Back to Multiple platform build/check report for BioC 3.19: simplified long |
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This page was generated on 2024-07-24 09:04 -0400 (Wed, 24 Jul 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" | 4747 |
palomino7 | Windows Server 2022 Datacenter | x64 | 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" | 4489 |
merida1 | macOS 12.7.5 Monterey | x86_64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4518 |
kjohnson1 | macOS 13.6.6 Ventura | arm64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4467 |
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 | 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: 2024-07-22 05:35:12 -0400 (Mon, 22 Jul 2024) |
EndedAt: 2024-07-22 05:40:01 -0400 (Mon, 22 Jul 2024) |
EllapsedTime: 289.6 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 4.4.1 (2024-06-14) * 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 4.4.1 (2024-06-14) -- "Race for Your Life" 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 -- Mon Jul 22 05:38:05 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 3.054 0.209 3.251
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-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 23.599 0.501 24.090
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-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 59.959 0.404 60.352
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-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 -0.0781574314 -0.0431502111 sample2 0.1192218403 0.0294088545 sample3 0.0531412113 -0.0746839813 sample4 -0.0292975146 -0.0005959950 sample5 -0.0202091750 0.0110463772 sample6 -0.1226089060 0.1053467091 sample7 -0.1078928115 -0.0322475781 sample8 -0.1782895306 0.1449363992 sample9 -0.0468698116 -0.0455174312 sample10 0.0036030522 0.0420111062 sample11 0.0035566477 -0.0566292605 sample12 -0.1006128911 0.0641380735 sample13 0.1174408335 0.0907488332 sample14 -0.0981203265 0.0617738125 sample15 -0.0085334309 -0.0087013535 sample16 -0.0783148661 0.1581294450 sample17 0.1483609914 0.0638581917 sample18 0.0963086244 0.0556640142 sample19 0.0217244098 -0.0720086122 sample20 0.0635636403 -0.0779653178 sample21 0.0201840347 0.1566391393 sample22 -0.0218268737 -0.0764104436 sample23 -0.0852042011 -0.0032688745 sample24 0.1287170631 0.1924543550 sample25 0.0430574152 -0.0456566076 sample26 0.1453896833 0.0541512126 sample27 0.0197488809 -0.1185656840 sample28 0.1025336301 0.0650685517 sample29 -0.0706018493 -0.0682988181 sample30 0.1295627525 -0.0066769344 sample31 -0.1147449128 0.1232686604 sample32 0.0374310878 0.0380177948 sample33 -0.0599515999 0.0136867174 sample34 0.0984200817 0.0375321038 sample35 0.0543098363 -0.0378105999 sample36 -0.1403625450 -0.0343756212 sample37 -0.0228941892 -0.0732846393 sample38 0.0222077251 -0.0962594867 sample39 0.0941738493 0.0215199205 sample40 -0.0643801164 -0.0687871103 sample41 0.0327638027 -0.1232188153 sample42 0.0500431839 -0.0292473304 sample43 0.0184498829 0.0233011090 sample44 -0.1487898786 0.1171354396 sample45 0.1050774176 0.1123201600 sample46 0.1151195715 -0.1094028803 sample47 0.0962593725 -0.0288463800 sample48 -0.0004837319 -0.0310277762 sample49 -0.1135207792 0.1213973030 sample50 0.0123553133 -0.1740743688 sample51 -0.0550529864 0.1258886673 sample52 -0.0499121220 0.0728544515 sample53 -0.1119773657 0.1588013792 sample54 0.0360055679 0.0228575613 sample55 -0.0210418985 0.0006731733 sample56 0.0434169235 0.0633126068 sample57 -0.0197824609 0.1150713527 sample58 -0.0030439893 0.0326097955 sample59 -0.0500253122 0.0129418662 sample60 -0.0184278665 0.0136085000 sample61 -0.0150299408 0.0635025694 sample62 0.0304763894 -0.0201319481 sample63 -0.1102252480 0.1285976944 sample64 -0.1552588092 0.0971168279 sample65 0.0058503063 0.0207115504 sample66 0.0025605353 0.0424320082 sample67 -0.1546634829 -0.0661716825 sample68 -0.0536369270 -0.0923683930 sample69 -0.0640330368 0.0081983022 sample70 -0.0163517742 -0.0663230120 sample71 0.0102537635 -0.1345921248 sample72 0.0654196050 -0.0196119767 sample73 0.1048556145 0.0220938078 sample74 -0.0123799467 0.0586115035 sample75 -0.0392077928 -0.0209755109 sample76 -0.0648953387 -0.0524764424 sample77 -0.1172922135 -0.0201186660 sample78 0.1463068073 0.0708472623 sample79 -0.0265211193 -0.1603307536 sample80 -0.0279737139 -0.0214205279 sample81 -0.0079211489 -0.0738450684 sample82 0.1544236499 -0.0361468021 sample83 0.0494211410 -0.0050048899 sample84 0.0259038481 -0.0346549368 sample85 -0.1116484356 -0.0031498097 sample86 0.1306483034 -0.0377215452 sample87 0.0554778202 -0.0459748951 sample88 0.0301623835 0.0382197539 sample89 0.1016866717 0.0694033652 sample90 -0.0086819874 -0.0201320096 sample91 -0.1578625328 -0.2097828259 sample92 -0.0170936842 -0.1655806395 sample93 0.0979806824 -0.0121512317 sample94 -0.0131484088 -0.0114932111 sample95 -0.0315682626 -0.0758858844 sample96 -0.0024125609 -0.0470135319 sample97 -0.0634545406 0.0270332045 sample98 0.0359374647 -0.0135488551 sample99 0.1009163305 0.1124780348 sample100 -0.0551753134 0.0246489473 sample101 0.0080118906 -0.1627368368 sample102 0.0046444296 0.0095632816 sample103 0.0472523189 -0.0940393354 sample104 -0.0198159486 -0.0591091268 sample105 0.0400237802 -0.0160911742 sample106 0.0923808406 0.0369017789 sample107 0.1019373955 0.0224954083 sample108 0.0877091654 -0.0128834121 sample109 -0.0864824401 -0.0900940946 sample110 0.1223115532 -0.0096085596 sample111 -0.0257354629 -0.0936168450 sample112 0.0765286607 0.0270347382 sample113 -0.0258803247 0.0377497614 sample114 -0.0021138907 -0.0882014724 sample115 -0.0303460221 -0.0723584783 sample116 -0.0780508454 -0.0685065813 sample117 -0.0536898122 -0.0911907661 sample118 -0.0666651166 -0.0236230908 sample119 -0.1021871633 -0.2324936458 sample120 -0.0750216557 0.0243379333 sample121 0.0756936390 0.0942950439 sample122 0.0259628075 0.0731987647 sample123 0.1037846263 -0.0369197321 sample124 -0.0611207938 0.0421724132 sample125 0.0738472724 0.0066950211 sample126 -0.0972916430 0.0762639609 sample127 -0.0824697625 -0.0096637276 sample128 0.1249407625 0.0929313025 