Back to Multiple platform build/check report for BioC 3.19: simplified long |
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This page was generated on 2024-05-30 11:35:57 -0400 (Thu, 30 May 2024).
Hostname | OS | Arch (*) | R version | Installed pkgs |
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nebbiolo1 | Linux (Ubuntu 22.04.3 LTS) | x86_64 | 4.4.0 (2024-04-24) -- "Puppy Cup" | 4753 |
lconway | macOS 12.7.1 Monterey | x86_64 | 4.4.0 (2024-04-24) -- "Puppy Cup" | 4518 |
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 | |||||||||
lconway | macOS 12.7.1 Monterey / x86_64 | OK | OK | OK | OK | |||||||||
kjohnson3 | macOS 13.6.5 Ventura / arm64 | see weekly results here | ||||||||||||
To the developers/maintainers of the STATegRa package: - Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/STATegRa.git to reflect on this report. See Troubleshooting Build Report for more information. - Use the following Renviron settings to reproduce errors and warnings. - If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information. |
Package: STATegRa |
Version: 1.40.0 |
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings STATegRa_1.40.0.tar.gz |
StartedAt: 2024-05-30 00:06:08 -0400 (Thu, 30 May 2024) |
EndedAt: 2024-05-30 00:10:06 -0400 (Thu, 30 May 2024) |
EllapsedTime: 237.9 seconds |
RetCode: 0 |
Status: OK |
CheckDir: STATegRa.Rcheck |
Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings STATegRa_1.40.0.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/Users/biocbuild/bbs-3.19-bioc/meat/STATegRa.Rcheck’ * using R version 4.4.0 (2024-04-24) * using platform: x86_64-apple-darwin20 * R was compiled by Apple clang version 14.0.0 (clang-1400.0.29.202) GNU Fortran (GCC) 12.2.0 * running under: macOS Monterey 12.7.1 * using session charset: UTF-8 * using option ‘--no-vignettes’ * checking for file ‘STATegRa/DESCRIPTION’ ... OK * checking extension type ... Package * this is package ‘STATegRa’ version ‘1.40.0’ * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package ‘STATegRa’ can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking ‘build’ directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... NOTE modelSelection,list-numeric-character: no visible binding for global variable ‘components’ modelSelection,list-numeric-character: no visible binding for global variable ‘mylabel’ plotVAF,caClass: no visible binding for global variable ‘comp’ plotVAF,caClass: no visible binding for global variable ‘VAF’ plotVAF,caClass: no visible binding for global variable ‘block’ selectCommonComps,list-numeric: no visible binding for global variable ‘comps’ selectCommonComps,list-numeric: no visible binding for global variable ‘block’ selectCommonComps,list-numeric: no visible binding for global variable ‘comp’ selectCommonComps,list-numeric: no visible binding for global variable ‘ratio’ Undefined global functions or variables: VAF block comp components comps mylabel ratio * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of ‘data’ directory ... OK * checking data for non-ASCII characters ... OK * checking data for ASCII and uncompressed saves ... OK * checking files in ‘vignettes’ ... OK * checking examples ... OK Examples with CPU (user + system) or elapsed time > 5s user system elapsed plotRes 5.523 0.248 5.826 plotVAF 4.967 0.237 5.245 * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘STATEgRa_Example.omicsCLUST.R’ Running ‘STATEgRa_Example.omicsPCA.R’ Running ‘STATegRa_Example.omicsNPC.R’ Running ‘runTests.R’ OK * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking running R code from vignettes ... SKIPPED * checking re-building of vignette outputs ... SKIPPED * checking PDF version of manual ... OK * DONE Status: 1 NOTE See ‘/Users/biocbuild/bbs-3.19-bioc/meat/STATegRa.Rcheck/00check.log’ for details.
