Back to Multiple platform build/check report for BioC 3.19: simplified long |
|
This page was generated on 2024-08-27 17:43 -0400 (Tue, 27 Aug 2024).
Hostname | OS | Arch (*) | R version | Installed pkgs |
---|---|---|---|---|
nebbiolo1 | Linux (Ubuntu 22.04.3 LTS) | x86_64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4757 |
palomino7 | Windows Server 2022 Datacenter | x64 | 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" | 4494 |
merida1 | macOS 12.7.5 Monterey | x86_64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4523 |
kjohnson1 | macOS 13.6.6 Ventura | arm64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4472 |
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 | |||||||||
palomino7 | Windows Server 2022 Datacenter / x64 | OK | OK | OK | OK | |||||||||
merida1 | macOS 12.7.5 Monterey / x86_64 | OK | OK | OK | OK | |||||||||
kjohnson1 | macOS 13.6.6 Ventura / arm64 | OK | OK | OK | NA | |||||||||
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: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings STATegRa_1.40.0.tar.gz |
StartedAt: 2024-08-26 12:57:33 -0400 (Mon, 26 Aug 2024) |
EndedAt: 2024-08-26 13:05:32 -0400 (Mon, 26 Aug 2024) |
EllapsedTime: 479.5 seconds |
RetCode: 0 |
Status: OK |
CheckDir: STATegRa.Rcheck |
Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings STATegRa_1.40.0.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/Users/biocbuild/bbs-3.19-bioc/meat/STATegRa.Rcheck’ * using R version 4.4.1 (2024-06-14) * using platform: x86_64-apple-darwin20 * R was compiled by Apple clang version 14.0.0 (clang-1400.0.29.202) GNU Fortran (GCC) 12.2.0 * running under: macOS Monterey 12.7.5 * using session charset: UTF-8 * using option ‘--no-vignettes’ * checking for file ‘STATegRa/DESCRIPTION’ ... OK * checking extension type ... Package * this is package ‘STATegRa’ version ‘1.40.0’ * 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 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 (user + system) or elapsed time > 5s user system elapsed plotRes 8.624 0.231 10.417 modelSelection 3.673 4.238 9.245 plotVAF 7.618 0.214 9.138 omicsCompAnalysis 6.677 0.222 8.062 * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘STATEgRa_Example.omicsCLUST.R’ Running ‘STATEgRa_Example.omicsPCA.R’ Running ‘STATegRa_Example.omicsNPC.R’ Running ‘runTests.R’ OK * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking running R code from vignettes ... SKIPPED * checking re-building of vignette outputs ... SKIPPED * checking PDF version of manual ... OK * DONE Status: 1 NOTE See ‘/Users/biocbuild/bbs-3.19-bioc/meat/STATegRa.Rcheck/00check.log’ for details.
STATegRa.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL STATegRa ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/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 4.4.1 (2024-06-14) -- "Race for Your Life" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > BiocGenerics:::testPackage("STATegRa") Common components [1] 2 Distinctive components [[1]] [1] 0 [[2]] [1] 0 Common components [1] 2 Distinctive components [[1]] [1] 1 [[2]] [1] 1 Common components [1] 2 Distinctive components [[1]] [1] 2 [[2]] [1] 2 RUNIT TEST PROTOCOL -- Mon Aug 26 13:05:17 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 6.317 0.486 7.757