sample129 0.0734067534 -0.0434363229 sample130 0.0003502014 -0.0309852629 sample131 -0.0930182811 0.0155936903 sample132 -0.0736222819 0.0733030204 sample133 0.0498397984 -0.0462437310 sample134 -0.1644873503 0.0720005322 sample135 0.0752297210 0.0003817408 sample136 -0.0227145751 -0.0495506263 sample137 -0.0564717392 -0.0288916112 sample138 -0.0255988115 -0.0610856535 sample139 -0.0621217807 0.0235807357 sample140 0.0604152559 -0.0435593645 sample141 -0.0246743965 0.0532648853 sample142 0.0409560318 0.0316280191 sample143 0.0077355220 -0.0476896173 sample144 -0.0173240815 -0.0156777857 sample145 -0.0485474550 0.1202770836 sample146 -0.0419645614 -0.0811281574 sample147 0.0977308364 -0.0274840500 sample148 -0.0368256192 0.0803979786 sample149 0.0072865795 -0.1532985809 sample150 -0.1020825293 0.0624774308 sample151 -0.0305399066 -0.0289277781 sample152 0.0533594793 -0.0638308982 sample153 0.0891627581 0.1799579088 sample154 0.0727557484 -0.0834161123 sample155 0.0880668585 -0.0220819876 sample156 0.0276561072 -0.0326625434 sample157 0.1155032188 0.0183615936 sample158 0.0281507534 -0.0104938779 sample159 -0.0663235721 0.0443837523 sample160 0.0302643902 0.0404265301 sample161 -0.0114715578 -0.0591025073 sample162 0.1337087095 0.1398135458 sample163 -0.1330124504 0.1688781322 sample164 0.0150336096 0.0028416502 sample165 -0.0076520267 -0.0164128218 sample166 -0.0367794388 0.0630662351 sample167 -0.1111988861 0.0030057796 sample168 0.0672981592 0.0446279399 sample169 0.0413004978 0.0224393931 > discoRes@scores$dist[[1]] ## Distinctive scores for Block 1 1 2 sample1 0.0420515061 0.0867863076 sample2 0.0820828465 -0.0410978057 sample3 -0.0155899405 -0.0195182368 sample4 0.1001337100 -0.0410786712 sample5 0.0153465935 -0.0253259690 sample6 -0.0340326338 -0.0408223185 sample7 -0.0722579634 0.0002332262 sample8 0.0457499259 -0.0370016236 sample9 0.0086249476 0.0820184901 sample10 0.0423598448 -0.0083923299 sample11 -0.0022548358 0.0787766045 sample12 -0.0322106007 0.1479824697 sample13 0.0293889292 -0.0306748625 sample14 -0.0337482980 -0.0367506846 sample15 -0.0815539018 0.1275622515 sample16 -0.0508452929 0.0540604671 sample17 -0.0062596969 0.0041023718 sample18 -0.0705640068 -0.0351047668 sample19 0.0476842091 -0.0509598106 sample20 -0.0522962445 0.0715521880 sample21 0.0119125952 -0.0376093097 sample22 -0.0724392717 -0.0095625091 sample23 0.0992532185 0.0134288757 sample24 0.1595117161 0.0728661971 sample25 0.0920693589 -0.0749757298 sample26 0.0595539893 0.0848966024 sample27 -0.0826485084 -0.0086735398 sample28 0.0384787876 0.0440966863 sample29 -0.0777671145 0.1735308544 sample30 -0.1229471259 -0.0819005480 sample31 -0.0579846977 -0.0238644735 sample32 -0.0970393326 -0.0111426269 sample33 -0.1017588049 -0.0630442533 sample34 -0.0637923006 0.0377941732 sample35 -0.0789984354 -0.0229723187 sample36 -0.1224939314 -0.1274954891 sample37 -0.1798820586 -0.1673427357 sample38 -0.0466303947 0.0888160963 sample39 0.0168687593 0.0421533747 sample40 -0.1756391914 -0.1526642286 sample41 -0.0042370133 0.0004928804 sample42 0.0447849883 -0.0651505024 sample43 -0.0482308509 -0.0253529259 sample44 0.1986714078 -0.0545777940 sample45 0.0741835888 0.0054703253 sample46 -0.0478771533 -0.0007071998 sample47 -0.0608188353 0.0481622662 sample48 0.1381489582 0.0578287723 sample49 0.0530519688 -0.1405532854 sample50 0.0173800858 0.1602389664 sample51 -0.0462561748 0.0303473851 sample52 -0.0280065511 0.0280388404 sample53 -0.0667622290 0.0237702064 sample54 -0.0121833692 -0.0521354320 sample55 -0.0182395923 0.0221328438 sample56 0.0001255186 0.0030907356 sample57 -0.0316676195 0.0530190293 sample58 -0.0393918358 -0.0297798731 sample59 -0.1278291044 -0.0546527902 sample60 -0.1486985299 0.1069156622 sample61 -0.0793122903 0.0569796555 sample62 -0.1172800877 -0.0149198441 sample63 0.0028726094 0.1300519830 sample64 -0.0237365225 0.1073287728 sample65 0.0126534843 0.0589808430 sample66 0.0468194533 -0.0771072715 sample67 -0.1494264407 -0.0769860227 sample68 -0.0977960707 -0.0577351011 sample69 -0.0403087282 0.0156042132 sample70 -0.0221530781 0.0315440967 sample71 0.0546435407 -0.0272396456 sample72 -0.1107487676 -0.0537319333 sample73 -0.0906761300 0.0579966640 sample74 -0.0586555704 0.0121421692 sample75 -0.0390493112 0.0349282827 sample76 0.0022960922 -0.1676558790 sample77 0.0232096235 -0.2067302803 sample78 0.0929754503 -0.0434939524 sample79 0.1619497726 -0.0378114292 sample80 -0.0680365330 0.1424663520 sample81 0.0530784545 -0.0358350866 sample82 -0.0266821746 -0.0577445093 sample83 -0.1517235124 -0.0448554282 sample84 0.0570967339 -0.0273813264 sample85 -0.1086289815 -0.1228119286 sample86 -0.0833859693 -0.0442914951 sample87 -0.0022018190 -0.0943906853 sample88 0.0078224525 -0.1140506535 sample89 -0.0611057659 -0.0094585133 sample90 -0.0022927965 -0.0936253992 sample91 -0.0433589139 0.3205982814 sample92 0.1815336471 -0.0334680350 sample93 -0.0267630594 0.0614429036 sample94 -0.0181877598 0.0605090414 sample95 0.0720376216 -0.0013045678 sample96 0.0559715133 -0.0118791437 sample97 0.0217410945 0.0195414134 sample98 -0.0379177232 0.0588357119 sample99 0.0792426622 -0.0151273833 sample100 -0.0222116568 -0.0023321424 sample101 0.0387229730 0.1224226210 sample102 0.2094614099 -0.0516442694 sample103 -0.0138480598 0.0301051962 sample104 0.0807987356 -0.0162718944 sample105 0.0520493419 -0.1229665186 sample106 0.0192613049 -0.0185238194 sample107 -0.0319017195 0.0405123297 sample108 0.0140691122 0.0163421376 sample109 0.1831930689 0.0613007502 sample110 0.0292790657 -0.0199849092 sample111 0.1423252680 0.0327340281 sample112 -0.0426333010 -0.0029083428 sample113 0.0771904317 0.0268733618 sample114 0.0241642041 -0.0184080422 sample115 0.1959016062 0.0460130616 sample116 0.1394476303 -0.0530805847 sample117 0.1672362261 -0.1386536454 sample118 0.0448344369 -0.0117621944 sample119 0.0910387857 0.2217433365 sample120 0.0331392111 -0.0057274507 sample121 -0.0307575402 0.1392506552 sample122 0.0839780731 -0.0291994454 sample123 -0.0239650193 -0.0642163718 sample124 0.0909150411 0.0130419470 sample125 0.0065350808 -0.1092631819 