STATegRa.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL STATegRa ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/library’ * installing *source* package ‘STATegRa’ ... ** using staged installation ** R ** data ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (STATegRa)
STATegRa.Rcheck/tests/runTests.Rout
R version 4.4.0 (2024-04-24) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > BiocGenerics:::testPackage("STATegRa") Common components [1] 2 Distinctive components [[1]] [1] 0 [[2]] [1] 0 Common components [1] 2 Distinctive components [[1]] [1] 1 [[2]] [1] 1 Common components [1] 2 Distinctive components [[1]] [1] 2 [[2]] [1] 2 RUNIT TEST PROTOCOL -- Thu May 30 00:09:56 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 3.769 0.351 4.147
STATegRa.Rcheck/tests/STATEgRa_Example.omicsCLUST.Rout
R version 4.4.0 (2024-04-24) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ########################################### > ########### EXAMPLE OF THE OMICSCLUSTERING > ########################################### > require(STATegRa) Loading required package: STATegRa > > ############################################# > ## PART 1: CREATING a bioMap CLASS > ############################################# > ####### This part creates or reads the map between features. > ####### In the present example the map is downloaded from a resource. > ####### then the class is created. > > #load("../data/STATegRa_S2.rda") > data(STATegRa_S2) > > MAP.SYMBOL<-bioMap(name = "Symbol-miRNA", + metadata = list(type_v1="Gene",type_v2="miRNA", + source_database="targetscan.Hs.eg.db", + data_extraction="July2014"), + map=mapdata) > > > ############################################# > ## PART 2: CREATING a bioDist CLASS > ############################################# > ##### In the second part given a set of main features and surrogate feautres, > ##### the profile of the main features is computed through the surrogate features. > > # Load Data > data(STATegRa_S1) > #load("../data/STATegRa.S1.Rdata") > > ## Create ExpressionSets > # source("../R/STATegRa_omicsPCA_classes_and_methods.R") > # Block1 - Expression data > mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname")) > # Block2 - miRNA expression data > miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname")) > > # Create Gene-gene distance computed through miRNA data > bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1), + reference = "Var1", + mapping = MAP.SYMBOL, + surrogateData = miRNA.ds, ### miRNA data + referenceData = mRNA.ds, ### mRNA data + maxitems=2, + selectionRule="sd", + expfac=NULL, + aggregation = "sum", + distance = "spearman", + noMappingDist = 0, + filtering = NULL, + name = "mRNAbymiRNA") > > require(Biobase) Loading required package: Biobase Loading required package: BiocGenerics Attaching package: 'BiocGenerics' The following objects are masked from 'package:stats': IQR, mad, sd, var, xtabs The following objects are masked from 'package:base': Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted, lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply, union, unique, unsplit, which.max, which.min Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. > > # Create Gene-gene distance through mRNA data > bioDistmRNA<-bioDistclass(name = "mRNAbymRNA", + distance = cor(t(exprs(mRNA.ds)),method="spearman"), + map.name = "id", + map.metadata = list(), + params = list()) > > ############################################# > ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList > ############################################# > > bioDistList<-list(bioDistmRNA,bioDistmiRNA) > weights<-matrix(0,4,2) > weights[,1]<-c(0,0.33,0.67,1) > weights[,2]<-c(1,0.67,0.33,0)# > > bioDistWList<-bioDistW(referenceFeatures = rownames(Block1), + bioDistList = bioDistList, + weights=weights) > length(bioDistWList) [1] 4 > > ############################################# > ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL > ############################################# > > bioDistWPlot(referenceFeatures = rownames(Block1) , + listDistW = bioDistWList, + method.cor="spearman") Warning messages: 1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], : Cannot compute exact p-value