STATegRa.Rcheck/tests/STATEgRa_Example.omicsCLUST.Rout
R version 4.4.1 (2024-06-14) -- "Race for Your Life" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ########################################### > ########### EXAMPLE OF THE OMICSCLUSTERING > ########################################### > require(STATegRa) Loading required package: STATegRa > > ############################################# > ## PART 1: CREATING a bioMap CLASS > ############################################# > ####### This part creates or reads the map between features. > ####### In the present example the map is downloaded from a resource. > ####### then the class is created. > > #load("../data/STATegRa_S2.rda") > data(STATegRa_S2) > > MAP.SYMBOL<-bioMap(name = "Symbol-miRNA", + metadata = list(type_v1="Gene",type_v2="miRNA", + source_database="targetscan.Hs.eg.db", + data_extraction="July2014"), + map=mapdata) > > > ############################################# > ## PART 2: CREATING a bioDist CLASS > ############################################# > ##### In the second part given a set of main features and surrogate feautres, > ##### the profile of the main features is computed through the surrogate features. > > # Load Data > data(STATegRa_S1) > #load("../data/STATegRa.S1.Rdata") > > ## Create ExpressionSets > # source("../R/STATegRa_omicsPCA_classes_and_methods.R") > # Block1 - Expression data > mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname")) > # Block2 - miRNA expression data > miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname")) > > # Create Gene-gene distance computed through miRNA data > bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1), + reference = "Var1", + mapping = MAP.SYMBOL, + surrogateData = miRNA.ds, ### miRNA data + referenceData = mRNA.ds, ### mRNA data + maxitems=2, + selectionRule="sd", + expfac=NULL, + aggregation = "sum", + distance = "spearman", + noMappingDist = 0, + filtering = NULL, + name = "mRNAbymiRNA") > > require(Biobase) Loading required package: Biobase Loading required package: BiocGenerics 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 40.765 1.245 48.900
STATegRa.Rcheck/tests/STATegRa_Example.omicsNPC.Rout
R version 4.4.1 (2024-06-14) -- "Race for Your Life" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > rm(list = ls()) > require("STATegRa") Loading required package: STATegRa > # Load the data > data("TCGA_BRCA_Batch_93") > # Setting dataTypes > dataTypes <- c("count", "count", "continuous") > # Setting methods to combine pvalues > combMethods = c("Fisher", "Liptak", "Tippett") > # Setting number of permutations > numPerms = 1000 > # Setting number of cores > numCores = 1 > # Setting holistOmics to print out the steps that it performs. > verbose = TRUE > # Run holistOmics analysis. > output <- omicsNPC(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes, combMethods = combMethods, numPerms = numPerms, numCores = numCores, verbose = verbose) 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 168.128 2.396 198.891
STATegRa.Rcheck/tests/STATEgRa_Example.omicsPCA.Rout
