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This page was generated on 2018-04-12 13:12:53 -0400 (Thu, 12 Apr 2018).
Package 1358/1472 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||
STATegRa 1.12.0 David Gomez-Cabrero
| malbec1 | Linux (Ubuntu 16.04.1 LTS) / x86_64 | NotNeeded | OK | [ OK ] | |||||||
tokay1 | Windows Server 2012 R2 Standard / x64 | NotNeeded | OK | OK | OK | |||||||
veracruz1 | OS X 10.11.6 El Capitan / x86_64 | NotNeeded | OK | OK | OK |
Package: STATegRa |
Version: 1.12.0 |
Command: /home/biocbuild/bbs-3.6-bioc/R/bin/R CMD check --no-vignettes --timings STATegRa_1.12.0.tar.gz |
StartedAt: 2018-04-12 03:11:17 -0400 (Thu, 12 Apr 2018) |
EndedAt: 2018-04-12 03:14:42 -0400 (Thu, 12 Apr 2018) |
EllapsedTime: 205.1 seconds |
RetCode: 0 |
Status: OK |
CheckDir: STATegRa.Rcheck |
Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.6-bioc/R/bin/R CMD check --no-vignettes --timings STATegRa_1.12.0.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/home/biocbuild/bbs-3.6-bioc/meat/STATegRa.Rcheck’ * using R version 3.4.4 (2018-03-15) * using platform: x86_64-pc-linux-gnu (64-bit) * using session charset: UTF-8 * using option ‘--no-vignettes’ * checking for file ‘STATegRa/DESCRIPTION’ ... OK * checking extension type ... Package * this is package ‘STATegRa’ version ‘1.12.0’ * 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 ... NOTE installed size is 5.9Mb sub-directories of 1Mb or more: data 2.4Mb doc 3.0Mb * checking package directory ... OK * checking ‘build’ directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking R files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking 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 biplotRes,caClass-character-numeric-character: no visible binding for global variable ‘values.1’ biplotRes,caClass-character-numeric-character: no visible binding for global variable ‘values.2’ biplotRes,caClass-character-numeric-character: no visible binding for global variable ‘color’ 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,matrix-matrix-numeric: no visible binding for global variable ‘comps’ selectCommonComps,matrix-matrix-numeric: no visible binding for global variable ‘block’ selectCommonComps,matrix-matrix-numeric: no visible binding for global variable ‘comp’ selectCommonComps,matrix-matrix-numeric: no visible binding for global variable ‘ratio’ Undefined global functions or variables: VAF block color comp comps ratio values.1 values.2 * 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 installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK * checking examples ... OK Examples with CPU or elapsed time > 5s user system elapsed biplotRes 5.856 0.016 5.878 plotRes 5.208 0.008 5.218 * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘STATEgRa_Example.omicsCLUST.R’ Running ‘STATEgRa_Example.omicsPCA.R’ Running ‘STATegRa_Example.omicsNPC.R’ Running ‘runTests.R’ OK * checking for unstated dependencies in vignettes ... OK * checking package vignettes in ‘inst/doc’ ... OK * checking running R code from vignettes ... SKIPPED * checking re-building of vignette outputs ... SKIPPED * checking PDF version of manual ... OK * DONE Status: 2 NOTEs See ‘/home/biocbuild/bbs-3.6-bioc/meat/STATegRa.Rcheck/00check.log’ for details.
STATegRa.Rcheck/00install.out
* installing *source* package ‘STATegRa’ ... ** R ** data ** inst ** preparing package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded * DONE (STATegRa)
STATegRa.Rcheck/tests/runTests.Rout
R version 3.4.4 (2018-03-15) -- "Someone to Lean On" Copyright (C) 2018 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > BiocGenerics:::testPackage("STATegRa") RUNIT TEST PROTOCOL -- Thu Apr 12 03:14:39 2018 *********************************************** Number of test functions: 9 Number of errors: 0 Number of failures: 0 1 Test Suite : STATegRa RUnit Tests - 9 test functions, 0 errors, 0 failures Number of test functions: 9 Number of errors: 0 Number of failures: 0 Warning messages: 1: In rownames(pData) == colnames(exprs) : longer object length is not a multiple of shorter object length 2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum", : Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2 3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num", : Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3 > > proc.time() user system elapsed 4.564 0.088 5.087
STATegRa.Rcheck/tests/STATEgRa_Example.omicsCLUST.Rout