with ties 2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], : Cannot compute exact p-value with ties 3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures], : Cannot compute exact p-value with ties 4: In plot.window(...) : axis(2, *): range of values ( 0) is small wrt |M| = 3e-09 --> not pretty() 5: In plot.window(...) : axis(2, *): range of values ( 0) is small wrt |M| = 3e-09 --> not pretty() 6: In plot.window(...) : axis(2, *): range of values ( 0) is small wrt |M| = 3e-09 --> not pretty() 7: In plot.window(...) : axis(2, *): range of values ( 0) is small wrt |M| = 3e-09 --> not pretty() > > ############################################# > ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE > ############################################# > > ## IDH1 > > IDH1.F<-bioDistFeature(Feature = "IDH1" , + listDistW = bioDistWList, + threshold.cor=0.7) > bioDistFeaturePlot(data=IDH1.F) > > ## PDGFRA > > #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" , > # listDistW = bioDistWList, > # threshold.cor=0.7) > #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png") > > ## EGFR > #EGFR.F<-bioDistFeature(Feature = "EGFR" , > # listDistW = bioDistWList, > # threshold.cor=0.7) > #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png") > > ## MGMT > #MGMT.F<-bioDistFeature(Feature = "MGMT" , > # listDistW = bioDistWList, > # threshold.cor=0.5) > #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png") > > > > > > proc.time() user system elapsed 30.160 0.988 31.464
STATegRa.Rcheck/tests/STATegRa_Example.omicsNPC.Rout
R version 4.4.0 (2024-04-24) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > rm(list = ls()) > require("STATegRa") Loading required package: STATegRa > # Load the data > data("TCGA_BRCA_Batch_93") > # Setting dataTypes > dataTypes <- c("count", "count", "continuous") > # Setting methods to combine pvalues > combMethods = c("Fisher", "Liptak", "Tippett") > # Setting number of permutations > numPerms = 1000 > # Setting number of cores > numCores = 1 > # Setting holistOmics to print out the steps that it performs. > verbose = TRUE > # Run holistOmics analysis. > output <- omicsNPC(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes, combMethods = combMethods, numPerms = numPerms, numCores = numCores, verbose = verbose) Compute initial statistics on data Building NULL distributions by permuting data Compute pseudo p-values based on NULL distributions... NPC p-values calculation... > > proc.time() user system elapsed 77.207 1.801 79.722
STATegRa.Rcheck/tests/STATEgRa_Example.omicsPCA.Rout
R version 4.4.0 (2024-04-24) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ########################################### > ########### EXAMPLE OF THE OMICSPCA > ########################################### > require(STATegRa) Loading required package: STATegRa > > # g_legend (not exported by STATegRa any more) > ## code from https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs > g_legend<-function(a.gplot){ + tmp <- ggplot_gtable(ggplot_build(a.gplot)) + leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box") + legend <- tmp$grobs[[leg]] + return(legend)} > > ######################### > ## PART 1. Load data > > ## Load data > data(STATegRa_S3) > > ls() [1] "Block1.PCA" "Block2.PCA" "ed.PCA" "g_legend" > > ## Create ExpressionSets > # Block1 - Expression data > B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname")) > # Block2 - miRNA expression data > B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname")) > > ######################### > ## PART 2. Model Selection > > require(grid) Loading required package: grid > require(gridExtra) Loading required package: gridExtra > require(ggplot2) Loading required package: ggplot2 > > ## Select the optimal components > ms <- modelSelection(Input=list(B1,B2),Rmax=4,fac.sel="single%",varthreshold=0.03,center=TRUE,scale=TRUE,weight=TRUE) Common components [1] 2 Distinctive components [[1]] [1] 2 [[2]] [1] 2 > > > ######################### > ## PART 3. Component Analysis > > ## 3.1 Component analysis of the three methods > discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=2,Rspecific=c(2,2),center=TRUE, + scale=TRUE,weight=TRUE) > jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=2,Rspecific=c(2,2),center=TRUE, + scale=TRUE,weight=TRUE) > o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=2,Rspecific=c(2,2),center=TRUE, + scale=TRUE,weight=TRUE) > > ## 3.2 Exploring scores structures > > # Exploring DISCO-SCA scores structure > discoRes@scores$common ## Common scores 1 2 sample1 -0.0781574341 0.0431502945 sample2 0.1192218467 -0.0294087386 sample3 0.0531412054 0.0746839762 sample4 -0.0292975095 0.0005961326 sample5 -0.0202091763 -0.0110463480 sample6 -0.1226089041 -0.1053467592 sample7 -0.1078928166 0.0322474737 sample8 -0.1782895215 -0.1449363456 sample9 -0.0468698124 0.0455174355 sample10 0.0036030537 -0.0420110362 sample11 0.0035566459 0.0566292532 sample12 -0.1006128911 -0.0641381058 sample13 0.1174408441 -0.0907488202 sample14 -0.0981203259 -0.0617738627 sample15 -0.0085334367 0.0087012508 sample16 -0.0783148627 -0.1581295169 sample17 0.1483609941 -0.0638581945 sample18 0.0963086228 -0.0556641072 sample19 0.0217244066 0.0720086954 sample20 0.0635636362 0.0779652386 sample21 0.0201840395 -0.1566391146 sample22 -0.0218268817 0.0764103515 sample23 -0.0852041973 0.0032690180 sample24 0.1287170845 -0.1924541531 sample25 0.0430574174 0.0456567411 sample26 0.1453896908 -0.0541511276 sample27 0.0197488714 0.1185655699 sample28 0.1025336369 -0.0650685031 sample29 -0.0706018556 0.0682987131 sample30 0.1295627452 0.0066767672 sample31 -0.1147449120 -0.1232687386 sample32 0.0374310820 -0.0380179195 sample33 -0.0599516070 -0.0136868475 sample34 0.0984200788 -0.0375321823 sample35 0.0543098342 0.0378104673 sample36 -0.1403625491 0.0343754101 sample37 -0.0228941979 0.0732843529 sample38 0.0222077190 0.0962594295 sample39 0.0941738503 -0.0215198862 sample40 -0.0643801254 0.0687868337 sample41 0.0327637969 0.1232188153 sample42 0.0500431837 0.0292474030 sample43 0.0184498782 -0.0233011613 sample44 -0.1487898604 -0.1171351845 sample45 0.1050774256 -0.1123200488 sample46 0.1151195653 0.1094028160 sample47 0.0962593694 0.0288462933 sample48 -0.0004837252 0.0310279660 sample49 -0.1135207713 -0.1213972395 sample50 0.0123553066 0.1740744058 sample51 -0.0550529845 -0.1258887365 sample52 -0.0499121201 -0.0728545042 sample53 -0.1119773638 -0.1588014717 sample54 0.0360055668 -0.0228575736 sample55 -0.0210419010 -0.0006731958 sample56 0.0434169258 -0.0633126088 sample57 -0.0197824566 -0.1150714141 sample58 -0.0030439905 -0.0326098494 sample59 -0.0500253182 -0.0129420530 sample60 -0.0184278687 -0.0136087421 sample61 -0.0150299422 -0.0635026884 sample62 0.0304763821 0.0201317898 sample63 -0.1102252430 -0.1285976848 sample64 -0.1552588062 -0.0971168634 sample65 0.0058503059 -0.0207115208 sample66 0.0025605371 -0.0424319345 sample67 -0.1546634886 0.0661714296 sample68 -0.0536369356 0.0923682489 sample69 -0.0640330405 -0.0081983449 sample70 -0.0163517797 0.0663229989 sample71 0.0102537607 0.1345922045 sample72 0.0654195970 0.0196118280 sample73 0.1048556090 -0.0220939191 sample74 -0.0123799509 -0.0586115695 sample75 -0.0392077976 0.0209754651 sample76 -0.0648953392 0.0524764290 sample77 -0.1172922126 0.0201186858 sample78 0.1463068180 -0.0708471410 sample79 -0.0265211157 0.1603309698 sample80 -0.0279737199 0.0214204422 sample81 -0.0079211501 0.0738451495 sample82 0.1544236495 0.0361467583 sample83 0.0494211307 0.0050046901 sample84 0.0259038476 0.0346550251 sample85 -0.1116484397 0.0031496364 sample86 0.1306482985 0.0377214289 sample87 0.0554778190 0.0459748825 sample88 0.0301623878 -0.0382197661 sample89 0.1016866703 -0.0694034437 sample90 -0.0086819896 0.0201320131 sample91 -0.1578625396 0.2097827520 sample92 -0.0170936779 0.1655808733 sample93 0.0979806801 0.0121512061 sample94 -0.0131484120 0.0114932021 sample95 -0.0315682621 0.0758859886 sample96 -0.0024125616 0.0470136231 sample97 -0.0634545415 -0.0270331534 sample98 0.0359374609 0.0135488145 sample99 0.1009163394 -0.1124779183 sample100 -0.0551753132 -0.0246489784 sample101 0.0080118864 0.1627369004 sample102 0.0046444403 -0.0095629862 sample103 0.0472523146 0.0940393182 sample104 -0.0198159463 0.0591092339 sample105 0.0400237805 0.0160912510 sample106 0.0923808442 -0.0369017554 sample107 0.1019373929 -0.0224954339 sample108 0.0877091654 