R version 4.4.1 (2024-06-14) -- "Race for Your Life" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ########################################### > ########### EXAMPLE OF THE 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 0.0781574359 -0.0431503286 sample2 -0.1192218484 0.0294086903 sample3 -0.0531412024 -0.0746839769 sample4 0.0292975070 -0.0005961838 sample5 0.0202091749 0.0110463446 sample6 0.1226089039 0.1053467758 sample7 0.1078928190 -0.0322474346 sample8 0.1782895190 0.1449363201 sample9 0.0468698109 -0.0455174292 sample10 -0.0036030556 0.0420110144 sample11 -0.0035566464 -0.0566292445 sample12 0.1006128924 0.0641381135 sample13 -0.1174408475 0.0907488112 sample14 0.0981203264 0.0617738788 sample15 0.0085334388 -0.0087012095 sample16 0.0783148613 0.1581295446 sample17 -0.1483609943 0.0638581915 sample18 -0.0963086211 0.0556641380 sample19 -0.0217244056 -0.0720087259 sample20 -0.0635636347 -0.0779652069 sample21 -0.0201840424 0.1566391088 sample22 0.0218268859 -0.0764103198 sample23 0.0852041952 -0.0032690704 sample24 -0.1287170923 0.1924540720 sample25 -0.0430574183 -0.0456567929 sample26 -0.1453896923 0.0541510887 sample27 -0.0197488678 -0.1185655249 sample28 -0.1025336390 0.0650684819 sample29 0.0706018587 -0.0682986736 sample30 -0.1295627419 -0.0066767047 sample31 0.1147449125 0.1232687647 sample32 -0.0374310803 0.0380179698 sample33 0.0599516111 0.0136868919 sample34 -0.0984200777 0.0375322127 sample35 -0.0543098333 -0.0378104167 sample36 0.1403625498 -0.0343753258 sample37 0.0228942011 -0.0732842427 sample38 -0.0222077154 -0.0962594115 sample39 -0.0941738507 0.0215198731 sample40 0.0643801289 -0.0687867278 sample41 -0.0327637946 -0.1232188147 sample42 -0.0500431831 -0.0292474335 sample43 -0.0184498772 0.0233011849 sample44 0.1487898539 0.1171350828 sample45 -0.1050774288 0.1123200056 sample46 -0.1151195621 -0.1094027941 sample47 -0.0962593679 -0.0288462610 sample48 0.0004837215 -0.0310280344 sample49 0.1135207687 0.1213972118 sample50 -0.0123553041 -0.1740744175 sample51 0.0550529817 0.1258887712 sample52 0.0499121168 0.0728545352 sample53 0.1119773631 0.1588015067 sample54 -0.0360055671 0.0228575811 sample55 0.0210419002 0.0006732124 sample56 -0.0434169281 0.0633126149 sample57 0.0197824523 0.1150714483 sample58 0.0030439911 0.0326098695 sample59 0.0500253205 0.0129421249 sample60 0.0184278684 0.0136088405 sample61 0.0150299408 0.0635027429 sample62 -0.0304763779 -0.0201317341 sample63 0.1102252417 0.1285976783 sample64 0.1552588055 0.0971168755 sample65 -0.0058503066 0.0207115133 sample66 -0.0025605389 0.0424319101 sample67 0.1546634902 -0.0661713299 sample68 0.0536369390 -0.0923681931 sample69 0.0640330431 0.0081983567 sample70 0.0163517836 -0.0663230005 sample71 -0.0102537594 -0.1345922357 sample72 -0.0654195941 -0.0196117700 sample73 -0.1048556068 0.0220939624 sample74 0.0123799513 0.0586116000 sample75 0.0392077987 -0.0209754435 sample76 0.0648953402 -0.0524764279 sample77 0.1172922134 -0.0201186991 sample78 -0.1463068208 0.0708470874 sample79 0.0265211140 -0.1603310513 sample80 0.0279737208 -0.0214204023 sample81 0.0079211510 -0.0738451823 sample82 -0.1544236477 -0.0361467492 sample83 -0.0494211262 -0.0050046150 sample84 -0.0259038489 -0.0346550527 sample85 0.1116484416 -0.0031495714 sample86 -0.1306482953 -0.0377213899 sample87 -0.0554778192 -0.0459748751 sample88 -0.0301623907 0.0382197746 sample89 -0.1016866701 0.0694034750 sample90 0.0086819914 -0.0201320188 sample91 0.1578625428 -0.2097827230 sample92 0.0170936754 -0.1655809634 sample93 -0.0979806784 -0.0121511989 sample94 0.0131484142 -0.0114932023 sample95 0.0315682624 -0.0758860303 sample96 0.0024125623 -0.0470136594 sample97 0.0634545425 0.0270331314 sample98 -0.0359374592 -0.0135487995 sample99 -0.1009163420 0.1124778686 sample100 0.0551753145 0.0246489848 sample101 -0.0080118836 -0.1627369285 sample102 -0.0046444452 0.0095628747 sample103 -0.0472523129 -0.0940393107 sample104 0.0198159445 -0.0591092712 sample105 -0.0400237816 -0.0160912769 sample106 -0.0923808455 0.0369017456 sample107 -0.1019373913 0.0224954416 sample108 -0.0877091651 -0.0128834503 sample109 0.0864824299 -0.0900944428 sample110 -0.1223115561 -0.0096086152 sample111 0.0257354593 -0.0936171326 sample112 -0.0765286590 0.0270348175 sample113 0.0258803179 0.0377496071 sample114 0.0021138967 -0.0882015381 sample115 0.0303460101 -0.0723588534 sample116 0.0780508336 -0.0685068155 sample117 0.0536898026 -0.0911910658 sample118 0.0666651119 -0.0236231598 sample119 0.1021871643 -0.2324938022 sample120 0.0750216549 0.0243378513 sample121 -0.0756936430 0.0942950983 sample122 -0.0259628159 0.0731985913 sample123 -0.1037846226 -0.0369196864 sample124 0.0611207869 0.0421722218 sample125 -0.0738472711 0.0066949993 sample126 0.0972916494 0.0762641146 sample127 0.0824697666 -0.0096637424 sample128 -0.1249407727 0.0929311591 sample129 -0.0734067443 -0.0434361984 sample130 -0.0003501970 -0.0309852755 sample131 0.0930182835 0.0155937768 sample132 0.0736222770 0.0733028740 sample133 -0.0498397982 -0.0462437837 sample134 0.1644873470 0.0720006411 sample135 -0.0752297158 0.0003818731 sample136 0.0227145856 -0.0495505331 sample137 0.0564717493 -0.0288914701 sample138 0.0255988079 -0.0610858410 sample139 0.0621217817 0.0235808636 sample140 -0.0604152464 -0.0435592113 sample141 0.0246743955 0.0532648325 sample142 -0.0409560396 0.0316278983 sample143 -0.0077355243 -0.0476896534 sample144 0.0173240833 -0.0156778108 sample145 0.0485474386 0.1202769976 sample146 0.0419645717 -0.0811280525 sample147 -0.0977308282 -0.0274838755 sample148 0.0368256133 0.0803979450 sample149 -0.0072865777 -0.1532986590 sample150 0.1020825290 0.0624775542 sample151 0.0305399030 -0.0289279311 sample152 -0.0533594811 -0.0638309569 sample153 -0.0891627894 0.1799576048 sample154 -0.0727557414 -0.0834160305 sample155 -0.0880668507 -0.0220818676 sample156 -0.0276560999 -0.0326624890 sample157 -0.1155032179 0.0183616464 sample158 -0.0281507501 -0.0104938156 sample159 0.0663235670 0.0443836819 sample160 -0.0302643885 0.0404266114 sample161 0.0114715515 -0.0591026552 sample162 -0.1337087207 0.1398135442 sample163 0.1330124402 0.1688781077 sample164 -0.0150336129 0.0028415387 sample165 0.0076520252 -0.0164128754 sample166 0.0367794313 0.0630661154 sample167 0.1111988884 0.0030058022 sample168 -0.0672981630 0.0446279075 sample169 -0.0413004946 0.0224395261 > discoRes@scores$dist[[1]] ## Distinctive scores for Block 1 1 2 sample1 0.0420513777 0.0867863154 sample2 0.0820828997 -0.0410977855 sample3 -0.0155901055 -0.0195182443 sample4 0.1001337219 -0.0410786481 sample5 0.0153466225 -0.0253259642 sample6 -0.0340324200 -0.0408223205 sample7 -0.0722580193 0.0002332078 sample8 0.0457502222 -0.0370016052 sample9 0.0086248665 0.0820184898 sample10 