R version 3.4.4 (2018-03-15) -- "Someone to Lean On" Copyright (C) 2018 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ########################################### > ########### EXAMPLE OF THE OMICSCLUSTERING > ########################################### > require(STATegRa) Loading required package: STATegRa > > ############################################# > ## PART 1: CREATING a bioMap CLASS > ############################################# > ####### This part creates or reads the map between features. > ####### In the present example the map is downloaded from a resource. > ####### then the class is created. > > #load("../data/STATegRa_S2.rda") > data(STATegRa_S2) > > MAP.SYMBOL<-bioMap(name = "Symbol-miRNA", + metadata = list(type_v1="Gene",type_v2="miRNA", + source_database="targetscan.Hs.eg.db", + data_extraction="July2014"), + map=mapdata) > > > ############################################# > ## PART 2: CREATING a bioDist CLASS > ############################################# > ##### In the second part given a set of main features and surrogate feautres, > ##### the profile of the main features is computed through the surrogate features. > > # Load Data > data(STATegRa_S1) > #load("../data/STATegRa.S1.Rdata") > > ## Create ExpressionSets > # source("../R/STATegRa_omicsPCA_classes_and_methods.R") > # Block1 - Expression data > mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname")) > # Block2 - miRNA expression data > miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname")) > > # Create Gene-gene distance computed through miRNA data > bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1), + reference = "Var1", + mapping = MAP.SYMBOL, + surrogateData = miRNA.ds, ### miRNA data + referenceData = mRNA.ds, ### mRNA data + maxitems=2, + selectionRule="sd", + expfac=NULL, + aggregation = "sum", + distance = "spearman", + noMappingDist = 0, + filtering = NULL, + name = "mRNAbymiRNA") > > require(Biobase) Loading required package: Biobase Loading required package: BiocGenerics Loading required package: parallel Attaching package: 'BiocGenerics' The following objects are masked from 'package:parallel': clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB The following objects are masked from 'package:stats': IQR, mad, sd, var, xtabs The following objects are masked from 'package:base': Filter, Find, Map, Position, Reduce, anyDuplicated, append, as.data.frame, cbind, colMeans, colSums, colnames, do.call, duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which, which.max, which.min Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. > > # Create Gene-gene distance through mRNA data > bioDistmRNA<-bioDistclass(name = "mRNAbymRNA", + distance = cor(t(exprs(mRNA.ds)),method="spearman"), + map.name = "id", + map.metadata = list(), + params = list()) > > ############################################# > ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList > ############################################# > > bioDistList<-list(bioDistmRNA,bioDistmiRNA) > weights<-matrix(0,4,2) > weights[,1]<-c(0,0.33,0.67,1) > weights[,2]<-c(1,0.67,0.33,0)# > > bioDistWList<-bioDistW(referenceFeatures = rownames(Block1), + bioDistList = bioDistList, + weights=weights) > length(bioDistWList) [1] 4 > > ############################################# > ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL > ############################################# > > bioDistWPlot(referenceFeatures = rownames(Block1) , + listDistW = bioDistWList, + method.cor="spearman") Warning messages: 1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], : Cannot compute exact p-value with ties 2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], : Cannot compute exact p-value with ties 3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], : Cannot compute exact p-value with ties 4: In plot.window(...) : relative range of values = 18 * EPS, is small (axis 2) 5: In plot.window(...) : relative range of values = 18 * EPS, is small (axis 2) 6: In plot.window(...) : relative range of values = 18 * EPS, is small (axis 2) 7: In plot.window(...) : relative range of values = 18 * EPS, is small (axis 2) > > ############################################# > ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE > ############################################# > > ## IDH1 > > IDH1.F<-bioDistFeature(Feature = "IDH1" , + listDistW = bioDistWList, + threshold.cor=0.7) > bioDistFeaturePlot(data=IDH1.F) > > ## PDGFRA > > #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" , > # listDistW = bioDistWList, > # threshold.cor=0.7) > #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png") > > ## EGFR > #EGFR.F<-bioDistFeature(Feature = "EGFR" , > # listDistW = bioDistWList, > # threshold.cor=0.7) > #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png") > > ## MGMT > #MGMT.F<-bioDistFeature(Feature = "MGMT" , > # listDistW = bioDistWList, > # threshold.cor=0.5) > #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png") > > > > > > proc.time() user system elapsed 24.340 0.256 25.041