0.0128834387 sample109 -0.0864824325 0.0900943453 sample110 0.1223115554 0.0096085988 sample111 -0.0257354606 0.0936170545 sample112 0.0765286593 -0.0270347950 sample113 -0.0258803205 -0.0377496469 sample114 -0.0021138947 0.0882015187 sample115 -0.0303460133 0.0723587485 sample116 -0.0780508363 0.0685067476 sample117 -0.0536898058 0.0911909840 sample118 -0.0666651126 0.0236231378 sample119 -0.1021871633 0.2324937571 sample120 -0.0750216543 -0.0243378777 sample121 0.0756936419 -0.0942950830 sample122 0.0259628129 -0.0731986373 sample123 0.1037846237 0.0369196988 sample124 -0.0611207882 -0.0421722777 sample125 0.0738472711 -0.0066950041 sample126 -0.0972916467 -0.0762640750 sample127 -0.0824697645 0.0096637343 sample128 0.1249407703 -0.0929312011 sample129 0.0734067473 0.0434362312 sample130 0.0003501987 0.0309852700 sample131 -0.0930182824 -0.0155937542 sample132 -0.0736222779 -0.0733029167 sample133 0.0498397975 0.0462437721 sample134 -0.1644873479 -0.0720006110 sample135 0.0752297173 -0.0003818362 sample136 -0.0227145820 0.0495505568 sample137 -0.0564717457 0.0288915063 sample138 -0.0255988090 0.0610857889 sample139 -0.0621217812 -0.0235808298 sample140 0.0604152488 0.0435592553 sample141 -0.0246743961 -0.0532648462 sample142 0.0409560365 -0.0316279286 sample143 0.0077355228 0.0476896470 sample144 -0.0173240833 0.0156778061 sample145 -0.0485474446 -0.1202770160 sample146 -0.0419645681 0.0811280793 sample147 0.0977308309 0.0274839220 sample148 -0.0368256160 -0.0803979504 sample149 0.0072865781 0.1532986375 sample150 -0.1020825289 -0.0624775206 sample151 -0.0305399044 0.0289278903 sample152 0.0533594809 0.0638309400 sample153 0.0891627806 -0.1799576898 sample154 0.0727557437 0.0834160523 sample155 0.0880668525 0.0220819028 sample156 0.0276561014 0.0326625072 sample157 0.1155032185 -0.0183616330 sample158 0.0281507503 0.0104938365 sample159 -0.0663235683 -0.0443837027 sample160 0.0302643876 -0.0404265832 sample161 -0.0114715538 0.0591026162 sample162 0.1337087179 -0.1398135463 sample163 -0.1330124426 -0.1688781169 sample164 0.0150336110 -0.0028415659 sample165 -0.0076520269 0.0164128661 sample166 -0.0367794331 -0.0630661505 sample167 -0.1111988875 -0.0030057970 sample168 0.0672981620 -0.0446279174 sample169 0.0413004953 -0.0224394883 > discoRes@scores$dist[[1]] ## Distinctive scores for Block 1 1 2 sample1 -0.0420514139 0.0867863130 sample2 -0.0820828860 -0.0410977911 sample3 0.0155900589 -0.0195182426 sample4 -0.1001337182 -0.0410786542 sample5 -0.0153466123 -0.0253259652 sample6 0.0340324787 -0.0408223198 sample7 0.0722580035 0.0002332127 sample8 -0.0457501413 -0.0370016100 sample9 -0.0086248867 0.0820184902 sample10 -0.0423598991 -0.0083923204 sample11 0.0022549174 0.0787766020 sample12 0.0322105433 0.1479824668 sample13 -0.0293890771 -0.0306748547 sample14 0.0337482046 -0.0367506878 sample15 0.0815539385 0.1275622376 sample16 0.0508450774 0.0540604653 sample17 0.0062596199 0.0041023730 sample18 0.0705639349 -0.0351047767 sample19 -0.0476841025 -0.0509598052 sample20 0.0522963579 0.0715521758 sample21 -0.0119128141 -0.0376093007 sample22 0.0724393863 -0.0095625246 sample23 -0.0992532117 0.0134288926 sample24 -0.1595119852 0.0728662311 sample25 -0.0920692998 -0.0749757162 sample26 -0.0595540435 0.0848966139 sample27 0.0826486726 -0.0086735586 sample28 -0.0384788720 0.0440966951 sample29 0.0777672349 0.1735308384 sample30 0.1229471316 -0.0819005691 sample31 0.0579845253 -0.0238644781 sample32 0.0970392857 -0.0111426413 sample33 0.1017587898 -0.0630442700 sample34 0.0637922640 0.0377941640 sample35 0.0789984677 -0.0229723340 sample36 0.1224939228 -0.1274955111 sample37 0.1798821076 -0.1673427694 sample38 0.0466305507 0.0888160841 sample39 -0.0168687725 0.0421533784 sample40 0.1756392388 -0.1526642613 sample41 0.0042371900 0.0004928746 sample42 -0.0447849448 -0.0651504961 sample43 0.0482308250 -0.0253529325 sample44 -0.1986715966 -0.0545777560 sample45 -0.0741837352 0.0054703424 sample46 0.0478773122 -0.0007072128 sample47 0.0608188832 0.0481622544 sample48 -0.1381489128 0.0578287944 sample49 -0.0530521655 -0.1405532715 sample50 -0.0173798128 0.1602389622 sample51 0.0462559922 0.0303473833 sample52 0.0280064327 0.0280388395 sample53 0.0667620094 