sample126 -0.0935312199 0.1368284076 sample127 -0.0035387765 0.0292755638 sample128 0.0660294994 0.1018566300 sample129 -0.0693638312 -0.0695421716 sample130 -0.0008493227 -0.0669704327 sample131 -0.0431024121 0.0174064883 sample132 0.0637039729 0.0029374699 sample133 0.0289495045 -0.0390818841 sample134 -0.0446203534 0.0456334523 sample135 -0.0712336938 0.0521634977 sample136 -0.0596270615 0.0197299342 sample137 -0.0793151769 -0.0380628279 sample138 0.0973548706 -0.0454218291 sample139 -0.0539905006 -0.1534327344 sample140 -0.0850826660 0.0955814558 sample141 0.0192681511 -0.0554450077 sample142 0.0672261774 -0.0461320937 sample143 0.0303730646 -0.0519260246 sample144 0.0089364712 0.0145814911 sample145 0.0638769081 0.0122258389 sample146 -0.0585855778 0.0063083370 sample147 -0.0894133206 -0.1124615673 sample148 0.0216366400 -0.0615967135 sample149 0.0515421579 -0.0839903504 sample150 -0.0568283790 -0.0124468904 sample151 0.0789532570 -0.0261831210 sample152 0.0330753925 0.1306443578 sample153 0.1751930101 0.1497732051 sample154 -0.0421423806 -0.0037010180 sample155 -0.0680177316 0.0095711249 sample156 -0.0388910834 0.1057562969 sample157 -0.0314769471 0.0561367433 sample158 -0.0329620455 0.0353947332 sample159 0.0398416228 -0.1007373785 sample160 -0.0424938990 0.0108496186 sample161 0.0888371599 -0.0679700196 sample162 0.0027475085 0.1237843860 sample163 0.0126104214 0.0725434341 sample164 0.0566779641 -0.0458324201 sample165 0.0315336471 -0.0236362360 sample166 0.0612057727 -0.0425233056 sample167 -0.0142729876 0.0179308281 sample168 0.0169503187 -0.0769617904 sample169 -0.0675080490 0.0131505350 > discoRes@scores$dist[[2]] ## Distinctive scores for Block 2 1 2 sample1 -0.0012329683 1.635717e-01 sample2 -0.0724350125 6.021260e-03 sample3 -0.0188460437 1.080036e-01 sample4 0.0390145255 -3.114099e-04 sample5 0.1774811619 2.996385e-02 sample6 -0.0451444471 3.455858e-02 sample7 -0.0226466200 7.020158e-03 sample8 -0.1033680316 9.856784e-03 sample9 0.1350011796 -8.979098e-02 sample10 0.1259887196 5.097853e-02 sample11 0.0979788424 -7.086534e-02 sample12 -0.0863019143 8.620317e-02 sample13 -0.1381401130 -1.828007e-01 sample14 -0.0615073884 2.642803e-02 sample15 0.0381598974 3.101664e-02 sample16 -0.0048776795 -1.271839e-03 sample17 -0.0788481006 1.547554e-02 sample18 -0.0884188789 3.795486e-02 sample19 0.0703044409 1.084004e-01 sample20 -0.0025585446 -7.975875e-02 sample21 0.0941601553 4.126741e-02 sample22 -0.0550273373 7.806743e-02 sample23 0.0679495276 4.102006e-02 sample24 -0.1310962911 -1.649309e-01 sample25 0.0113585258 4.426863e-02 sample26 -0.1402945973 -2.016543e-02 sample27 0.0261561215 -1.588447e-03 sample28 -0.0724198758 -5.850592e-02 sample29 -0.0330058501 -2.060829e-03 sample30 -0.0228752541 2.015430e-02 sample31 -0.0635068005 6.670334e-02 sample32 0.0685099637 4.955273e-02 sample33 -0.0777765230 1.272078e-01 sample34 0.0157842390 3.024314e-02 sample35 -0.0529632677 -1.500972e-01 sample36 0.0070900892 -2.025307e-01 sample37 -0.0442420448 -1.802089e-01 sample38 -0.0781511246 3.676420e-02 sample39 0.0120331820 3.388841e-02 sample40 -0.0473291931 -1.471562e-01 sample41 0.0228189466 2.673554e-02 sample42 -0.0245360272 7.960867e-02 sample43 0.1036362780 8.229577e-02 sample44 -0.1012228882 -7.049451e-02 sample45 0.0013731939 2.450911e-02 sample46 -0.0558509957 -2.947387e-03 sample47 -0.0380481142 -4.554174e-02 sample48 0.0784342077 -4.888980e-02 sample49 -0.0605164036 1.162355e-02 sample50 0.0530079350 2.737932e-02 sample51 0.1514646499 -5.678344e-02 sample52 0.1860935247 -1.246717e-01 sample53 -0.0064177159 2.700993e-02 sample54 0.0697038322 2.308389e-02 sample55 0.1633577044 -1.366441e-02 sample56 0.1011485076 -4.682205e-02 sample57 0.1730374210 -1.609603e-01 sample58 -0.0071384719 1.666955e-02 sample59 -0.0030461641 -3.005286e-02 sample60 0.0215835256 -2.665877e-01 sample61 0.1510583659 -1.002385e-01 sample62 -0.0925533926 4.845841e-02 sample63 -0.0596311860 4.137022e-02 sample64 -0.0449225827 2.600580e-03 sample65 0.0939383732 4.406909e-02 sample66 0.1063400698 5.709993e-02 sample67 -0.0201589849 -2.361728e-01 sample68 0.0037203294 -2.418390e-02 sample69 -0.0645161227 1.155622e-01 sample70 -0.1013440011 1.351789e-01 sample71 -0.0016467831 2.976842e-02 sample72 0.0328893047 2.835857e-02 sample73 0.0275080032 5.148186e-02 sample74 0.1341719649 7.895280e-02 sample75 0.0951575678 3.943184e-02 sample76 -0.0864721922 -3.034992e-02 sample77 -0.1035749559 2.545353e-02 sample78 -0.1575644200 -4.939594e-02 sample79 0.0189137136 -4.874679e-02 sample80 0.1384140621 -4.265001e-05 sample81 -0.0118846452 6.357932e-02 sample82 -0.1675308151 -3.533912e-02 sample83 -0.0065673393 7.812609e-02 sample84 0.1486891602 3.109057e-02 sample85 -0.0532724356 -7.417885e-02 sample86 -0.1138477308 1.914921e-05 sample87 0.0432864020 -6.080472e-02 sample88 0.0433450383 -1.402491e-01 sample89 0.0331205744 1.395401e-02 sample90 -0.0607412828 8.610414e-02 sample91 -0.0566272457 -1.303747e-01 sample92 -0.0359582435 -1.061604e-01 sample93 -0.0433646372 4.443635e-02 sample94 -0.0477291319 1.059574e-01 sample95 -0.0249595760 3.980525e-02 sample96 0.0035218992 9.293928e-02 sample97 -0.0066048812 1.527231e-01 sample98 0.0020366814 5.579550e-02 sample99 -0.0886616194 3.728226e-02 sample100 -0.1091259147 3.560420e-02 sample101 -0.0739726423 4.317998e-02 sample102 0.0574461085 2.783914e-02 sample103 0.0142731072 -9.705559e-03 sample104 0.0710395233 -4.068351e-02 sample105 0.0980831328 3.452953e-02 sample106 -0.0254259335 -3.628984e-02 sample107 -0.0160653488 9.173394e-02 sample108 -0.0200987670 2.379692e-02 sample109 -0.0389780643 -1.692359e-02 sample110 -0.0326304853 -2.988110e-02 sample111 0.0676937560 6.038213e-02 sample112 0.0167883424 -5.336938e-03 sample113 0.0969216972 2.757604e-02 sample114 -0.0026398341 9.209157e-02 sample115 -0.0308047325 -1.603822e-02 sample116 -0.1240307132 -1.273000e-01 sample117 0.0334729104 -5.392710e-02 sample118 -0.1037152894 -6.252431e-02 sample119 -0.1064176516 -1.196202e-01 sample120 -0.0771355134 1.004933e-01 sample121 -0.0129350790 -3.181975e-02 sample122 0.0847492213 5.568327e-02 sample123 -0.0041336762 -7.693181e-03 sample124 -0.0583458064 8.396389e-02 sample125 0.0634844572 5.232540e-02 sample126 -0.0662580983 1.091733e-01 sample127 -0.0865024622 1.094176e-01 sample128 -0.0627817514 1.470964e-02 sample129 -0.0336276430 4.007858e-02 sample130 -0.0293517755 8.046117e-02 sample131 -0.0469197653 2.209748e-03 sample132 -0.0241740761 1.248598e-01 sample133 0.0907303226 -1.466700e-02 sample134 -0.0350842066 -7.539662e-02 sample135 0.0001333419 -9.185379e-03 sample136 -0.0335876045 9.860274e-02 sample137 -0.0640148890 7.554470e-02 sample138 0.0060964847 1.742762e-02 sample139 -0.0592084437 -5.614969e-02 sample140 0.0427985962 1.099551e-02 sample141 0.0618796328 9.301038e-02 sample142 0.0898554441 -3.573417e-02 sample143 0.0817389256 -8.880524e-02 sample144 0.0787754778 3.821392e-02 sample145 0.1085821554 -1.569476e-01 sample146 -0.0589557893 4.373360e-02 sample147 -0.0495330414 -7.277205e-03 sample148 0.1161592752 -9.079080e-03 sample149 -0.0121579372 -7.788374e-02 sample150 -0.0314512536 -3.520213e-02 sample151 0.0575382164 1.945353e-02 sample152 -0.0494542060 -7.025537e-02 sample153 -0.0941332821 -2.153297e-01 sample154 -0.0335931958 -2.078729e-02 sample155 0.0690457661 2.780410e-02 sample156 0.1039901627 6.292525e-02 sample157 -0.0408645794 -8.065517e-03 sample158 0.1018105320 -7.816873e-03 sample159 -0.0281730568 1.207206e-02 sample160 0.1643052998 -2.978101e-03 sample161 0.0374329284 -8.524610e-02 sample162 -0.0804535383 -8.349755e-02 sample163 -0.0743228051 1.406225e-02 sample164 0.1208805989 2.139460e-02 sample165 0.1608115921 -2.025191e-02 sample166 -0.0425944690 2.660714e-02 sample167 -0.0226849480 4.464282e-02 sample168 -0.0180735617 7.466192e-04 sample169 0.0190779015 -2.645402e-02 > # Exploring O2PLS scores structure > o2plsRes@scores$common[[1]] ## Common scores for Block 1 [,1] [,2] sample1 -0.0572060227 -1.729087e-02 sample2 0.0875245208 1.112588e-02 sample3 0.0403482602 -3.168994e-02 sample4 -0.0218345996 4.052760e-06 sample5 -0.0150905011 4.795041e-03 sample6 -0.0924362933 4.511003e-02 sample7 -0.0793066751 -1.243823e-02 sample8 -0.1342997187 6.215220e-02 sample9 -0.0338886944 -1.854401e-02 sample10 0.0020547173 1.749421e-02 sample11 0.0037275602 -2.364116e-02 sample12 -0.0753094533 2.772698e-02 sample13 0.0856160091 3.679963e-02 sample14 -0.0737457307 2.668452e-02 sample15 -0.0062111746 -3.554864e-03 sample16 -0.0602355268 6.675115e-02 sample17 0.1086768843 2.524534e-02 sample18 0.0702999472 2.231671e-02 sample19 0.0173785882 -3.024846e-02 sample20 0.0484173812 -3.310904e-02 sample21 0.0124657042 6.517144e-02 sample22 -0.0140989936 -3.159137e-02 sample23 -0.0627028403 -5.393710e-04 sample24 0.0919972100 7.909297e-02 sample25 0.0326998483 -1.945206e-02 sample26 0.1064741246 2.120849e-02 sample27 0.0166058995 -4.964993e-02 sample28 0.0743504770 2.614211e-02 sample29 -0.0511008491 -2.782647e-02 sample30 0.0962250842 -3.974893e-03 sample31 -0.0869563008 5.250819e-02 sample32 0.0271858919 1.552005e-02 sample33 -0.0448364581 6.243160e-03 sample34 0.0718415218 1.469396e-02 sample35 0.0403086451 -1.632629e-02 sample36 -0.1036402827 -1.304320e-02 sample37 -0.0159385744 -3.036525e-02 sample38 0.0182198369 -4.034805e-02 sample39 0.0690363619 8.058350e-03 sample40 -0.0467312750 -2.810325e-02 sample41 0.0263674438 -5.171216e-02 sample42 0.0374578960 -1.268634e-02 sample43 0.0132336869 9.536642e-03 sample44 -0.1119154428 5.028683e-02 sample45 0.0759639367 4.587903e-02 sample46 0.0871885519 -4.670385e-02 sample47 0.0721490571 -1.288540e-02 sample48 0.0005086144 -1.290565e-02 sample49 -0.0858177028 5.173760e-02 sample50 0.0118992665 -7.276215e-02 sample51 -0.0426446855 5.306205e-02 sample52 -0.0381605826 3.086785e-02 sample53 -0.0855757630 6.730043e-02 sample54 0.0261723092 9.184260e-03 sample55 -0.0156418304 4.682404e-04 sample56 0.0307831193 2.597550e-02 sample57 -0.0157242103 4.829381e-02 sample58 -0.0031174404 1.359898e-02 sample59 -0.0373001859 5.868397e-03 sample60 -0.0142609099 5.831654e-03 sample61 -0.0122255144 2.663579e-02 sample62 0.0228002942 -8.692265e-03 sample63 -0.0833127581 5.473229e-02 sample64 -0.1166548159 4.196500e-02 sample65 0.0038808902 8.568590e-03 sample66 0.0011561811 1.766612e-02 sample67 -0.1129311062 -2.608702e-02 sample68 -0.0382526429 -3.804045e-02 sample69 -0.0476502440 4.003241e-03 sample70 -0.0110329882 -2.752719e-02 sample71 0.0096850282 -5.627056e-02 sample72 0.0487124704 -8.800131e-03 sample73 0.0773058132 8.239864e-03 sample74 -0.0102488176 2.454957e-02 sample75 -0.0286613976 -8.387293e-03 sample76 -0.0472655595 -2.129315e-02 sample77 -0.0865043074 -7.296820e-03 sample78 0.1070293698 2.818346e-02 sample79 -0.0165060681 -6.659721e-02 sample80 -0.0206765949 -8.712112e-03 sample81 -0.0050943615 -3.079175e-02 sample82 0.1153622361 -1.647054e-02 sample83 0.0367979217 -2.538114e-03 sample84 0.0199463070 -1.468961e-02 sample85 -0.0827122185 -2.709824e-04 sample86 0.0969487314 -1.699897e-02 sample87 0.0421957457 -1.965953e-02 sample88 0.0215934743 1.566050e-02 sample89 0.0751559502 2.811652e-02 sample90 -0.0057328000 -8.283795e-03 sample91 -0.1134005268 -8.603522e-02 sample92 -0.0101689918 -6.894992e-02 sample93 0.0725967502 -6.003176e-03 sample94 -0.0096878852 -4.693081e-03 sample95 -0.0223502239 -3.139636e-02 sample96 -0.0013232863 -1.963604e-02 sample97 -0.0476541710 1.183660e-02 sample98 0.0269546160 -5.978398e-03 sample99 0.0728179461 4.597884e-02 sample100 -0.0413398038 1.079347e-02 sample101 0.0087536994 -6.796076e-02 sample102 0.0032509529 3.932612e-03 sample103 0.0360342395 -3.973263e-02 sample104 -0.0141722563 -2.453107e-02 sample105 0.0294940465 -7.140722e-03 sample106 0.0686472054 1.462895e-02 sample107 0.0748635927 8.401339e-03 sample108 0.0650175850 -6.211942e-03 sample109 -0.0628017242 -3.681224e-02 sample110 0.0905513691 -5.169053e-03 sample111 -0.0176679473 -3.884777e-02 sample112 0.0570870472 1.066018e-02 sample113 -0.0200110554 1.596044e-02 sample114 -0.0001474542 -3.679272e-02 sample115 -0.0213333038 -2.991667e-02 sample116 -0.0567675453 -2.785636e-02 sample117 -0.0379865990 -3.752078e-02 sample118 -0.0484878786 -9.173691e-03 sample119 -0.0713511831 -9.598634e-02 sample120 -0.0555093586 1.089843e-02 sample121 0.0542443861 3.861344e-02 sample122 0.0178575357 3.027138e-02 sample123 0.0775020581 -1.636852e-02 sample124 -0.0460701050 1.814758e-02 sample125 0.0543846585 2.075898e-03 sample126 -0.0729417144 3.276659e-02 sample127 -0.0609509157 -3.270814e-03 sample128 0.0908136899 3.758801e-02 sample129 0.0552445878 -1.879062e-02 sample130 0.0007128089 -1.294308e-02 sample131 -0.0693311345 7.357082e-03 sample132 -0.0556565156 3.126995e-02 sample133 0.0375870104 -1.977240e-02 sample134 -0.1229130924 3.159495e-02 sample135 0.0555550315 -5.563250e-04 sample136 -0.0159768414 -2.046339e-02 sample137 -0.0412337694 -1.151652e-02 sample138 -0.0180604476 -2.526505e-02 sample139 -0.0465649201 1.040683e-02 sample140 0.0452288969 -1.876279e-02 sample141 -0.0189142561 2.247042e-02 sample142 0.0297545566 1.280524e-02 sample143 0.0064292003 -1.997706e-02 sample144 -0.0124284903 -6.369733e-03 sample145 -0.0377141491 5.066743e-02 sample146 -0.0296240067 -3.344465e-02 sample147 0.0726083535 -1.239968e-02 sample148 -0.0284795794 3.389732e-02 sample149 0.0082261455 -6.399305e-02 sample150 -0.0765013197 2.704021e-02 sample151 -0.0220567356 -1.178159e-02 sample152 0.0403422737 -2.714879e-02 sample153 0.0629117719 7.425085e-02 sample154 0.0551622927 -3.548984e-02 sample155 0.0654439133 -1.005306e-02 sample156 0.0209310714 -1.390213e-02 sample157 0.0851522597 6.577150e-03 sample158 0.0208354599 -4.663078e-03 sample159 -0.0498794349 1.913257e-02 sample160 0.0216074437 1.656579e-02 sample161 -0.0075742328 -2.455676e-02 sample162 0.0963663017 5.705881e-02 sample163 -0.1009542191 7.174224e-02 sample164 0.0109881996 1.026806e-03 sample165 -0.0053146157 -6.772855e-03 sample166 -0.0275757357 2.673084e-02 sample167 -0.0825048036 2.278863e-03 sample168 0.0486147429 1.793843e-02 sample169 0.0302506727 8.984253e-03 > o2plsRes@scores$common[[2]] ## Common scores for Block 2 [,1] [,2] sample1 -0.0621842115 -1.364509e-02 sample2 0.0944623785 9.720892e-03 sample3 0.0406196267 -2.236338e-02 sample4 -0.0229316496 -3.932487e-04 sample5 -0.0157330047 3.231033e-03 sample6 -0.0945794025 3.120720e-02 sample7 -0.0854427118 -1.052880e-02 sample8 -0.1376625920 4.286608e-02 sample9 -0.0377115311 -1.415134e-02 sample10 0.0035244506 1.280825e-02 sample11 0.0016639987 -1.717895e-02 sample12 -0.0781403168 1.884368e-02 sample13 0.0938400516 2.838858e-02 sample14 -0.0759839772 1.810989e-02 sample15 -0.0068340837 -2.705361e-03 sample16 -0.0590150849 4.757848e-02 sample17 0.1178805097 2.040526e-02 sample18 0.0767858320 1.756604e-02 sample19 0.0157112113 -2.172867e-02 sample20 0.0485318300 -2.327033e-02 sample21 0.0185928176 4.777095e-02 sample22 -0.0191358702 -2.329775e-02 sample23 -0.0672994194 -1.535656e-03 sample24 0.1047476642 5.935707e-02 sample25 0.0329844953 -1.358036e-02 sample26 0.1154952052 1.741529e-02 sample27 0.0133849853 -3.590922e-02 sample28 0.0821554039 2.042376e-02 sample29 -0.0567643690 -2.123848e-02 sample30 0.1016073931 -1.134728e-03 sample31 -0.0880396372 3.670548e-02 sample32 0.0300363338 1.182406e-02 sample33 -0.0467252272 3.739254e-03 sample34 0.0783666394 1.203777e-02 sample35 0.0424227097 -1.118559e-02 sample36 -0.1107646166 -1.143464e-02 sample37 -0.0191667664 -2.246060e-02 sample38 0.0155968095 -2.909621e-02 sample39 0.0746847148 7.148218e-03 sample40 -0.0517028178 -2.137267e-02 sample41 0.0234979494 -3.723018e-02 sample42 0.0388797356 -8.557228e-03 sample43 0.0149555568 7.210002e-03 sample44 -0.1150305613 3.461805e-02 sample45 0.0846146236 3.486020e-02 sample46 0.0884426404 -3.246853e-02 sample47 0.0748644971 -8.083045e-03 sample48 -0.0012033198 -9.403647e-03 sample49 -0.0872662737 3.616245e-02 sample50 0.0066941314 -5.284863e-02 sample51 -0.0411777630 3.791830e-02 sample52 -0.0379355780 2.180834e-02 sample53 -0.0851639886 4.751761e-02 sample54 0.0288006248 7.184424e-03 sample55 -0.0164920835 5.919925e-05 sample56 0.0355115616 1.951043e-02 sample57 -0.0141146068 3.492409e-02 sample58 -0.0015636132 9.862883e-03 sample59 -0.0390656483 3.590929e-03 sample60 -0.0139454780 3.963030e-03 sample61 -0.0106410274 1.919705e-02 sample62 0.0236748439 -5.922677e-03 sample63 -0.0846790877 3.839102e-02 sample64 -0.1202581015 2.846469e-02 sample65 0.0050548584 6.328644e-03 sample66 0.0028013072 1.291807e-02 sample67 -0.1231623009 -2.112565e-02 sample68 -0.0437782161 -2.845072e-02 sample69 -0.0501199692 2.053469e-03 sample70 -0.0140278645 -2.027157e-02 sample71 0.0057489505 -4.085977e-02 sample72 0.0511212704 -5.522408e-03 sample73 0.0828141409 7.431582e-03 sample74 -0.0085959456 1.772951e-02 sample75 -0.0312180394 -6.636869e-03 sample76 -0.0519051781 -1.640191e-02 sample77 -0.0925924762 -6.907800e-03 sample78 0.1163971046 2.251122e-02 sample79 -0.0240906926 -4.887766e-02 sample80 -0.0221327065 -6.730703e-03 sample81 -0.0072114968 -2.254399e-02 sample82 0.1204416674 -9.907422e-03 sample83 0.0386739485 -1.171663e-03 sample84 0.0195988488 -1.033806e-02 sample85 -0.0877680171 -1.725057e-03 sample86 0.1023541048 -1.062501e-02 sample87 0.0425213089 -1.356865e-02 sample88 0.0244788514 1.180820e-02 sample89 0.0804276691 2.188588e-02 sample90 -0.0074639871 -6.140721e-03 sample91 -0.1278832404 -6.485140e-02 sample92 -0.0162199697 -5.048358e-02 sample93 0.0769344893 -3.045135e-03 sample94 -0.0104345587 -3.593172e-03 sample95 -0.0260058453 -2.330475e-02 sample96 -0.0025018700 -1.433516e-02 sample97 -0.0492358305 7.774183e-03 sample98 0.0279220220 -3.862141e-03 sample99 0.0813921923 3.487339e-02 sample100 -0.0428797405 7.112807e-03 sample101 0.0032855240 -4.940743e-02 sample102 0.0038439317 2.938008e-03 sample103 0.0358511139 -2.831881e-02 sample104 -0.0162784000 -1.815061e-02 sample105 0.0314853405 -4.656633e-03 sample106 0.0726456731 1.192390e-02 sample107 0.0807342975 7.508627e-03 sample108 0.0688338003 -3.336161e-03 sample109 -0.0694151950 -2.800146e-02 sample110 0.0961218924 -2.111997e-03 sample111 -0.0217900036 -2.864702e-02 sample112 0.0599954082 8.820317e-03 sample113 -0.0195006577 1.128215e-02 sample114 -0.0032126533 -2.682851e-02 sample115 -0.0251101087 -2.221077e-02 sample116 -0.0625141551 -2.137258e-02 