STATegRa.Rcheck/tests/STATegRa_Example.omicsNPC.Rout
R version 3.4.4 (2018-03-15) -- "Someone to Lean On" Copyright (C) 2018 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > rm(list = ls()) > require("STATegRa") Loading required package: STATegRa > # Load the data > data("TCGA_BRCA_Batch_93") > # Setting dataTypes > dataTypes <- c("count", "count", "continuous") > # Setting methods to combine pvalues > combMethods = c("Fisher", "Liptak", "Tippett") > # Setting number of permutations > numPerms = 1000 > # Setting number of cores > numCores = 1 > # Setting holistOmics to print out the steps that it performs. > verbose = TRUE > # Run holistOmics analysis. > output <- omicsNPC(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes, combMethods = combMethods, numPerms = numPerms, numCores = numCores, verbose = verbose) Compute initial statistics on data Building NULL distributions by permuting data Compute pseudo p-values based on NULL distributions... NPC p-values calculation... > > proc.time() user system elapsed 64.324 0.096 64.889
STATegRa.Rcheck/tests/STATEgRa_Example.omicsPCA.Rout
R version 3.4.4 (2018-03-15) -- "Someone to Lean On" Copyright (C) 2018 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ########################################### > ########### EXAMPLE OF THE OMICSPCA > ########################################### > require(STATegRa) Loading required package: STATegRa > > # g_legend (not exported by STATegRa any more) > ## code from https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs > g_legend<-function(a.gplot){ + tmp <- ggplot_gtable(ggplot_build(a.gplot)) + leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box") + legend <- tmp$grobs[[leg]] + return(legend)} > > ######################### > ## PART 1. Load data > > ## Load data > data(STATegRa_S3) > > ls() [1] "Block1.PCA" "Block2.PCA" "ed.PCA" "g_legend" > > ## Create ExpressionSets > # Block1 - Expression data > B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname")) > # Block2 - miRNA expression data > B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname")) > > ######################### > ## PART 2. Model Selection > > require(grid) Loading required package: grid > require(gridExtra) Loading required package: gridExtra > require(ggplot2) Loading required package: ggplot2 > > ## 2.1 Select common components > cc <- selectCommonComps(X=Block1.PCA,Y=Block2.PCA,Rmax=3) > cc$common [1] 2 > cc$pssq > cc$pratios > #png("modelSelection.png",width=822,height=416) > grid.arrange(cc$pssq,cc$pratios,ncol=2) > #dev.off() > > ## 2.2 Select distinctive components > # Block 1 > PCA.selection(Data=Block1.PCA,fac.sel="single%",varthreshold=0.03)$numComps [1] 4 > # Block2 > PCA.selection(Data=Block2.PCA,fac.sel="single%",varthreshold=0.03)$numComps [1] 4 > > ## 2.3 Optimal components analysis > ms <- modelSelection(Input=list(B1,B2),Rmax=4,fac.sel="single%",varthreshold=0.03) > ms $common [1] 2 $dist [1] 2 2 > > ######################### > ## PART 3. Component Analysis > > ## 3.1 Component analysis of the three methods > discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=ms$common,Rspecific=ms$dist,center=TRUE, + scale=TRUE,weight=TRUE) > jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=ms$common,Rspecific=ms$dist,center=TRUE, + scale=TRUE,weight=TRUE) > o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=ms$common,Rspecific=ms$dist,center=TRUE, + scale=TRUE,weight=TRUE) > > ## 3.2 Exploring scores structures > > # Exploring DISCO-SCA scores structure > discoRes@scores$common ## Common scores 1 2 sample1 0.0781575592 -0.0431547778 sample2 -0.1192221304 0.0294022975 sample3 -0.0531408767 -0.0746837645 sample4 0.0292971865 -0.0006032699 sample5 0.0202090774 0.0110455408 sample6 0.1226088463 0.1053492734 sample7 0.1078931262 -0.0322420061 sample8 0.1782891242 0.1449330997 sample9 0.0468697308 -0.0455171758 sample10 -0.0036032635 0.0420078345 sample11 -0.0035566368 -0.0566285242 sample12 0.1006129661 0.0641393817 sample13 -0.1174412701 0.0907475627 sample14 0.0981203552 0.0617763150 sample15 0.0085337189 -0.0086957083 sample16 0.0783146863 0.1581332876 sample17 -0.1483610647 0.0638580161 sample18 -0.0963084438 