0.0423599219 -0.0083923173 sample11 -0.0022549464 0.0787766009 sample12 -0.0322105228 0.1479824659 sample13 0.0293891324 -0.0306748518 sample14 -0.0337481700 -0.0367506890 sample15 -0.0815539518 0.1275622325 sample16 -0.0508449944 0.0540604642 sample17 -0.0062595920 0.0041023735 sample18 -0.0705639093 -0.0351047802 sample19 0.0476840620 -0.0509598031 sample20 -0.0522964009 0.0715521714 sample21 0.0119128995 -0.0376092979 sample22 -0.0724394319 -0.0095625299 sample23 0.0992532102 0.0134288987 sample24 0.1595120874 0.0728662433 sample25 0.0920692767 -0.0749757111 sample26 0.0595540620 0.0848966184 sample27 -0.0826487352 -0.0086735653 sample28 0.0384789036 0.0440966983 sample29 -0.0777672812 0.1735308327 sample30 -0.1229471347 -0.0819005767 sample31 -0.0579844606 -0.0238644799 sample32 -0.0970392668 -0.0111426468 sample33 -0.1017587864 -0.0630442758 sample34 -0.0637922498 0.0377941606 sample35 -0.0789984802 -0.0229723396 sample36 -0.1224939183 -0.1274955196 sample37 -0.1798821265 -0.1673427817 sample38 -0.0466306120 0.0888160801 sample39 0.0168687776 0.0421533798 sample40 -0.1756392572 -0.1526642733 sample41 -0.0042372577 0.0004928728 sample42 0.0447849269 -0.0651504936 sample43 -0.0482308139 -0.0253529351 sample44 0.1986716678 -0.0545777422 sample45 0.0741837912 0.0054703485 sample46 -0.0478773743 -0.0007072171 sample47 -0.0608189020 0.0481622502 sample48 0.1381488972 0.0578288024 sample49 0.0530522396 -0.1405532666 sample50 0.0173797091 0.1602389610 sample51 -0.0462559188 0.0303473818 sample52 -0.0280063829 0.0280388382 sample53 -0.0667619251 0.0237702000 sample54 -0.0121833180 -0.0521354333 sample55 -0.0182395862 0.0221328401 sample56 0.0001256490 0.0030907393 sample57 -0.0316673716 0.0530190284 sample58 -0.0393917694 -0.0297798804 sample59 -0.1278290573 -0.0546528189 sample60 -0.1486984749 0.1069156281 sample61 -0.0793121554 0.0569796409 sample62 -0.1172801379 -0.0149198723 sample63 0.0028728260 0.1300519908 sample64 -0.0237363500 0.1073287727 sample65 0.0126535050 0.0589808474 sample66 0.0468195453 -0.0771072580 sample67 -0.1494264939 -0.0769860612 sample68 -0.0977962265 -0.0577351287 sample69 -0.0403087354 0.0156042046 sample70 -0.0221532413 0.0315440881 sample71 0.0546432810 -0.0272396404 sample72 -0.1107488025 -0.0537319597 sample73 -0.0906761179 0.0579966445 sample74 -0.0586554698 0.0121421596 sample75 -0.0390493609 0.0349282730 sample76 0.0022960375 -0.1676558817 sample77 0.0232096329 -0.2067302762 sample78 0.0929755895 -0.0434939278 sample79 0.1619494843 -0.0378114009 sample80 -0.0680366001 0.1424663356 sample81 0.0530783084 -0.0358350783 sample82 -0.0266822420 -0.0577445181 sample83 -0.1517235308 -0.0448554632 sample84 0.0570966704 -0.0273813147 sample85 -0.1086289382 -0.1228119540 sample86 -0.0833860459 -0.0442915168 sample87 -0.0022018771 -0.0943906884 sample88 0.0078225784 -0.1140506499 sample89 -0.0611056394 -0.0094585235 sample90 -0.0022928317 -0.0936254007 sample91 -0.0433593565 0.3205982594 sample92 0.1815333561 -0.0334680028 sample93 -0.0267631146 0.0614428967 sample94 -0.0181878168 0.0605090367 sample95 0.0720374696 -0.0013045553 sample96 0.0559714079 -0.0118791331 sample97 0.0217411175 0.0195414204 sample98 -0.0379177762 0.0588357026 sample99 0.0792428644 -0.0151273592 sample100 -0.0222116150 -0.0023321463 