0.0237702020 sample54 0.0121833329 -0.0521354328 sample55 0.0182395901 0.0221328415 sample56 -0.0001256113 0.0030907386 sample57 0.0316674436 0.0530190294 sample58 0.0393917876 -0.0297798784 sample59 0.1278290704 -0.0546528111 sample60 0.1486984919 0.1069156376 sample61 0.0793121954 0.0569796454 sample62 0.1172801225 -0.0149198650 sample63 -0.0028727665 0.1300519889 sample64 0.0237363976 0.1073287729 sample65 -0.0126534981 0.0589808464 sample66 -0.0468195188 -0.0771072614 sample67 0.1494264799 -0.0769860507 sample68 0.0977961833 -0.0577351214 sample69 0.0403087321 0.0156042067 sample70 0.0221531942 0.0315440899 sample71 -0.0546433534 -0.0272396421 sample72 0.1107487929 -0.0537319526 sample73 0.0906761214 0.0579966497 sample74 0.0586554991 0.0121421624 sample75 0.0390493482 0.0349282757 sample76 -0.0022960540 -0.1676558812 sample77 -0.0232096321 -0.2067302775 sample78 -0.0929755529 -0.0434939346 sample79 -0.1619495639 -0.0378114087 sample80 0.0680365836 0.1424663402 sample81 -0.0530783494 -0.0358350808 sample82 0.0266822211 -0.0577445162 sample83 0.1517235252 -0.0448554539 sample84 -0.0570966863 -0.0273813177 sample85 0.1086289497 -0.1228119471 sample86 0.0833860230 -0.0442915113 sample87 0.0022018616 -0.0943906875 sample88 -0.0078225425 -0.1140506505 sample89 0.0611056748 -0.0094585206 sample90 0.0022928205 -0.0936254006 sample91 0.0433592342 0.3205982650 sample92 -0.1815334369 -0.0334680117 sample93 0.0267630985 0.0614428983 sample94 0.0181878000 0.0605090378 sample95 -0.0720375123 -0.0013045589 sample96 -0.0559714376 -0.0118791361 sample97 -0.0217411119 0.0195414184 sample98 0.0379177614 0.0588357050 sample99 -0.0792428097 -0.0151273657 sample100 0.0222116251 -0.0023321455 sample101 -0.0387227188 0.1224226204 sample102 -0.2094614286 -0.0516442336 sample103 0.0138481970 0.0301051899 sample104 -0.0807986634 -0.0162718831 sample105 -0.0520493344 -0.1229665101 sample106 -0.0192613588 -0.0185238148 sample107 0.0319017132 0.0405123252 sample108 -0.0140690833 0.0163421393 sample109 -0.1831929398 0.0613007771 sample110 -0.0292790525 -0.0199849049 sample111 -0.1423251263 0.0327340485 sample112 0.0426332663 -0.0029083488 sample113 -0.0771904786 0.0268733768 sample114 -0.0241640721 -0.0184080418 sample115 -0.1959015012 0.0460130914 sample116 -0.1394475638 -0.0530805648 sample117 -0.1672361330 -0.1386536210 sample118 -0.0448344169 -0.0117621883 sample119 -0.0910384453 0.2217433415 sample120 -0.0331392358 -0.0057274443 sample121 0.0307574324 0.1392506539 sample122 -0.0839781720 -0.0291994278 sample123 0.0239650647 -0.0642163775 sample124 -0.0909150891 0.0130419640 sample125 -0.0065350981 -0.1092631803 sample126 0.0935311467 0.1368283951 sample127 0.0035388059 0.0292755627 sample128 -0.0660296020 0.1018566446 sample129 0.0693638898 -0.0695421853 sample130 0.0008493657 -0.0669704342 sample131 0.0431023890 0.0174064816 sample132 -0.0637040615 0.0029374838 sample133 -0.0289494470 -0.0390818809 sample134 0.0446202420 0.0456334478 sample135 0.0712337049 0.0521634857 sample136 0.0596271461 0.0197299221 sample137 0.0793152185 -0.0380628426 sample138 -0.0973547923 -0.0454218153 sample139 0.0539904317 -0.1534327427 sample140 0.0850827470 0.0955814398 sample141 -0.0192682242 -0.0554450019 sample142 -0.0672262327 -0.0461320808 sample143 -0.0303730194 -0.0519260213 sample144 -0.0089364437 0.0145814923 sample145 -0.0638770985 0.0122258550 sample146 0.0585856960 0.0063083236 sample147 0.0894133450 -0.1124615838 sample148 -0.0216367669 -0.0615967060 sample149 -0.0515419685 -0.0839903483 sample150 0.0568282799 -0.0124468973 sample151 -0.0789532209 -0.0261831086 sample152 -0.0330752875 0.1306443604 sample153 -0.1751932582 0.1497732414 sample154 0.0421424977 -0.0037010288 sample155 0.0680177717 0.0095711127 sample156 0.0388911554 0.1057562895 sample157 0.0314769368 0.0561367385 sample158 0.0329620654 0.0353947276 sample159 -0.0398417032 -0.1007373700 sample160 0.0424938439 0.0108496138 sample161 -0.0888371016 -0.0679700070 sample162 -0.0027476852 0.1237843918 sample163 -0.0126106489 0.0725434431 sample164 -0.0566779734 -0.0458324099 sample165 -0.0315336332 -0.0236362307 sample166 -0.0612058657 -0.0425232928 sample167 0.0142729865 0.0179308259 sample168 -0.0169503897 -0.0769617858 sample169 0.0675080185 0.0131505246 > discoRes@scores$dist[[2]] ## Distinctive scores for Block 2 1 2 sample1 0.0012329693 1.635717e-01 sample2 0.0724350110 6.021279e-03 sample3 0.0188460438 1.080036e-01 sample4 -0.0390145242 -3.113976e-04 sample5 -0.1774811623 2.996385e-02 sample6 0.0451444444 3.455859e-02 sample7 0.0226466212 7.020141e-03 sample8 0.1033680296 9.856809e-03 sample9 -0.1350011762 -8.979099e-02 sample10 -0.1259887210 5.097854e-02 sample11 -0.0979788396 -7.086535e-02 sample12 0.0863019127 8.620317e-02 sample13 0.1381401115 -1.828007e-01 sample14 0.0615073868 2.642803e-02 sample15 -0.0381598978 3.101662e-02 sample16 0.0048776754 -1.271824e-03 sample17 0.0788480968 1.547555e-02 sample18 0.0884188748 3.795486e-02 sample19 -0.0703044400 1.084004e-01 sample20 0.0025585469 -7.975877e-02 sample21 -0.0941601607 4.126744e-02 sample22 0.0550273379 7.806741e-02 sample23 -0.0679495259 4.102007e-02 sample24 0.1310962882 -1.649309e-01 sample25 -0.0113585247 4.426864e-02 sample26 0.1402945954 -2.016541e-02 sample27 -0.0261561192 -1.588476e-03 sample28 0.0724198741 -5.850591e-02 sample29 0.0330058527 -2.060851e-03 sample30 0.0228752509 2.015428e-02 sample31 0.0635067965 6.670334e-02 sample32 -0.0685099670 4.955272e-02 sample33 0.0777765202 1.272078e-01 sample34 -0.0157842422 3.024314e-02 sample35 0.0529632690 -1.500972e-01 sample36 -0.0070900861 -2.025308e-01 sample37 0.0442420466 -1.802089e-01 sample38 0.0781511266 3.676418e-02 sample39 -0.0120331837 3.388842e-02 sample40 0.0473291950 -1.471562e-01 sample41 -0.0228189437 2.673553e-02 sample42 0.0245360268 7.960867e-02 sample43 -0.1036362806 8.229577e-02 sample44 0.1012228893 -7.049447e-02 sample45 -0.0013731978 2.450914e-02 sample46 0.0558509971 -2.947406e-03 sample47 0.0380481141 -4.554175e-02 sample48 -0.0784342044 -4.888979e-02 sample49 0.0605164011 1.162358e-02 sample50 -0.0530079294 2.737930e-02 sample51 -0.1514646527 -5.678344e-02 sample52 -0.1860935250 -1.246717e-01 sample53 0.0064177114 2.700995e-02 sample54 -0.0697038339 2.308389e-02 sample55 -0.1633577041 -1.366442e-02 sample56 -0.1011485094 -4.682204e-02 sample57 -0.1730374223 -1.609603e-01 sample58 0.0071384701 1.666955e-02 sample59 0.0030461629 -3.005287e-02 sample60 -0.0215835237 -2.665878e-01 sample61 -0.1510583670 -1.002385e-01 sample62 0.0925533908 4.845840e-02 sample63 0.0596311835 4.137024e-02 sample64 0.0449225817 2.600589e-03 sample65 -0.0939383740 4.406910e-02 sample66 -0.1063400716 5.709994e-02 sample67 0.0201589894 -2.361728e-01 sample68 -0.0037203271 -2.418392e-02 sample69 0.0645161213 1.155622e-01 sample70 0.1013440014 1.351789e-01 sample71 0.0016467871 2.976840e-02 sample72 -0.0328893067 2.835856e-02 sample73 -0.0275080064 5.148185e-02 sample74 -0.1341719681 7.895280e-02 sample75 -0.0951575676 3.943183e-02 sample76 0.0864721941 -3.034993e-02 sample77 0.1035749569 2.545353e-02 sample78 0.1575644179 -4.939591e-02 sample79 -0.0189137062 -4.874679e-02 sample80 -0.1384140614 -4.266476e-05 sample81 0.0118846472 6.357931e-02 sample82 0.1675308143 -3.533912e-02 sample83 0.0065673360 7.812607e-02 sample84 -0.1486891593 3.109057e-02 sample85 0.0532724360 -7.417886e-02 sample86 0.1138477294 1.913670e-05 sample87 -0.0432864009 -6.080473e-02 sample88 -0.0433450384 -1.402491e-01 sample89 -0.0331205784 1.395401e-02 sample90 0.0607412820 8.610414e-02 sample91 0.0566272561 -1.303748e-01 sample92 0.0359582519 -1.061604e-01 sample93 0.0433646358 4.443634e-02 sample94 0.0477291311 1.059574e-01 sample95 0.0249595789 3.980525e-02 sample96 -0.0035218983 9.293928e-02 sample97 0.0066048795 1.527231e-01 sample98 -0.0020366823 5.579549e-02 sample99 0.0886616154 3.728229e-02 sample100 0.1091259138 3.560420e-02 sample101 0.0739726474 4.317997e-02 sample102 -0.0574461069 2.783917e-02 sample103 -0.0142731049 -9.705574e-03 sample104 -0.0710395200 -4.068350e-02 sample105 -0.0980831331 3.452953e-02 sample106 0.0254259320 -3.628983e-02 sample107 0.0160653457 9.173395e-02 sample108 0.0200987663 2.379692e-02 sample109 0.0389780705 -1.692358e-02 sample110 0.0326304849 -2.988109e-02 sample111 -0.0676937519 6.038213e-02 sample112 -0.0167883445 -5.336938e-03 sample113 -0.0969216974 2.757605e-02 sample114 0.0026398357 9.209156e-02 sample115 