.0689436560 -6.127635e-03 sample156 0.0212272938 -9.747423e-03 sample157 0.0911931194 6.355708e-03 sample158 0.0220840645 -3.016357e-03 sample159 -0.0513244242 1.304175e-02 sample160 0.0246213576 1.248444e-02 sample161 -0.0100369130 -1.805391e-02 sample162 0.1078802043 4.337260e-02 sample163 -0.1017965082 5.047171e-02 sample164 0.0119430799 9.593002e-04 sample165 -0.0063708014 -5.032148e-03 sample166 -0.0283181180 1.899222e-02 sample167 -0.0872832229 1.516582e-04 sample168 0.0540714512 1.397701e-02 sample169 0.0328432652 7.104347e-03 > o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1 [,1] [,2] sample1 0.0133684846 2.195848e-02 sample2 0.0254157197 -1.058416e-02 sample3 -0.0049551479 -4.840017e-03 sample4 0.0310390570 -1.063929e-02 sample5 0.0046941318 -6.488426e-03 sample6 -0.0107406753 -1.026702e-02 sample7 -0.0225157631 2.624712e-04 sample8 0.0141320952 -9.505821e-03 sample9 0.0029681280 2.078210e-02 sample10 0.0131729174 -2.275042e-03 sample11 -0.0004164298 1.994019e-02 sample12 -0.0095211620 3.759883e-02 sample13 0.0091018604 -7.953956e-03 sample14 -0.0106557524 -9.181659e-03 sample15 -0.0249924121 3.262724e-02 sample16 -0.0156216400 1.375700e-02 sample17 -0.0019382446 1.073994e-03 sample18 -0.0221072481 -8.703592e-03 sample19 0.0146917619 -1.311712e-02 sample20 -0.0160353760 1.826290e-02 sample21 0.0035947899 -9.616341e-03 sample22 -0.0225060762 -2.532589e-03 sample23 0.0310000683 3.033060e-03 sample24 0.0499544372 1.809450e-02 sample25 0.0284442301 -1.932558e-02 sample26 0.0188220043 2.146985e-02 sample27 -0.0257763219 -1.999228e-03 sample28 0.0120888648 1.125834e-02 sample29 -0.0236482520 4.426726e-02 sample30 -0.0385486305 -2.055935e-02 sample31 -0.0181539336 -5.877838e-03 sample32 -0.0302630460 -2.607192e-03 sample33 -0.0319565715 -1.562628e-02 sample34 -0.0197970124 9.906813e-03 sample35 -0.0247412713 -5.434440e-03 sample36 -0.0386259060 -3.190394e-02 sample37 -0.0566199273 -4.192574e-02 sample38 -0.0142060273 2.259644e-02 sample39 0.0053589035 1.076485e-02 sample40 -0.0552546493 -3.819896e-02 sample41 -0.0013089975 9.278818e-05 sample42 0.0137252142 -1.664652e-02 sample43 -0.0151259626 -6.290953e-03 sample44 0.0617391754 -1.442883e-02 sample45 0.0231410886 1.163143e-03 sample46 -0.0148898209 -1.384176e-04 sample47 -0.0187252536 1.221690e-02 sample48 0.0432839432 1.416671e-02 sample49 0.0160818605 -3.588745e-02 sample50 0.0059333545 4.067003e-02 sample51 -0.0142914866 7.776270e-03 sample52 -0.0086339952 7.208917e-03 sample53 -0.0207386980 6.272432e-03 sample54 -0.0039856719 -1.316934e-02 sample55 -0.0056217017 5.692315e-03 sample56 0.0000123292 8.978290e-04 sample57 -0.0095805555 1.324253e-02 sample58 -0.0124160295 -7.326376e-03 sample59 -0.0400195442 -1.349736e-02 sample60 -0.0460063358 2.770091e-02 sample61 -0.0245266456 1.470710e-02 sample62 -0.0366022783 -3.437352e-03 sample63 0.0013742171 3.288796e-02 sample64 -0.0070599859 2.739588e-02 sample65 0.0041201911 1.498268e-02 sample66 0.0143173351 -1.968812e-02 sample67 -0.0467477531 -1.929938e-02 sample68 -0.0306751978 -1.436184e-02 sample69 -0.0125317217 4.130407e-03 sample70 -0.0068071487 8.080857e-03 sample71 0.0169170264 -7.027348e-03 sample72 -0.0346909749 -1.333770e-02 sample73 -0.0280506153 1.493843e-02 sample74 -0.0182611498 3.294697e-03 sample75 -0.0120563964 8.974612e-03 sample76 0.0001437236 -4.253184e-02 sample77 0.0065330299 -5.252886e-02 sample78 0.0288278141 -1.127782e-02 sample79 0.0503961481 -1.023318e-02 sample80 -0.0207693429 3.648391e-02 sample81 0.0163562768 -9.074596e-03 sample82 -0.0084317129 -1.478976e-02 sample83 -0.0474097918 -1.103126e-02 sample84 0.0177181395 -7.191197e-03 sample85 -0.0342718548 -3.082360e-02 sample86 -0.0261671791 -1.089491e-02 sample87 -0.0009486358 -2.411514e-02 sample88 0.0020528931 -2.894615e-02 sample89 -0.0189361111 -2.638639e-03 sample90 -0.0009863658 -2.390075e-02 sample91 -0.0124352695 8.153234e-02 sample92 0.0564264106 -8.909537e-03 sample93 -0.0081461774 1.570851e-02 sample94 -0.0054896581 1.547251e-02 sample95 0.0224073150 -4.374348e-04 sample96 0.0173528924 -3.050441e-03 sample97 0.0067948115 5.008237e-03 sample98 -0.0116030825 1.498764e-02 sample99 0.0246422688 -4.054795e-03 sample100 -0.0069420745 -4.846343e-04 sample101 0.0124923691 3.091503e-02 sample102 0.0650835386 -1.367400e-02 sample103 -0.0042741828 7.855985e-03 sample104 0.0250591040 -4.171938e-03 sample105 0.0157516368 -3.121990e-02 sample106 0.0060593853 -5.101693e-03 sample107 -0.0098329626 1.044506e-02 sample108 0.0044269853 4.142036e-03 sample109 0.0572473486 1.517542e-02 sample110 0.0090474827 -5.119868e-03 sample111 0.0444263015 7.983232e-03 sample112 -0.0131765484 -9.696342e-04 sample113 0.0241047399 6.706740e-03 sample114 0.0074558775 -4.728652e-03 sample115 0.0611851433 1.117210e-02 sample116 0.0432646951 -1.380556e-02 sample117 0.0516750066 -3.575617e-02 sample118 0.0139942100 -3.279138e-03 sample119 0.0291722987 5.587946e-02 sample120 0.0103515853 -1.690016e-03 sample121 -0.0091396331 3.552116e-02 sample122 0.0260431679 -7.583975e-03 sample123 -0.0076666389 -1.628489e-02 sample124 0.0283466326 3.127845e-03 sample125 0.0016472378 -2.770692e-02 sample126 -0.0286529417 3.489336e-02 sample127 -0.0010224500 7.483214e-03 sample128 0.0209049296 2.572016e-02 sample129 -0.0218184878 -1.755347e-02 sample130 -0.0005009620 -1.697978e-02 sample131 -0.0134032968 4.637390e-03 sample132 0.0198526786 5.723983e-04 sample133 0.0088812957 -9.988115e-03 sample134 -0.0137484514 1.172591e-02 sample135 -0.0220314568 1.347465e-02 sample136 -0.0185173353 5.168079e-03 sample137 -0.0248352123 -9.472788e-03 sample138 0.0301635767 -1.175283e-02 sample139 -0.0173576929 -3.872592e-02 sample140 -0.0262157762 2.456863e-02 sample141 0.0058369763 -1.420854e-02 sample142 0.0207886071 -1.188764e-02 