sample101 0.0387226198 0.1224226208 sample102 0.2094614365 -0.0516442205 sample103 -0.0138482493 0.0301051878 sample104 0.0807986372 -0.0162718790 sample105 0.0520493327 -0.1229665071 sample106 0.0192613792 -0.0185238132 sample107 -0.0319017116 0.0405123237 sample108 0.0140690716 0.0163421400 sample109 0.1831928898 0.0613007873 sample110 0.0292790469 -0.0199849033 sample111 0.1423250728 0.0327340561 sample112 -0.0426332526 -0.0029083511 sample113 0.0771904983 0.0268733820 sample114 0.0241640207 -0.0184080413 sample115 0.1959014606 0.0460131026 sample116 0.1394475368 -0.0530805571 sample117 0.1672360978 -0.1386536120 sample118 0.0448344078 -0.0117621858 sample119 0.0910383140 0.2217433440 sample120 0.0331392433 -0.0057274416 sample121 -0.0307573907 0.1392506532 sample122 0.0839782111 -0.0291994217 sample123 -0.0239650825 -0.0642163794 sample124 0.0909151061 0.0130419704 sample125 0.0065351052 -0.1092631798 sample126 -0.0935311200 0.1368283905 sample127 -0.0035388194 0.0292755626 sample128 0.0660296404 0.1018566500 sample129 -0.0693639135 -0.0695421900 sample130 -0.0008493835 -0.0669704345 sample131 -0.0431023809 0.0174064792 sample132 0.0637040944 0.0029374890 sample133 0.0289494264 -0.0390818798 sample134 -0.0446201990 0.0456334458 sample135 -0.0712337091 0.0521634813 sample136 -0.0596271798 0.0197299180 sample137 -0.0793152361 -0.0380628477 sample138 0.0973547621 -0.0454218100 sample139 -0.0539904063 -0.1534327458 sample140 -0.0850827773 0.0955814340 sample141 0.0192682526 -0.0554449999 sample142 0.0672262557 -0.0461320764 sample143 0.0303730039 -0.0519260203 sample144 0.0089364343 0.0145814926 sample145 0.0638771747 0.0122258601 sample146 -0.0585857427 0.0063083191 sample147 -0.0894133557 -0.1124615897 sample148 0.0216368176 -0.0615967038 sample149 0.0515418958 -0.0839903473 sample150 -0.0568282421 -0.0124469001 sample151 0.0789532079 -0.0261831041 sample152 0.0330752471 0.1306443615 sample153 0.1751933536 0.1497732543 sample154 -0.0421425433 -0.0037010325 sample155 -0.0680177862 0.0095711082 sample156 -0.0388911813 0.1057562867 sample157 -0.0314769335 0.0561367368 sample158 -0.0329620711 0.0353947253 sample159 0.0398417334 -0.1007373669 sample160 -0.0424938198 0.0108496115 sample161 0.0888370802 -0.0679700025 sample162 0.0027477524 0.1237843937 sample163 0.0126107353 0.0725434462 sample164 0.0566779789 -0.0458324065 sample165 0.0315336308 -0.0236362292 sample166 0.0612059004 -0.0425232881 sample167 -0.0142729866 0.0179308251 sample168 0.0169504164 -0.0769617842 sample169 -0.0675080063 0.0131505206 > discoRes@scores$dist[[2]] ## Distinctive scores for Block 2 1 2 sample1 0.0012329710 -1.635717e-01 sample2 0.0724350148 -6.021286e-03 sample3 0.0188460431 -1.080036e-01 sample4 -0.0390145221 3.113914e-04 sample5 -0.1774811611 -2.996386e-02 sample6 0.0451444472 -3.455859e-02 sample7 0.0226466178 -7.020133e-03 sample8 0.1033680348 -9.856818e-03 sample9 -0.1350011796 8.979099e-02 sample10 -0.1259887177 -5.097855e-02 sample11 -0.0979788429 7.086535e-02 sample12 0.0863019149 -8.620317e-02 sample13 0.1381401129 1.828007e-01 sample14 0.0615073880 -2.642803e-02 sample15 -0.0381598996 -3.101662e-02 sample16 0.0048776792 1.271817e-03 sample17 0.0788481002 -1.547555e-02 sample18 0.0884188766 -3.795486e-02 sample19 -0.0703044393 -1.084004e-01 