sample117 -0.0440473375 -2.806256e-02 sample118 -0.0532042630 -7.590494e-03 sample119 -0.0848603028 -7.133574e-02 sample120 -0.0588832131 6.937326e-03 sample121 0.0613899126 2.915307e-02 sample122 0.0218424338 2.241775e-02 sample123 0.0809008460 -1.051759e-02 sample124 -0.0472109313 1.239887e-02 sample125 0.0583180947 2.521167e-03 sample126 -0.0753941872 2.256455e-02 sample127 -0.0649774209 -3.496964e-03 sample128 0.1000212216 2.908091e-02 sample129 0.0568033049 -1.269016e-02 sample130 -0.0002370832 -9.419675e-03 sample131 -0.0727030877 4.091672e-03 sample132 -0.0566219024 2.179861e-02 sample133 0.0384172955 -1.372840e-02 sample134 -0.1280862736 2.077912e-02 sample135 0.0592633273 6.106685e-04 sample136 -0.0187635410 -1.521173e-02 sample137 -0.0449958970 -9.152840e-03 sample138 -0.0211348699 -1.875415e-02 sample139 -0.0482882861 6.729304e-03 sample140 0.0468926306 -1.285498e-02 sample141 -0.0186248693 1.605439e-02 sample142 0.0328031246 9.887746e-03 sample143 0.0052919839 -1.445666e-02 sample144 -0.0140067923 -4.867248e-03 sample145 -0.0361804310 3.625323e-02 sample146 -0.0345286735 -2.493652e-02 sample147 0.0765025670 -7.714769e-03 sample148 -0.0276016641 2.420589e-02 sample149 0.0027545308 -4.653007e-02 sample150 -0.0792296010 1.831289e-02 sample151 -0.0245894512 -8.991738e-03 sample152 0.0409796547 -1.907063e-02 sample153 0.0734301757 5.528780e-02 sample154 0.0557740684 -2.487723e-02 sample155 0.0689436560 -6.127635e-03 sample156 0.0212272938 -9.747423e-03 sample157 0.0911931194 6.355708e-03 sample158 0.0220840645 -3.016357e-03 sample159 -0.0513244242 1.304175e-02 sample160 0.0246213576 1.248444e-02 sample161 -0.0100369130 -1.805391e-02 sample162 0.1078802043 4.337260e-02 sample163 -0.1017965082 5.047171e-02 sample164 0.0119430799 9.593002e-04 sample165 -0.0063708014 -5.032148e-03 sample166 -0.0283181180 1.899222e-02 sample167 -0.0872832229 1.516582e-04 sample168 0.0540714512 1.397701e-02 sample169 0.0328432652 7.104347e-03 > o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1 [,1] [,2] sample1 0.0133684846 2.195848e-02 sample2 0.0254157197 -1.058416e-02 sample3 -0.0049551479 -4.840017e-03 sample4 0.0310390570 -1.063929e-02 sample5 0.0046941318 -6.488426e-03 sample6 -0.0107406753 -1.026702e-02 sample7 -0.0225157631 2.624712e-04 sample8 0.0141320952 -9.505821e-03 sample9 0.0029681280 2.078210e-02 sample10 0.0131729174 -2.275042e-03 sample11 -0.0004164298 1.994019e-02 sample12 -0.0095211620 3.759883e-02 sample13 0.0091018604 -7.953956e-03 sample14 -0.0106557524 -9.181659e-03 sample15 -0.0249924121 3.262724e-02 sample16 -0.0156216400 1.375700e-02 sample17 -0.0019382446 1.073994e-03 sample18 -0.0221072481 -8.703592e-03 sample19 0.0146917619 -1.311712e-02 sample20 -0.0160353760 1.826290e-02 sample21 0.0035947899 -9.616341e-03 sample22 -0.0225060762 -2.532589e-03 sample23 0.0310000683 3.033060e-03 sample24 0.0499544372 1.809450e-02 sample25 0.0284442301 -1.932558e-02 sample26 0.0188220043 2.146985e-02 sample27 -0.0257763219 -1.999228e-03 sample28 0.0120888648 1.125834e-02 sample29 -0.0236482520 4.426726e-02 sample30 -0.0385486305 -2.055935e-02 sample31 -0.0181539336 -5.877838e-03 sample32 -0.0302630460 -2.607192e-03 sample33 -0.0319565715 -1.562628e-02 sample34 -0.0197970124 9.906813e-03 sample35 -0.0247412713 -5.434440e-03 sample36 -0.0386259060 -3.190394e-02 sample37 -0.0566199273 -4.192574e-02 sample38 -0.0142060273 2.259644e-02 sample39 0.0053589035 1.076485e-02 sample40 -0.0552546493 -3.819896e-02 sample41 -0.0013089975 9.278818e-05 sample42 0.0137252142 -1.664652e-02 sample43 -0.0151259626 -6.290953e-03 sample44 0.0617391754 -1.442883e-02 sample45 0.0231410886 1.163143e-03 sample46 -0.0148898209 -1.384176e-04 sample47 -0.0187252536 1.221690e-02 sample48 0.0432839432 1.416671e-02 sample49 0.0160818605 -3.588745e-02 sample50 0.0059333545 4.067003e-02 sample51 -0.0142914866 7.776270e-03 sample52 -0.0086339952 7.208917e-03 sample53 -0.0207386980 6.272432e-03 sample54 -0.0039856719 -1.316934e-02 sample55 -0.0056217017 5.692315e-03 sample56 0.0000123292 8.978290e-04 sample57 -0.0095805555 1.324253e-02 sample58 -0.0124160295 -7.326376e-03 sample59 -0.0400195442 -1.349736e-02 sample60 -0.0460063358 2.770091e-02 sample61 -0.0245266456 1.470710e-02 sample62 -0.0366022783 -3.437352e-03 sample63 0.0013742171 3.288796e-02 sample64 -0.0070599859 2.739588e-02 sample65 0.0041201911 1.498268e-02 sample66 0.0143173351 -1.968812e-02 sample67 -0.0467477531 -1.929938e-02 sample68 -0.0306751978 -1.436184e-02 sample69 -0.0125317217 4.130407e-03 sample70 -0.0068071487 8.080857e-03 sample71 0.0169170264 -7.027348e-03 sample72 -0.0346909749 -1.333770e-02 sample73 -0.0280506153 1.493843e-02 sample74 -0.0182611498 3.294697e-03 sample75 -0.0120563964 8.974612e-03 sample76 0.0001437236 -4.253184e-02 sample77 0.0065330299 -5.252886e-02 sample78 0.0288278141 -1.127782e-02 sample79 0.0503961481 -1.023318e-02 sample80 -0.0207693429 3.648391e-02 sample81 0.0163562768 -9.074596e-03 sample82 -0.0084317129 -1.478976e-02 sample83 -0.0474097918 -1.103126e-02 sample84 0.0177181395 -7.191197e-03 sample85 -0.0342718548 -3.082360e-02 sample86 -0.0261671791 -1.089491e-02 sample87 -0.0009486358 -2.411514e-02 sample88 0.0020528931 -2.894615e-02 sample89 -0.0189361111 -2.638639e-03 sample90 -0.0009863658 -2.390075e-02 sample91 -0.0124352695 8.153234e-02 sample92 0.0564264106 -8.909537e-03 sample93 -0.0081461774 1.570851e-02 sample94 -0.0054896581 1.547251e-02 sample95 0.0224073150 -4.374348e-04 sample96 0.0173528924 -3.050441e-03 sample97 0.0067948115 5.008237e-03 sample98 -0.0116030825 1.498764e-02 sample99 0.0246422688 -4.054795e-03 sample100 -0.0069420745 -4.846343e-04 sample101 0.0124923691 3.091503e-02 sample102 0.0650835386 -1.367400e-02 sample103 -0.0042741828 7.855985e-03 sample104 0.0250591040 -4.171938e-03 sample105 0.0157516368 -3.121990e-02 sample106 0.0060593853 -5.101693e-03 sample107 -0.0098329626 1.044506e-02 sample108 0.0044269853 4.142036e-03 sample109 0.0572473486 1.517542e-02 sample110 0.0090474827 -5.119868e-03 sample111 0.0444263015 7.983232e-03 sample112 -0.0131765484 -9.696342e-04 sample113 0.0241047399 6.706740e-03 