sample143 0.0092832598 -1.324238e-02 sample144 0.0028442140 3.627979e-03 sample145 0.0199749569 2.862202e-03 sample146 -0.0182236697 1.726556e-03 sample147 -0.0282519995 -2.825595e-02 sample148 0.0065435868 -1.572917e-02 sample149 0.0158233820 -2.159451e-02 sample150 -0.0177383738 -3.020633e-03 sample151 0.0245166984 -6.888241e-03 sample152 0.0107259913 3.314630e-02 sample153 0.0550963965 3.758760e-02 sample154 -0.0131452472 -8.153903e-04 sample155 -0.0211742574 2.642246e-03 sample156 -0.0117803505 2.698265e-02 sample157 -0.0096167165 1.433840e-02 sample158 -0.0101754772 9.137620e-03 sample159 0.0120662931 -2.565236e-02 sample160 -0.0132238202 2.916023e-03 sample161 0.0274491966 -1.748284e-02 sample162 0.0012482909 3.152261e-02 sample163 0.0042031315 1.830701e-02 sample164 0.0174896157 -1.175915e-02 sample165 0.0097517662 -6.119019e-03 sample166 0.0190134679 -1.121582e-02 sample167 -0.0044140836 4.665585e-03 sample168 0.0049689168 -1.941822e-02 sample169 -0.0209802098 3.498729e-03 > o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2 [,1] [,2] sample1 -0.0515543627 -0.0305856787 sample2 -0.0144993256 0.0236342950 sample3 -0.0371833108 -0.0140263348 sample4 0.0068945388 -0.0132539692 sample5 0.0215035333 -0.0663338101 sample6 -0.0187055152 0.0088773016 sample7 -0.0061521552 0.0064029054 sample8 -0.0210874459 0.0334652901 sample9 0.0516865043 -0.0291142799 sample10 0.0059440366 -0.0527217447 sample11 0.0393010793 -0.0200624712 sample12 -0.0420837100 0.0131331362 sample13 0.0333252565 0.0818552509 sample14 -0.0190062644 0.0160202175 sample15 -0.0030968049 -0.0189230681 sample16 -0.0004452158 0.0018880102 sample17 -0.0185848615 0.0240170131 sample18 -0.0273093598 0.0230213640 sample19 -0.0217761111 -0.0445894441 sample20 0.0245820821 0.0159812738 sample21 0.0034527644 -0.0400016054 sample22 -0.0340789054 0.0039289109 sample23 -0.0010344929 -0.0310161212 sample24 0.0289468503 0.0760962436 sample25 -0.0119098496 -0.0122798760 sample26 -0.0181001057 0.0517892852 sample27 0.0050465417 -0.0086515844 sample28 0.0057491502 0.0358830107 sample29 -0.0051104246 0.0116605117 sample30 -0.0103085904 0.0039678538 sample31 -0.0319929858 0.0090606113 sample32 -0.0036232521 -0.0328202010 sample33 -0.0534742153 0.0024751837 sample34 -0.0067495749 -0.0111000311 sample35 0.0378745721 0.0465929296 sample36 0.0647886800 0.0359987924 sample37 0.0488441236 0.0492906912 sample38 -0.0251514062 0.0197110110 sample39 -0.0085428066 -0.0105117852 sample40 0.0379324087 0.0440810741 sample41 -0.0044199152 -0.0128820644 sample42 -0.0292553573 -0.0067045265 sample43 -0.0077829155 -0.0510178219 sample44 0.0045122248 0.0479660309 sample45 -0.0074444298 -0.0051116726 sample46 -0.0088025512 0.0196186661 sample47 0.0076696301 0.0215947965 sample48 0.0290108585 -0.0175568376 sample49 -0.0141754858 0.0184717099 sample50 0.0006282201 -0.0233054373 sample51 0.0441995177 -0.0410022921 sample52 0.0715329391 -0.0399499475 sample53 -0.0095954087 -0.0029140909 sample54 0.0048933768 -0.0281884386 sample55 0.0327325487 -0.0532290012 sample56 0.0323068984 -0.0256595538 sample57 0.0806603122 -0.0286748097 sample58 -0.0064792049 -0.0006945349 sample59 0.0088958941 0.0067389649 sample60 0.0874124612 0.0431964341 sample61 0.0577604571 -0.0326112099 sample62 -0.0313318464 0.0224391756 sample63 -0.0233625220 0.0125110562 sample64 -0.0086426068 0.0148770341 sample65 0.0025256193 -0.0404466327 sample66 0.0006014071 -0.0471576264 sample67 0.0706087042 0.0516228406 sample68 0.0082301011 0.0033109509 sample69 -0.0475076743 0.0001452708 sample70 -0.0600773716 0.0089986962 sample71 -0.0096321627 -0.0050761187 sample72 -0.0031773546 -0.0166221542 sample73 -0.0113700517 -0.0191726684 sample74 -0.0014179662 -0.0608101325 sample75 0.0041911740 -0.0399981269 sample76 -0.0055326449 0.0353114263 sample77 -0.0260214459 0.0305731380 sample78 -0.0119267436 0.0632236007 sample79 0.0186017239 0.0027402910 sample80 0.0241047889 -0.0472697181 sample81 -0.0220288317 -0.0079577210 sample82 -0.0180751258 0.0639051029 sample83 -0.0256671713 -0.0125898269 sample84 0.0161392598 -0.0567222449 sample85 0.0139988188 0.0322763454 sample86 -0.0198382995 0.0389225776 sample87 0.0266270281 -0.0032979996 sample88 0.0515677078 0.0117902495 sample89 0.0014022125 -0.0140510488 sample90 -0.0375949749 0.0044004551 sample91 0.0310397965 0.0440610926 sample92 0.0270570567 0.0324380452 sample93 -0.0215009202 0.0063993941 sample94 -0.0415702912 -0.0037692077 sample95 -0.0168416047 0.0010019120 sample96 -0.0285582661 -0.0187991000 sample97 -0.0490843868 -0.0266760748 sample98 -0.0171579033 -0.0112897471 sample99 -0.0271316525 0.0232395583 sample100 -0.0301789816 0.0305498693 sample101 -0.0264371151 0.0170723968 sample102 0.0012767734 -0.0248949597 sample103 0.0055214687 -0.0030040587 sample104 0.0251346074 -0.0165212671 sample105 0.0062424215 -0.0400309901 sample106 0.0069768684 0.0154982315 sample107 -0.0315912602 -0.0118883820 sample108 -0.0109690679 0.0023637162 sample109 -0.0014762845 0.0165583675 sample110 0.0036971063 0.0168260726 sample111 -0.0071624739 -0.0345651461 sample112 0.0046098120 -0.0048009350 sample113 0.0082236008 -0.0383233357 sample114 -0.0293642209 -0.0165595240 sample115 -0.0003260453 0.0135805368 sample116 0.0183575759 0.0665377581 sample117 0.0227640036 -0.0012287760 sample118 0.0015695248 0.0472617382 sample119 0.0190084932 0.0590034062 sample120 -0.0449645755 0.0072755697 sample121 0.0077307184 0.0104738937 sample122 -0.0027132063 -0.0394983138 sample123 0.0016959300 0.0028593594 sample124 -0.0365091615 0.0040382925 sample125 -0.0053658663 -0.0316029164 sample126 -0.0458032408 0.0019165544 sample127 -0.0494064872 0.0088209044 sample128 -0.0155454766 0.0186819802 sample129 -0.0184340400 0.0038684312 sample130 -0.0303640987 -0.0052225766 