sample20 0.0025585419 7.975877e-02 sample21 -0.0941601540 -4.126745e-02 sample22 0.0550273348 -7.806740e-02 sample23 -0.0679495236 -4.102007e-02 sample24 0.1310962964 1.649308e-01 sample25 -0.0113585231 -4.426864e-02 sample26 0.1402945991 2.016540e-02 sample27 -0.0261561252 1.588489e-03 sample28 0.0724198769 5.850590e-02 sample29 0.0330058478 2.060862e-03 sample30 0.0228752492 -2.015428e-02 sample31 0.0635068000 -6.670335e-02 sample32 -0.0685099667 -4.955272e-02 sample33 0.0777765203 -1.272078e-01 sample34 -0.0157842411 -3.024314e-02 sample35 0.0529632637 1.500972e-01 sample36 -0.0070900944 2.025308e-01 sample37 0.0442420368 1.802089e-01 sample38 0.0781511228 -3.676417e-02 sample39 -0.0120331814 -3.388842e-02 sample40 0.0473291858 1.471562e-01 sample41 -0.0228189475 -2.673552e-02 sample42 0.0245360286 -7.960867e-02 sample43 -0.1036362793 -8.229577e-02 sample44 0.1012228957 7.049445e-02 sample45 -0.0013731910 -2.450915e-02 sample46 0.0558509929 2.947416e-03 sample47 0.0380481115 4.554175e-02 sample48 -0.0784342033 4.888978e-02 sample49 0.0605164062 -1.162359e-02 sample50 -0.0530079348 -2.737929e-02 sample51 -0.1514646507 5.678343e-02 sample52 -0.1860935256 1.246717e-01 sample53 0.0064177153 -2.700995e-02 sample54 -0.0697038327 -2.308389e-02 sample55 -0.1633577049 1.366442e-02 sample56 -0.1011485077 4.682203e-02 sample57 -0.1730374220 1.609603e-01 sample58 0.0071384707 -1.666955e-02 sample59 0.0030461597 3.005288e-02 sample60 -0.0215835316 2.665878e-01 sample61 -0.1510583685 1.002385e-01 sample62 0.0925533886 -4.845839e-02 sample63 0.0596311880 -4.137025e-02 sample64 0.0449225835 -2.600591e-03 sample65 -0.0939383723 -4.406910e-02 sample66 -0.1063400679 -5.709995e-02 sample67 0.0201589786 2.361728e-01 sample68 -0.0037203333 2.418394e-02 sample69 0.0645161223 -1.155622e-01 sample70 0.1013440008 -1.351789e-01 sample71 0.0016467842 -2.976840e-02 sample72 -0.0328893088 -2.835855e-02 sample73 -0.0275080061 -5.148185e-02 sample74 -0.1341719661 -7.895280e-02 sample75 -0.0951575688 -3.943183e-02 sample76 0.0864721916 3.034993e-02 sample77 0.1035749566 -2.545353e-02 sample78 0.1575644226 4.939590e-02 sample79 -0.0189137093 4.874679e-02 sample80 -0.1384140640 4.266840e-05 sample81 0.0118846469 -6.357931e-02 sample82 0.1675308129 3.533913e-02 sample83 0.0065673342 -7.812606e-02 sample84 -0.1486891585 -3.109058e-02 sample85 0.0532724316 7.417887e-02 sample86 0.1138477271 -1.912883e-05 sample87 -0.0432864032 6.080473e-02 sample88 -0.0433450390 1.402490e-01 sample89 -0.0331205764 -1.395401e-02 sample90 0.0607412829 -8.610413e-02 sample91 0.0566272439 1.303748e-01 sample92 0.0359582481 1.061604e-01 sample93 0.0433646362 -4.443634e-02 sample94 0.0477291320 -1.059574e-01 sample95 0.0249595783 -3.980525e-02 sample96 -0.0035218971 -9.293928e-02 sample97 0.0066048832 -1.527231e-01 sample98 -0.0020366824 -5.579549e-02 sample99 0.0886616225 -3.728230e-02 sample100 0.1091259145 -3.560419e-02 sample101 0.0739726432 -4.317996e-02 sample102 -0.0574461013 -2.783919e-02 sample103 -0.0142731084 9.705580e-03 sample104 -0.0710395211 4.068350e-02 sample105 -0.0980831315 -3.452954e-02 sample106 0.0254259338 3.628982e-02 sample107 0.0160653481 -9.173395e-02 sample108 0.0200987672 -2.379692e-02 sample109 0.0389780704 1.692357e-02 sample110 0.0326304855 