0.0556687175 sample19 -0.0217243098 -0.0720127927 sample20 -0.0635633993 -0.0779610904 sample21 -0.0201843905 0.1566382146 sample22 0.0218273721 -0.0764056574 sample23 0.0852039116 -0.0032763813 sample24 -0.1287181245 0.1924428066 sample25 -0.0430575606 -0.0456637505 sample26 -0.1453899714 0.0541459661 sample27 -0.0197483737 -0.1185593836 sample28 -0.1025339350 0.0650655819 sample29 0.0706022363 -0.0682933767 sample30 -0.1295623087 -0.0066679046 sample31 0.1147449308 0.1232726647 sample32 -0.0374308274 0.0380248499 sample33 0.0599520656 0.0136935094 sample34 -0.0984199341 0.0375363963 sample35 -0.0543096464 -0.0378036241 sample36 0.1403627989 -0.0343640713 sample37 0.0228947544 -0.0732691950 sample38 -0.0222073127 -0.0962567635 sample39 -0.0941739202 0.0215180821 sample40 0.0643806941 -0.0687722115 sample41 -0.0327635049 -0.1232187220 sample42 -0.0500431638 -0.0292513181 sample43 -0.0184497219 0.0233043898 sample44 0.1487889570 0.1171212294 sample45 -0.1050778685 0.1123141282 sample46 -0.1151191695 -0.1093996252 sample47 -0.0962591578 -0.0288418701 sample48 0.0004832723 -0.0310377911 sample49 0.1135203967 0.1213937135 sample50 -0.0123550002 -0.1740762667 sample51 0.0550527499 0.1258930143 sample52 0.0499118554 0.0728580423 sample53 0.1119772693 0.1588063564 sample54 -0.0360055715 0.0228585487 sample55 0.0210418834 0.0006750448 sample56 -0.0434171442 0.0633131191 sample57 0.0197820785 0.1150753372 sample58 0.0030440687 0.0326127134 sample59 0.0500256661 0.0129520152 sample60 0.0184280057 0.0136216183 sample61 0.0150298936 0.0635095917 sample62 -0.0304758869 -0.0201237312 sample63 0.1102250175 0.1285968502 sample64 0.1552586858 0.0971185003 sample65 -0.0058503801 0.0207102919 sample66 -0.0025607421 0.0424285080 sample67 0.1546638586 -0.0661580192 sample68 0.0536374141 -0.0923605238 sample69 0.0640332938 0.0082003569 sample70 0.0163521644 -0.0663227241 sample71 -0.0102536154 -0.1345964309 sample72 -0.0654191850 -0.0196037405 sample73 -0.1048553309 0.0220999166 sample74 0.0123800483 0.0586156284 sample75 0.0392079697 -0.0209726592 sample76 0.0648954549 -0.0524759548 sample77 0.1172922638 -0.0201200154 sample78 -0.1463072519 0.0708400604 sample79 0.0265208886 -0.1603423752 sample80 0.0279739170 -0.0214154094 sample81 0.0079212134 -0.0738494864 sample82 -0.1544234642 -0.0361450828 sample83 -0.0494205548 -0.0049940464 sample84 -0.0259039702 -0.0346590976 sample85 0.1116487378 -0.0031405512 sample86 -0.1306479143 -0.0377156818 sample87 -0.0554777870 -0.0459739769 sample88 -0.0301626465 0.0382206585 sample89 -0.1016866199 0.0694077603 sample90 0.0086821602 -0.0201323757 sample91 0.1578629671 -0.2097792705 sample92 0.0170933579 -0.1655934530 sample93 -0.0979805138 -0.0121500634 sample94 0.0131486170 -0.0114929497 sample95 0.0315682463 -0.0758916291 sample96 0.0024125833 -0.0470184459 sample97 0.0634545797 0.0270304114 sample98 -0.0359372602 -0.0135466818 sample99 -0.1009167517 0.1124713654 sample100 0.0551754073 0.0246501941 sample101 -0.0080116063 -0.1627406661 sample102 -0.0046451030 0.0095475352 sample103 -0.0472520921 -0.0940383671 sample104 0.0198157501 -0.0591146353 sample105 -0.0400238963 -0.0160948543 sample106 -0.0923810100 0.0369004075 sample107 -0.1019372404 0.0224967103 sample108 -0.0877091517 -0.0128849487 sample109 0.0864820367 -0.0901078825 sample110 -0.1223116458 -0.0096108154 sample111 0.0257352481 -0.0936279413 sample112 -0.0765285935 0.0270378858 sample113 0.0258799925 0.0377439324 sample114 0.0021141043 -0.0882039751 sample115 0.0303455399 -0.0723732867 sample116 0.0780504550 -0.0685160814 sample117 0.0536894158 -0.0912023529 sample118 0.0666649921 -0.0236260597 sample119 0.1021872545 -0.2325002709 sample120 0.0750216336 0.0243346186 sample121 -0.0756937850 0.0942970120 sample122 -0.0259631973 0.0731922252 sample123 -0.1037844721 -0.0369179034 sample124 0.0611205232 0.0421648299 sample125 -0.0738472621 0.0066944363 sample126 0.0972919072 0.0762697913 sample127 0.0824699397 -0.0096644555 sample128 -0.1249411438 0.0929254765 sample129 -0.0734063780 -0.0434314395 sample130 -0.0003500292 -0.0309857132 sample131 0.0930184021 0.0155969564 sample132 0.0736220655 0.0732973080 sample133 -0.0498398350 -0.0462455644 sample134 0.1644872648 0.0720046275 sample135 -0.0752295092 0.0003868762 sample136 0.0227149877 -0.0495470019 sample137 0.0564721612 -0.0288861501 sample138 0.0255986521 -0.0610929363 sample139 0.0621218758 0.0235856967 sample140 -0.0604149025 -0.0435533346 sample141 0.0246743065 0.0532630629 sample142 -0.0409563788 0.0316235012 sample143 -0.0077356364 -0.0476908638 