sample131 -0.0088697422 0.0156339713 sample132 -0.0433916471 -0.0154075483 sample133 0.0204029276 -0.0282209049 sample134 0.0175513332 0.0262883962 sample135 0.0029009925 0.0017003151 sample136 -0.0367997573 -0.0072249751 sample137 -0.0348600323 0.0075400273 sample138 -0.0044063824 -0.0053752428 sample139 0.0073103935 0.0308956174 sample140 0.0039925654 -0.0167019605 sample141 -0.0184093462 -0.0387953445 sample142 0.0268670676 -0.0239229634 sample143 0.0421049126 -0.0110888235 sample144 0.0017253664 -0.0341766012 sample145 0.0681741320 -0.0073526377 sample146 -0.0239965222 0.0118396767 sample147 -0.0063453522 0.0183130585 sample148 0.0230825251 -0.0379753037 sample149 0.0223298673 0.0188909118 sample150 0.0055709108 0.0174179009 sample151 0.0039177786 -0.0233533275 sample152 0.0134325667 0.0302344591 sample153 0.0511990309 0.0730230140 sample154 0.0006698324 0.0154177486 sample155 0.0032926626 -0.0288651601 sample156 -0.0016463495 -0.0474657733 sample157 -0.0045857599 0.0154934573 sample158 0.0201775524 -0.0332982124 sample159 -0.0086909001 0.0073496711 sample160 0.0295437331 -0.0555734536 sample161 0.0332754288 0.0033779619 sample162 0.0121954537 0.0433540412 sample163 -0.0173490933 0.0227219128 sample164 0.0143374783 -0.0453542590 sample165 0.0343612593 -0.0511194536 sample166 -0.0157536004 0.0094621170 sample167 -0.0179654624 -0.0006982358 sample168 -0.0033829919 0.0060747155 sample169 0.0116231468 -0.0015112800 > > ## 3.3 Plotting VAF > > # DISCO-SCA plotVAF > plotVAF(discoRes) > > # JIVE plotVAF > plotVAF(jiveRes) > > > ######################### > ## PART 4. Plot Results > > # Scores for common part. DISCO-SCA > plotRes(object=discoRes,comps=c(1,2),what="scores",type="common", + combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > > # 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 11.675 0.284 11.951
STATegRa.Rcheck/STATegRa-Ex.timings
name | user | system | elapsed | |
STATegRaUsersGuide | 0.001 | 0.000 | 0.001 | |
STATegRa_data | 0.232 | 0.000 | 0.232 | |
STATegRa_data_TCGA_BRCA | 0.002 | 0.000 | 0.002 | |
bioDist | 0.396 | 0.040 | 0.436 | |
bioDistFeature | 0.308 | 0.008 | 0.316 | |
bioDistFeaturePlot | 0.307 | 0.048 | 0.355 | |
bioDistW | 0.305 | 0.022 | 0.328 | |
bioDistWPlot | 0.308 | 0.012 | 0.321 | |
bioMap | 0.003 | 0.000 | 0.003 | |
combiningMappings | 0.011 | 0.000 | 0.012 | |
createOmicsExpressionSet | 0.138 | 0.004 | 0.142 | |
getInitialData | 0.600 | 0.008 | 0.608 | |
getLoadings | 0.622 | 0.036 | 0.659 | |
getMethodInfo | 0.688 | 0.024 | 0.711 | |
getPreprocessing | 0.780 | 0.640 | 1.419 | |
getScores | 0.746 | 0.056 | 0.802 | |
getVAF | 0.578 | 0.000 | 0.577 | |
holistOmics | 0.003 | 0.000 | 0.002 | |
modelSelection | 1.383 | 1.700 | 3.082 | |
omicsCompAnalysis | 4.09 | 0.10 | 4.19 | |
omicsNPC | 0.002 | 0.000 | 0.003 | |
plotRes | 4.576 | 0.076 | 4.653 | |
plotVAF | 4.401 | 0.028 | 4.429 | |