2.988109e-02 sample111 -0.0676937512 -6.038213e-02 sample112 -0.0167883441 5.336938e-03 sample113 -0.0969216941 -2.757606e-02 sample114 0.0026398348 -9.209155e-02 sample115 0.0308047389 1.603820e-02 sample116 0.1240307171 1.273000e-01 sample117 -0.0334729057 5.392709e-02 sample118 0.1037152907 6.252431e-02 sample119 0.1064176538 1.196203e-01 sample120 0.0771355155 -1.004933e-01 sample121 0.0129350784 3.181974e-02 sample122 -0.0847492179 -5.568330e-02 sample123 0.0041336745 7.693190e-03 sample124 0.0583458105 -8.396392e-02 sample125 -0.0634844573 -5.232541e-02 sample126 0.0662580970 -1.091732e-01 sample127 0.0865024631 -1.094176e-01 sample128 0.0627817541 -1.470967e-02 sample129 0.0336276400 -4.007856e-02 sample130 0.0293517755 -8.046116e-02 sample131 0.0469197644 -2.209740e-03 sample132 0.0241740797 -1.248599e-01 sample133 -0.0907303223 1.466700e-02 sample134 0.0350842060 7.539662e-02 sample135 -0.0001333446 9.185391e-03 sample136 0.0335876029 -9.860271e-02 sample137 0.0640148867 -7.554467e-02 sample138 -0.0060964816 -1.742763e-02 sample139 0.0592084416 5.614970e-02 sample140 -0.0427985993 -1.099548e-02 sample141 -0.0618796314 -9.301039e-02 sample142 -0.0898554422 3.573415e-02 sample143 -0.0817389255 8.880524e-02 sample144 -0.0787754773 -3.821391e-02 sample145 -0.1085821531 1.569476e-01 sample146 0.0589557873 -4.373357e-02 sample147 0.0495330374 7.277224e-03 sample148 -0.1161592741 9.079058e-03 sample149 0.0121579374 7.788376e-02 sample150 0.0314512523 3.520213e-02 sample151 -0.0575382137 -1.945353e-02 sample152 0.0494542066 7.025538e-02 sample153 0.0941332880 2.153296e-01 sample154 0.0335931934 2.078731e-02 sample155 -0.0690457687 -2.780408e-02 sample156 -0.1039901637 -6.292524e-02 sample157 0.0408645781 8.065515e-03 sample158 -0.1018105333 7.816881e-03 sample159 0.0281730585 -1.207208e-02 sample160 -0.1643053011 2.978098e-03 sample161 -0.0374329263 8.524610e-02 sample162 0.0804535386 8.349751e-02 sample163 0.0743228074 -1.406228e-02 sample164 -0.1208805970 -2.139462e-02 sample165 -0.1608115913 2.025191e-02 sample166 0.0425944717 -2.660717e-02 sample167 0.0226849483 -4.464281e-02 sample168 0.0180735620 -7.466347e-04 sample169 -0.0190779039 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 25.182 0.961 30.976
STATegRa.Rcheck/STATegRa-Ex.timings
name | user | system | elapsed | |
STATegRaUsersGuide | 0.001 | 0.002 | 0.003 | |
STATegRa_data | 0.342 | 0.026 | 0.456 | |
STATegRa_data_TCGA_BRCA | 0.003 | 0.002 | 0.007 | |
bioDist | 0.983 | 0.093 | 1.239 | |
bioDistFeature | 0.618 | 0.049 | 0.744 | |
bioDistFeaturePlot | 0.622 | 0.044 | 0.773 | |
bioDistW | 0.598 | 0.031 | 0.722 | |
bioDistWPlot | 0.621 | 0.036 | 0.798 | |
bioMap | 0.005 | 0.003 | 0.008 | |
combiningMappings | 0.025 | 0.006 | 0.039 | |
createOmicsExpressionSet | 0.234 | 0.008 | 0.435 | |
getInitialData | 1.046 | 0.184 | 1.468 | |
getLoadings | 1.070 | 0.254 | 1.578 | |
getMethodInfo | 1.122 | 0.117 | 1.509 | |
getPreprocessing | 1.633 | 1.681 | 3.903 | |
getScores | 1.185 | 0.153 | 1.568 | |
getVAF | 1.016 | 0.102 | 1.307 | |
holistOmics | 0.003 | 0.003 | 0.007 | |
modelSelection | 3.673 | 4.238 | 9.245 | |
omicsCompAnalysis | 6.677 | 0.222 | 8.062 | |
omicsNPC | 0.004 | 0.004 | 0.012 | |
plotRes | 8.624 | 0.231 | 10.417 | |
plotVAF | 7.618 | 0.214 | 9.138 | |