sample144 0.0173241001 -0.0156785739 sample145 0.0485467864 0.1202738457 sample146 0.0419649883 -0.0811241581 sample147 -0.0977304732 -0.0274772415 sample148 0.0368253352 0.0803969674 sample149 -0.0072864897 -0.1533016497 sample150 0.1020825509 0.0624821763 sample151 0.0305397204 -0.0289336064 sample152 -0.0533595202 -0.0638334607 sample153 -0.0891639171 0.1799455257 sample154 -0.0727554468 -0.0834129938 sample155 -0.0880665882 -0.0220771183 sample156 -0.0276558898 -0.0326602207 sample157 -0.1155031577 0.0183635192 sample158 -0.0281506718 -0.0104912391 sample159 0.0663233821 0.0443810186 sample160 -0.0302644004 0.0404300737 sample161 0.0114713014 -0.0591082171 sample162 -0.1337090915 0.1398131489 sample163 0.1330120834 0.1688769521 sample164 -0.0150338099 0.0028376065 sample165 0.0076518870 -0.0164145545 sample166 0.0367791560 0.0630615019 sample167 0.1111989797 0.0030066301 sample168 -0.0672982946 0.0446266937 sample169 -0.0413003666 0.0224446195 > discoRes@scores$dist[[1]] ## Distinctive scores for Block 1 1 2 sample1 0.0420464492 0.0867866014 sample2 0.0820848922 -0.0410969121 sample3 -0.0155963742 -0.0195186220 sample4 0.1001342563 -0.0410776709 sample5 0.0153479198 -0.0253257795 sample6 -0.0340242320 -0.0408223364 sample7 -0.0722601559 0.0002324192 sample8 0.0457615054 -0.0370007235 sample9 0.0086217892 0.0820184504 sample10 0.0423629982 -0.0083917828 sample11 -0.0022591799 0.0787764175 sample12 -0.0322077267 0.1479823437 sample13 0.0293967251 -0.0306743080 sample14 -0.0337432815 -0.0367508310 sample15 -0.0815559432 0.1275614127 sample16 -0.0508336471 0.0540604356 sample17 -0.0062556768 0.0041024832 sample18 -0.0705602312 -0.0351053170 sample19 0.0476785850 -0.0509595524 sample20 -0.0523024762 0.0715514319 sample21 0.0119246398 -0.0376087284 sample22 -0.0724455592 -0.0095634593 sample23 0.0992529634 0.0134298636 sample24 0.1595260066 0.0728683494 sample25 0.0920662282 -0.0749749518 sample26 0.0595566018 0.0848973419 sample27 -0.0826573655 -0.0086747168 sample28 0.0384831945 0.0440972541 sample29 -0.0777738333 0.1735298824 sample30 -0.1229474222 -0.0819018069 sample31 -0.0579754097 -0.0238646792 sample32 -0.0970366773 -0.0111434860 sample33 -0.1017580410 -0.0630452273 sample34 -0.0637903419 0.0377936384 sample35 -0.0790002670 -0.0229732200 sample36 -0.1224933285 -0.1274967937 sample37 -0.1798846446 -0.1673447402 sample38 -0.0466390554 0.0888153417 sample39 0.0168694445 0.0421535972 sample40 -0.1756416972 -0.1526661747 sample41 -0.0042465666 0.0004924697 sample42 0.0447826417 -0.0651501497 sample43 -0.0482292632 -0.0253533405 sample44 0.1986815542 -0.0545754511 sample45 0.0741915033 0.0054713876 sample46 -0.0478858740 -0.0007080128 sample47 -0.0608215555 0.0481615660 sample48 0.1381465664 0.0578300611 sample49 0.0530626794 -0.1405523875 sample50 0.0173652272 0.1602386229 sample51 -0.0462460547 0.0303473058 sample52 -0.0279998613 0.0280387881 sample53 -0.0667503303 0.0237700203 sample54 -0.0121812515 -0.0521354887 sample55 -0.0182392359 0.0221326688 sample56 0.0001306895 0.0030909235 sample57 -0.0316578552 0.0530190637 sample58 -0.0393892035 -0.0297801698 sample59 -0.1278272120 -0.0546540214 sample60 -0.1486965379 0.1069142294 sample61 -0.0793069600 0.0569790592 sample62 -0.1172821095 -0.0149210793 sample63 0.0028809412 0.1300524006 sample64 -0.0237298844 0.1073288372 sample65 0.0126543299 0.0589810301 sample66 0.0468232397 -0.0771066791 sample67 -0.1494285134 -0.0769876882 sample68 -0.0978021130 -0.0577363445 sample69 -0.0403090370 0.0156038368 sample70 -0.0221595463 0.0315436751 sample71 0.0546334013 -0.0272395012 sample72 -0.1107500527 -0.0537331024 sample73 -0.0906756862 0.0579958142 sample74 -0.0586515013 0.0121417586 sample75 -0.0390511923 0.0349278350 sample76 0.0022940387 -0.1676560061 sample77 0.0232101290 -0.2067301000 sample78 0.0929807736 -0.0434928295 sample79 0.1619385249 -0.0378102846 sample80 -0.0680391757 0.1424656146 sample81 0.0530727776 -0.0358347773 sample82 -0.0266849347 -0.0577448982 sample83 -0.1517241786 -0.0448569634 sample84 0.0570944118 -0.0273808606 sample85 -0.1086272196 -0.1228130098 sample86 -0.0833890432 -0.0442924523 sample87 -0.0022039907 -0.0943908460 sample88 0.0078274868 -0.1140504601 sample89 -0.0611007846 -0.0094589264 sample90 -0.0022941101 -0.0936254844 sample91 -0.0433766271 0.3205972449 sample92 0.1815222229 -0.0334667107 sample93 -0.0267653187 0.0614425896 sample94 -0.0181900590 0.0605088232 sample95 0.0720316645 -0.0013040727 sample96 0.0559674274 -0.0118787272 sample97 0.0217420258 0.0195417115 sample98 -0.0379198457 0.0588352886 