sample114 0.0074558775 -4.728652e-03 sample115 0.0611851433 1.117210e-02 sample116 0.0432646951 -1.380556e-02 sample117 0.0516750066 -3.575617e-02 sample118 0.0139942100 -3.279138e-03 sample119 0.0291722987 5.587946e-02 sample120 0.0103515853 -1.690016e-03 sample121 -0.0091396331 3.552116e-02 sample122 0.0260431679 -7.583975e-03 sample123 -0.0076666389 -1.628489e-02 sample124 0.0283466326 3.127845e-03 sample125 0.0016472378 -2.770692e-02 sample126 -0.0286529417 3.489336e-02 sample127 -0.0010224500 7.483214e-03 sample128 0.0209049296 2.572016e-02 sample129 -0.0218184878 -1.755347e-02 sample130 -0.0005009620 -1.697978e-02 sample131 -0.0134032968 4.637390e-03 sample132 0.0198526786 5.723983e-04 sample133 0.0088812957 -9.988115e-03 sample134 -0.0137484514 1.172591e-02 sample135 -0.0220314568 1.347465e-02 sample136 -0.0185173353 5.168079e-03 sample137 -0.0248352123 -9.472788e-03 sample138 0.0301635767 -1.175283e-02 sample139 -0.0173576929 -3.872592e-02 sample140 -0.0262157762 2.456863e-02 sample141 0.0058369763 -1.420854e-02 sample142 0.0207886071 -1.188764e-02 sample143 0.0092832598 -1.324238e-02 sample144 0.0028442140 3.627979e-03 sample145 0.0199749569 2.862202e-03 sample146 -0.0182236697 1.726556e-03 sample147 -0.0282519995 -2.825595e-02 sample148 0.0065435868 -1.572917e-02 sample149 0.0158233820 -2.159451e-02 sample150 -0.0177383738 -3.020633e-03 sample151 0.0245166984 -6.888241e-03 sample152 0.0107259913 3.314630e-02 sample153 0.0550963965 3.758760e-02 sample154 -0.0131452472 -8.153903e-04 sample155 -0.0211742574 2.642246e-03 sample156 -0.0117803505 2.698265e-02 sample157 -0.0096167165 1.433840e-02 sample158 -0.0101754772 9.137620e-03 sample159 0.0120662931 -2.565236e-02 sample160 -0.0132238202 2.916023e-03 sample161 0.0274491966 -1.748284e-02 sample162 0.0012482909 3.152261e-02 sample163 0.0042031315 1.830701e-02 sample164 0.0174896157 -1.175915e-02 sample165 0.0097517662 -6.119019e-03 sample166 0.0190134679 -1.121582e-02 sample167 -0.0044140836 4.665585e-03 sample168 0.0049689168 -1.941822e-02 sample169 -0.0209802098 3.498729e-03 > o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2 [,1] [,2] sample1 -0.0515543627 -0.0305856787 sample2 -0.0144993256 0.0236342950 sample3 -0.0371833108 -0.0140263348 sample4 0.0068945388 -0.0132539692 sample5 0.0215035333 -0.0663338101 sample6 -0.0187055152 0.0088773016 sample7 -0.0061521552 0.0064029054 sample8 -0.0210874459 0.0334652901 sample9 0.0516865043 -0.0291142799 sample10 0.0059440366 -0.0527217447 sample11 0.0393010793 -0.0200624712 sample12 -0.0420837100 0.0131331362 sample13 0.0333252565 0.0818552509 sample14 -0.0190062644 0.0160202175 sample15 -0.0030968049 -0.0189230681 sample16 -0.0004452158 0.0018880102 sample17 -0.0185848615 0.0240170131 sample18 -0.0273093598 0.0230213640 sample19 -0.0217761111 -0.0445894441 sample20 0.0245820821 0.0159812738 sample21 0.0034527644 -0.0400016054 sample22 -0.0340789054 0.0039289109 sample23 -0.0010344929 -0.0310161212 sample24 0.0289468503 0.0760962436 sample25 -0.0119098496 -0.0122798760 sample26 -0.0181001057 0.0517892852 sample27 0.0050465417 -0.0086515844 sample28 0.0057491502 0.0358830107 sample29 -0.0051104246 0.0116605117 sample30 -0.0103085904 0.0039678538 sample31 -0.0319929858 0.0090606113 sample32 -0.0036232521 -0.0328202010 sample33 -0.0534742153 0.0024751837 sample34 -0.0067495749 -0.0111000311 sample35 0.0378745721 0.0465929296 sample36 0.0647886800 0.0359987924 sample37 0.0488441236 0.0492906912 sample38 -0.0251514062 0.0197110110 sample39 -0.0085428066 -0.0105117852 sample40 0.0379324087 0.0440810741 sample41 -0.0044199152 -0.0128820644 sample42 -0.0292553573 -0.0067045265 sample43 -0.0077829155 -0.0510178219 sample44 0.0045122248 0.0479660309 sample45 -0.0074444298 -0.0051116726 sample46 -0.0088025512 0.0196186661 sample47 0.0076696301 0.0215947965 sample48 0.0290108585 -0.0175568376 sample49 -0.0141754858 0.0184717099 sample50 0.0006282201 -0.0233054373 sample51 0.0441995177 -0.0410022921 sample52 0.0715329391 -0.0399499475 sample53 -0.0095954087 -0.0029140909 sample54 0.0048933768 -0.0281884386 sample55 0.0327325487 -0.0532290012 sample56 0.0323068984 -0.0256595538 sample57 0.0806603122 -0.0286748097 sample58 -0.0064792049 -0.0006945349 sample59 0.0088958941 0.0067389649 sample60 0.0874124612 0.0431964341 sample61 0.0577604571 -0.0326112099 sample62 -0.0313318464 0.0224391756 sample63 -0.0233625220 0.0125110562 sample64 -0.0086426068 0.0148770341 sample65 0.0025256193 -0.0404466327 sample66 0.0006014071 -0.0471576264 sample67 0.0706087042 0.0516228406 sample68 0.0082301011 0.0033109509 sample69 -0.0475076743 0.0001452708 sample70 -0.0600773716 0.0089986962 sample71 -0.0096321627 -0.0050761187 sample72 -0.0031773546 -0.0166221542 sample73 -0.0113700517 -0.0191726684 sample74 -0.0014179662 -0.0608101325 sample75 0.0041911740 -0.0399981269 sample76 -0.0055326449 0.0353114263 sample77 -0.0260214459 0.0305731380 sample78 -0.0119267436 0.0632236007 sample79 0.0186017239 0.0027402910 sample80 0.0241047889 -0.0472697181 sample81 -0.0220288317 -0.0079577210 sample82 -0.0180751258 0.0639051029 sample83 -0.0256671713 -0.0125898269 sample84 0.0161392598 -0.0567222449 sample85 0.0139988188 0.0322763454 sample86 -0.0198382995 0.0389225776 sample87 0.0266270281 -0.0032979996 sample88 0.0515677078 0.0117902495 sample89 0.0014022125 -0.0140510488 sample90 -0.0375949749 0.0044004551 sample91 0.0310397965 0.0440610926 sample92 0.0270570567 0.0324380452 sample93 -0.0215009202 0.0063993941 sample94 -0.0415702912 -0.0037692077 sample95 -0.0168416047 0.0010019120 sample96 -0.0285582661 -0.0187991000 sample97 -0.0490843868 -0.0266760748 sample98 -0.0171579033 -0.0112897471 sample99 -0.0271316525 0.0232395583 sample100 -0.0301789816 0.0305498693 sample101 -0.0264371151 0.0170723968 sample102 0.0012767734 -0.0248949597 sample103 0.0055214687 -0.0030040587 sample104 0.0251346074 -0.0165212671 sample105 0.0062424215 -0.0400309901 sample106 0.0069768684 0.0154982315 sample107 -0.0315912602 -0.0118883820 sample108 -0.0109690679 0.0023637162 sample109 -0.0014762845 0.0165583675 sample110 0.0036971063 0.0168260726 sample111 -0.0071624739 -0.0345651461 sample112 0.0046098120 -0.0048009350 sample113 0.0082236008 -0.0383233357 sample114 -0.0293642209 -0.0165595240 sample115 -0.0003260453 0.0135805368 sample116 0.0183575759 0.0665377581 sample117 0.0227640036 -0.0012287760 sample118 0.0015695248 0.0472617382 sample119 0.0190084932 0.0590034062 sample120 -0.0449645755 0.0072755697 sample121 0.0077307184 0.0104738937 sample122 -0.0027132063 -0.0394983138 sample123 0.0016959300 0.0028593594 sample124 -0.0365091615 0.0040382925 sample125 -0.0053658663 -0.0316029164 sample126 -0.0458032408 0.0019165544 sample127 -0.0494064872 0.0088209044 sample128 -0.0155454766 0.0186819802 sample129 -0.0184340400 0.0038684312 sample130 -0.0303640987 -0.0052225766 sample131 -0.0088697422 0.0156339713 sample132 -0.0433916471 -0.0154075483 sample133 0.0204029276 -0.0282209049 sample134 0.0175513332 0.0262883962 sample135 0.0029009925 0.0017003151 sample136 -0.0367997573 -0.0072249751 sample137 -0.0348600323 0.0075400273 sample138 -0.0044063824 -0.0053752428 sample139 0.0073103935 0.0308956174 sample140 0.0039925654 -0.0167019605 sample141 -0.0184093462 -0.0387953445 sample142 0.0268670676 -0.0239229634 sample143 0.0421049126 -0.0110888235 sample144 0.0017253664 -0.0341766012 sample145 0.0681741320 -0.0073526377 sample146 -0.0239965222 0.0118396767 sample147 -0.0063453522 0.0183130585 sample148 0.0230825251 -0.0379753037 sample149 0.0223298673 0.0188909118 sample150 0.0055709108 0.0174179009 sample151 0.0039177786 -0.0233533275 sample152 0.0134325667 0.0302344591 sample153 0.0511990309 0.0730230140 sample154 0.0006698324 0.0154177486 sample155 0.0032926626 -0.0288651601 sample156 -0.0016463495 -0.0474657733 sample157 -0.0045857599 0.0154934573 sample158 0.0201775524 -0.0332982124 sample159 -0.0086909001 0.0073496711 sample160 0.0295437331 -0.0555734536 sample161 0.0332754288 0.0033779619 sample162 0.0121954537 0.0433540412 sample163 -0.0173490933 0.0227219128 sample164 0.0143374783 -0.0453542590 sample165 0.0343612593 -0.0511194536 sample166 -0.0157536004 0.0094621170 sample167 -0.0179654624 -0.0006982358 sample168 -0.0033829919 0.0060747155 sample169 0.0116231468 -0.0015112800 > > ## 3.3 Plotting VAF > > # DISCO-SCA plotVAF > plotVAF(discoRes) > > # JIVE plotVAF > plotVAF(jiveRes) > > > ######################### > ## PART 4. Plot Results > > # Scores for common part. DISCO-SCA > plotRes(object=discoRes,comps=c(1,2),what="scores",type="common", + combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > > # Scores for common part. JIVE > plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common", + combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > > # Scores for common part. O2PLS. > p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > legend <- g_legend(p1) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + legend,heights=c(6/7,1/7)) > > # Combined plot of scores for common part. O2PLS. > plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common", + combined=TRUE,block=NULL,color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > > > # Scores for distinctive part. DISCO-SCA. (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > legend <- g_legend(p1) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + legend,heights=c(6/7,1/7)) > > # Combined plot of scores for distinctive part. DISCO-SCA > plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual", + combined=TRUE,block=NULL,color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > > # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block) > p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > legend <- g_legend(p1) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + legend,heights=c(6/7,1/7)) > > # Combined plot of scores for common and distinctive part. DISCO (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > legend <- g_legend(p1) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + legend,heights=c(6/7,1/7)) > > # Loadings for common part. DISCO-SCA. (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > # Loadings for distinctive part. DISCO-SCA. (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > # Combined plot for loadings from common and distinctive part (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > > ## Plot scores and loadings togheter: Common components DISCO-SCA > p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common", + combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common", + combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > ## Plot scores and loadings togheter: Common components O2PLS > p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common", + combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common", + combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > ## Plot scores and loadings togheter: Distintive components DISCO-SCA > p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual", + combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual", + combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > > > proc.time() user system elapsed 11.804 0.293 12.086
STATegRa.Rcheck/STATegRa-Ex.timings
name | user | system | elapsed | |
STATegRaUsersGuide | 0.001 | 0.000 | 0.001 | |
STATegRa_data | 0.222 | 0.012 | 0.233 | |
STATegRa_data_TCGA_BRCA | 0.002 | 0.000 | 0.002 | |
bioDist | 0.426 | 0.044 | 0.470 | |
bioDistFeature | 0.317 | 0.055 | 0.373 | |
bioDistFeaturePlot | 0.314 | 0.062 | 0.376 | |
bioDistW | 0.302 | 0.008 | 0.310 | |
bioDistWPlot | 0.318 | 0.000 | 0.318 | |
bioMap | 0.003 | 0.000 | 0.003 | |
combiningMappings | 0.008 | 0.003 | 0.012 | |
createOmicsExpressionSet | 0.139 | 0.001 | 0.140 | |
getInitialData | 0.591 | 0.020 | 0.611 | |
getLoadings | 0.620 | 0.043 | 0.665 | |
getMethodInfo | 0.686 | 0.033 | 0.718 | |
getPreprocessing | 0.782 | 0.639 | 1.422 | |
getScores | 0.707 | 0.059 | 0.766 | |
getVAF | 0.578 | 0.013 | 0.590 | |
holistOmics | 0.002 | 0.000 | 0.002 | |
modelSelection | 1.558 | 1.587 | 3.146 | |
omicsCompAnalysis | 4.049 | 0.081 | 4.130 | |
omicsNPC | 0.002 | 0.000 | 0.003 | |
plotRes | 4.686 | 0.088 | 4.774 | |
plotVAF | 4.214 | 0.036 | 4.251 | |