sample99 0.0792505164 -0.0151262715 sample100 -0.0222100945 -0.0023322886 sample101 0.0387089670 0.1224225219 sample102 0.2094625573 -0.0516421522 sample103 -0.0138555080 0.0301047756 sample104 0.0807949495 -0.0162712595 sample105 0.0520491930 -0.1229660502 sample106 0.0192641861 -0.0185235257 sample107 -0.0319014485 0.0405120658 sample108 0.0140674805 0.0163422307 sample109 0.1831859683 0.0613023184 sample110 0.0292782929 -0.0199846564 sample111 0.1423176700 0.0327351720 sample112 -0.0426313999 -0.0029086945 sample113 0.0771931104 0.0268742466 sample114 0.0241570659 -0.0184080651 sample115 0.1958958293 0.0460148041 sample116 0.1394438824 -0.0530793877 sample117 0.1672313595 -0.1386522404 sample118 0.0448332062 -0.0117618108 sample119 0.0910199785 0.2217435671 sample120 0.0331404492 -0.0057270454 sample121 -0.0307518517 0.1392506216 sample122 0.0839835918 -0.0291983936 sample123 -0.0239674505 -0.0642167295 sample124 0.0909175545 0.0130429809 sample125 0.0065362027 -0.1092631042 sample126 -0.0935274513 0.1368277052 sample127 -0.0035405154 0.0292755028 sample128 0.0660348546 0.1018575510 sample129 -0.0693670140 -0.0695430006 sample130 -0.0008516403 -0.0669705359 sample131 -0.0431012311 0.0174061123 sample132 0.0637087572 0.0029383251 sample133 0.0289465505 -0.0390817351 sample134 -0.0446143815 0.0456332373 sample135 -0.0712343586 0.0521627817 sample136 -0.0596317118 0.0197291913 sample137 -0.0793174842 -0.0380637028 sample138 0.0973506816 -0.0454210371 sample139 -0.0539867170 -0.1534331966 sample140 -0.0850870851 0.0955804729 sample141 0.0192722687 -0.0554446573 sample142 0.0672293503 -0.0461313308 sample143 0.0303707793 -0.0519258597 sample144 0.0089350956 0.0145815354 sample145 0.0638873926 0.0122268432 sample146 -0.0585920763 0.0063075139 sample147 -0.0894146420 -0.1124625501 sample148 0.0216437626 -0.0615962541 sample149 0.0515319358 -0.0839902889 sample150 -0.0568230279 -0.0124472622 sample151 0.0789514184 -0.0261824160 sample152 0.0330694985 0.1306444951 sample153 0.1752061618 0.1497754825 sample154 -0.0421487966 -0.0037016920 sample155 -0.0680198232 0.0095703732 sample156 -0.0388949168 0.1057558094 sample157 -0.0314765217 0.0561364727 sample158 -0.0329629994 0.0353943725 sample159 0.0398460403 -0.1007368443 sample160 -0.0424906680 0.0108493144 sample161 0.0888341108 -0.0679693043 sample162 0.0027568260 0.1237848166 sample163 0.0126226047 0.0725440707 sample164 0.0566786999 -0.0458318464 sample165 0.0315331693 -0.0236359663 sample166 0.0612107887 -0.0425225063 sample167 -0.0142729568 0.0179307031 sample168 0.0169541950 -0.0769614956 sample169 -0.0675063850 0.0131499254 > discoRes@scores$dist[[2]] ## Distinctive scores for Block 2 1 2 sample1 -0.0012331623 1.635716e-01 sample2 -0.0724353150 6.022111e-03 sample3 -0.0188459952 1.080029e-01 sample4 0.0390143170 -3.106692e-04 sample5 0.1774810649 2.996428e-02 sample6 -0.0451446417 3.455897e-02 sample7 -0.0226463539 7.019231e-03 sample8 -0.1033684469 9.857923e-03 sample9 0.1350014167 -8.979113e-02 sample10 0.1259884476 5.097935e-02 sample11 0.0979790871 -7.086566e-02 sample12 -0.0863020945 8.620322e-02 sample13 -0.1381401818 -1.827998e-01 sample14 -0.0615074718 2.642808e-02 sample15 0.0381600556 3.101603e-02 sample16 -0.0048779337 -1.271041e-03 sample17 -0.0788483179 1.547605e-02 sample18 -0.0884189480 3.795477e-02 sample19 0.0703043536 1.084003e-01 sample20 -0.0025581437 -7.975968e-02 sample21 0.0941596739 4.126890e-02 sample22 -0.0550270912 7.806620e-02 sample23 0.0679492867 4.102075e-02 sample24 -0.1310969312 -1.649283e-01 sample25 0.0113583598 4.426899e-02 sample26 -0.1402948850 -2.016461e-02 sample27 0.0261565949 -1.589927e-03 sample28 -0.0724200744 -5.850512e-02 sample29 -0.0330054844 -2.062051e-03 sample30 -0.0228750374 2.015346e-02 sample31 -0.0635070357 6.670368e-02 sample32 0.0685099999 4.955247e-02 sample33 -0.0777764930 1.272070e-01 sample34 0.0157842094 3.024311e-02 sample35 -0.0529628028 -1.500981e-01 sample36 0.0070907836 -2.025320e-01 sample37 -0.0442411977 -1.802109e-01 sample38 -0.0781508434 3.676301e-02 sample39 0.0120330104 3.388883e-02 sample40 -0.0473284001 -1.471581e-01 sample41 0.0228192136 2.673460e-02 sample42 -0.0245361828 7.960877e-02 sample43 0.1036362031 8.229577e-02 sample44 -0.1012234665 -7.049245e-02 sample45 0.0013726800 2.451066e-02 sample46 -0.0558506522 -2.948556e-03 sample47 -0.0380478764 -4.554235e-02 sample48 0.0784340492 -4.888894e-02 sample49 -0.0605167991 1.162469e-02 sample50 0.0530082843 2.737816e-02 sample51 0.1514645376 -5.678262e-02 sample52 0.1860935995 -1.246711e-01 sample53 -0.0064179652 2.701058e-02 sample54 0.0697037579 2.308412e-02 sample55 0.1633577692 -1.366433e-02 sample56 0.1011484019 -4.682135e-02 sample57 0.1730374401 -1.609594e-01 sample58 -0.0071384884 1.666951e-02 sample59 -0.0030458536 -3.005374e-02 sample60 0.0215842051 -2.665887e-01 sample61 0.1510585301 -1.002384e-01 sample62 -0.0925531629 4.845731e-02 sample63 -0.0596315365 4.137107e-02 sample64 -0.0449227252 2.600951e-03 sample65 0.0939382279 4.406949e-02 sample66 0.1063397797 5.710076e-02 sample67 -0.0201580998 -2.361746e-01 sample68 0.0037208287 -2.418539e-02 sample69 -0.0645161979 1.155618e-01 sample70 -0.1013439751 1.351780e-01 sample71 -0.0016466141 2.976775e-02 sample72 0.0328895361 2.835773e-02 sample73 0.0275080385 5.148153e-02 sample74 0.1341718396 7.895302e-02 sample75 0.0951576626 3.943149e-02 sample76 -0.0864719988 -3.035052e-02 sample77 -0.1035749506 2.545326e-02 sample78 -0.1575647797 -4.939478e-02 sample79 0.0189138346 -4.874690e-02 sample80 0.1384142728 -4.313960e-05 sample81 -0.0118846661 6.357909e-02 sample82 -0.1675306669 -3.533967e-02 sample83 -0.0065671196 7.812500e-02 sample84 0.1486890678 3.109095e-02 sample85 -0.0532720426 -7.417986e-02 sample86 -0.1138474963 1.822564e-05 sample87 0.0432865909 -6.080499e-02 sample88 0.0433451178 -1.402486e-01 sample89 0.0331204823 1.395428e-02 sample90 -0.0607413462 8.610386e-02 sample91 -0.0566263933 -1.303769e-01 sample92 -0.0359580739 -1.061605e-01 sample93 -0.0433646453 4.443610e-02 sample94 -0.0477292110 1.059571e-01 sample95 -0.0249595922 3.980510e-02 sample96 0.0035217610 9.293931e-02 sample97 -0.0066051935 1.527234e-01 sample98 0.0020367061 5.579516e-02 sample99 -0.0886621613 3.728370e-02 sample100 -0.1091259592 3.560402e-02 sample101 -0.0739723888 4.317888e-02 sample102 0.0574455819 2.784082e-02 sample103 0.0142733694 -9.706333e-03 sample104 0.0710395573 -4.068331e-02 sample105 0.0980829971 3.452996e-02 sample106 -0.0254260469 -3.628934e-02 sample107 -0.0160655005 9.173398e-02 sample108 -0.0200988297 2.379699e-02 sample109 -0.0389781918 -1.692313e-02 sample110 -0.0326305242 -2.988087e-02 sample111 0.0676935970 6.038248e-02 sample112 0.0167883511 -5.336924e-03 sample113 0.0969214018 2.757701e-02 sample114 -0.0026397983 9.209103e-02 sample115 -0.0308049522 -1.603746e-02 sample116 -0.1240306414 -1.272998e-01 sample117 0.0334728657 -5.392663e-02 sample118 -0.1037152175 -6.252439e-02 sample119 -0.1064170717 -1.196217e-01 sample120 -0.0771357653 1.004935e-01 sample121 -0.0129352281 -3.181916e-02 sample122 0.0847487659 5.568460e-02 sample123 -0.0041335543 -7.693542e-03 sample124 -0.0583462115 8.396474e-02 sample125 0.0634843282 5.232567e-02 sample126 -0.0662582071 1.091730e-01 sample127 -0.0865025593 1.094173e-01 sample128 -0.0627821932 1.471090e-02 sample129 -0.0336274627 4.007777e-02 sample130 -0.0293518105 8.046087e-02 sample131 -0.0469196811 2.209394e-03 sample132 -0.0241745527 1.248608e-01 sample133 0.0907303787 -1.466698e-02 sample134 -0.0350841250 -7.539660e-02 sample135 0.0001334851 -9.185807e-03 sample136 -0.0335874831 9.860184e-02 sample137 -0.0640147304 7.554374e-02 sample138 0.0060964064 1.742782e-02 sample139 -0.0592082798 -5.615005e-02 sample140 0.0427988549 1.099468e-02 sample141 0.0618793340 9.301100e-02 sample142 0.0898552550 -3.573326e-02 sample143 0.0817391023 -8.880528e-02 sample144 0.0787754482 3.821395e-02 sample145 0.1085819553 -1.569461e-01 sample146 -0.0589555065 4.373240e-02 sample147 -0.0495327964 -7.278036e-03 sample148 0.1161590536 -9.078161e-03 sample149 -0.0121575591 -7.788460e-02 sample150 -0.0314511990 -3.520220e-02 sample151 0.0575380971 1.945391e-02 sample152 -0.0494540398 -7.025565e-02 sample153 -0.0941338416 -2.153271e-01 sample154 -0.0335928899 -2.078822e-02 sample155 0.0690459016 2.780363e-02 sample156 0.1039902290 6.292489e-02 sample157 -0.0408645839 -8.065529e-03 sample158 0.1018106320 -7.817018e-03 sample159 -0.0281732485 1.207259e-02 sample160 0.1643052868 -2.977818e-03 sample161 0.0374330062 -8.524589e-02 sample162 -0.0804538202 -8.349638e-02 sample163 -0.0743232298 1.406343e-02 sample164 0.1208804318 2.139522e-02 sample165 0.1608115953 -2.025160e-02 sample166 -0.0425947861 2.660798e-02 sample167 -0.0226849506 4.464258e-02 sample168 -0.0180737328 7.471410e-04 sample169 0.0190780182 -2.645426e-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="",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="",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="",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > > # Combined plot of scores for common part. DISCO. > plotRes(object=discoRes,comps=c(1,1),what="scores",type="common", + combined=TRUE,block="",color="classname") Warning message: In plotRes(object = discoRes, comps = c(1, 1), what = "scores", : It is not possible to combine common components in DISCO-SCA approach. > > > # 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="",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=TRUE,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=TRUE,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=TRUE,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=TRUE,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)) > > # Combined plot for loadings for common part. DISCO-SCA. > plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common", + combined=TRUE,block="",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > > # 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 for distinctive part > plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual", + combined=TRUE,block="",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > > # Combined plot for common and distinctive part (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both", + combined=TRUE,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=TRUE,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)) > > > > ######################### > ## PART 5. Biplot results > > ## Common components DISCO-SCA > biplotRes(object=discoRes,type="common",comps=c(1,2),block="",title=NULL, + colorCol="classname",sizeValues=c(2,4),shapeValues=c(17,0), + background=TRUE,pointSize=4,labelSize=NULL,axisSize=NULL, + titleSize=NULL) > > > ## Common components O2PLS > p1 <- biplotRes(object=o2plsRes,type="common",comps=c(1,2),block="expr",title=NULL, + colorCol="classname",sizeValues=c(2,4),shapeValues=c(17,0), + background=TRUE,pointSize=4,labelSize=NULL,axisSize=NULL, + titleSize=NULL) Warning message: In data.row.names(row.names, rowsi, i) : some row.names duplicated: 170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338 --> row.names NOT used > p2 <- biplotRes(object=o2plsRes,type="common",comps=c(1,2),block="mirna",title=NULL, + colorCol="classname",sizeValues=c(2,4),shapeValues=c(17,0), + background=TRUE,pointSize=4,labelSize=NULL,axisSize=NULL,titleSize=NULL) Warning message: In data.row.names(row.names, rowsi, i) : some row.names duplicated: 170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338 --> row.names NOT used > 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)) > > ## Distintive components DISCO-SCA > p1 <- biplotRes(object=discoRes,type="individual",comps=c(1,2),block="expr",title=NULL, + colorCol="classname",sizeValues=c(2,4),shapeValues=c(17,0), + background=TRUE,pointSize=4,labelSize=NULL,axisSize=NULL, + titleSize=NULL) Warning message: In data.row.names(row.names, rowsi, i) : some row.names duplicated: 170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338 --> row.names NOT used > p2 <- biplotRes(object=discoRes,type="individual",comps=c(1,2),block="mirna",title=NULL, + colorCol="classname",sizeValues=c(2,4),shapeValues=c(17,0), + background=TRUE,pointSize=4,labelSize=NULL,axisSize=NULL, + titleSize=NULL) Warning message: In data.row.names(row.names, rowsi, i) : some row.names duplicated: 170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338 --> row.names NOT used > 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)) > > > > proc.time() user system elapsed 16.124 0.148 16.787
STATegRa.Rcheck/STATegRa-Ex.timings
name | user | system | elapsed | |
PCA.selection | 0.236 | 0.008 | 0.244 | |
STATegRaUsersGuide | 0 | 0 | 0 | |
STATegRa_data | 0.220 | 0.008 | 0.227 | |
STATegRa_data_TCGA_BRCA | 0.004 | 0.000 | 0.003 | |
bioDist | 0.700 | 0.012 | 0.715 | |
bioDistFeature | 1.164 | 0.000 | 1.168 | |
bioDistFeaturePlot | 0.32 | 0.00 | 0.32 | |
bioDistW | 0.420 | 0.004 | 0.425 | |
bioDistWPlot | 0.320 | 0.004 | 0.326 | |
bioMap | 0.000 | 0.000 | 0.004 | |
biplotRes | 5.856 | 0.016 | 5.878 | |
combiningMappings | 0.100 | 0.000 | 0.101 | |
createOmicsExpressionSet | 0.200 | 0.000 | 0.199 | |
getInitialData | 0.932 | 0.004 | 0.937 | |
getLoadings | 1.328 | 0.636 | 1.967 | |
getMethodInfo | 0.604 | 0.000 | 0.602 | |
getPreprocessing | 0.752 | 0.164 | 0.914 | |
getScores | 0.604 | 0.004 | 0.608 | |
getVAF | 0.596 | 0.000 | 0.598 | |
holistOmics | 0.004 | 0.000 | 0.003 | |
modelSelection | 0.376 | 0.000 | 0.372 | |
omicsCompAnalysis | 4.012 | 0.000 | 4.016 | |
omicsNPC | 0.004 | 0.000 | 0.003 | |
plotRes | 5.208 | 0.008 | 5.218 | |
plotVAF | 4.936 | 0.000 | 4.939 | |
selectCommonComps | 0.624 | 0.004 | 0.630 | |