0.0308047376 -1.603821e-02 sample116 0.1240307191 -1.273000e-01 sample117 -0.0334729051 -5.392709e-02 sample118 0.1037152920 -6.252431e-02 sample119 0.1064176630 -1.196203e-01 sample120 0.0771355126 1.004933e-01 sample121 0.0129350760 -3.181974e-02 sample122 -0.0847492235 5.568329e-02 sample123 0.0041336757 -7.693187e-03 sample124 0.0583458059 8.396391e-02 sample125 -0.0634844592 5.232540e-02 sample126 0.0662580953 1.091732e-01 sample127 0.0865024621 1.094176e-01 sample128 0.0627817484 1.470966e-02 sample129 0.0336276418 4.007857e-02 sample130 0.0293517751 8.046116e-02 sample131 0.0469197654 2.209744e-03 sample132 0.0241740739 1.248598e-01 sample133 -0.0907303215 -1.466701e-02 sample134 0.0350842069 -7.539662e-02 sample135 -0.0001333433 -9.185387e-03 sample136 0.0335876044 9.860272e-02 sample137 0.0640148885 7.554468e-02 sample138 -0.0060964819 1.742763e-02 sample139 0.0592084431 -5.614970e-02 sample140 -0.0427985963 1.099549e-02 sample141 -0.0618796353 9.301039e-02 sample142 -0.0898554445 -3.573416e-02 sample143 -0.0817389230 -8.880524e-02 sample144 -0.0787754775 3.821391e-02 sample145 -0.1085821556 -1.569476e-01 sample146 0.0589557910 4.373358e-02 sample147 0.0495330397 -7.277218e-03 sample148 -0.1161592771 -9.079067e-03 sample149 0.0121579428 -7.788375e-02 sample150 0.0314512526 -3.520213e-02 sample151 -0.0575382147 1.945353e-02 sample152 0.0494542091 -7.025538e-02 sample153 0.0941332812 -2.153297e-01 sample154 0.0335931972 -2.078730e-02 sample155 -0.0690457675 2.780409e-02 sample156 -0.1039901628 6.292524e-02 sample157 0.0408645776 -8.065515e-03 sample158 -0.1018105322 -7.816880e-03 sample159 0.0281730561 1.207207e-02 sample160 -0.1643053017 -2.978102e-03 sample161 -0.0374329247 -8.524610e-02 sample162 0.0804535343 -8.349752e-02 sample163 0.0743228019 1.406227e-02 sample164 -0.1208805989 2.139461e-02 sample165 -0.1608115911 -2.025192e-02 sample166 0.0425944678 2.660716e-02 sample167 0.0226849485 4.464281e-02 sample168 0.0180735595 7.466298e-04 sample169 -0.0190779030 -2.645403e-02 > # Exploring O2PLS scores structure > o2plsRes@scores$common[[1]] ## Common scores for Block 1 [,1] [,2] sample1 -0.0572060227 -1.729087e-02 sample2 0.0875245208 1.112588e-02 sample3 0.0403482602 -3.168994e-02 sample4 -0.0218345996 4.052760e-06 sample5 -0.0150905011 4.795041e-03 sample6 -0.0924362933 4.511003e-02 sample7 -0.0793066751 -1.243823e-02 sample8 -0.1342997187 6.215220e-02 sample9 -0.0338886944 -1.854401e-02 sample10 0.0020547173 1.749421e-02 sample11 0.0037275602 -2.364116e-02 sample12 -0.0753094533 2.772698e-02 sample13 0.0856160091 3.679963e-02 sample14 -0.0737457307 2.668452e-02 sample15 -0.0062111746 -3.554864e-03 sample16 -0.0602355268 6.675115e-02 sample17 0.1086768843 2.524534e-02 sample18 0.0702999472 2.231671e-02 sample19 0.0173785882 -3.024846e-02 sample20 0.0484173812 -3.310904e-02 sample21 0.0124657042 6.517144e-02 sample22 -0.0140989936 -3.159137e-02 sample23 -0.0627028403 -5.393710e-04 sample24 0.0919972100 7.909297e-02 sample25 0.0326998483 -1.945206e-02 sample26 0.1064741246 2.120849e-02 sample27 0.0166058995 -4.964993e-02 sample28 0.0743504770 2.614211e-02 sample29 -0.0511008491 -2.782647e-02 sample30 0.0962250842 -3.974893e-03 sample31 -0.0869563008 5.250819e-02 sample32 0.0271858919 1.552005e-02 sample33 -0.0448364581 6.243160e-03 sample34 0.0718415218 1.469396e-02 sample35 0.0403086451 -1.632629e-02 sample36 -0.1036402827 -1.304320e-02 sample37 -0.0159385744 -3.036525e-02 sample38 0.0182198369 -4.034805e-02 sample39 0.0690363619 8.058350e-03 sample40 -0.0467312750 -2.810325e-02 sample41 0.0263674438 -5.171216e-02 sample42 0.0374578960 -1.268634e-02 sample43 0.0132336869 9.536642e-03 sample44 -0.1119154428 5.028683e-02 sample45 0.0759639367 4.587903e-02 sample46 0.0871885519 -4.670385e-02 sample47 0.0721490571 -1.288540e-02 sample48 0.0005086144 -1.290565e-02 sample49 -0.0858177028 5.173760e-02 sample50 0.0118992665 -7.276215e-02 sample51 -0.0426446855 5.306205e-02 sample52 -0.0381605826 3.086785e-02 sample53 -0.0855757630 6.730043e-02 sample54 0.0261723092 9.184260e-03 sample55 -0.0156418304 4.682404e-04 sample56 0.0307831193 2.597550e-02 sample57 -0.0157242103 4.829381e-02 sample58 -0.0031174404 1.359898e-02 sample59 -0.0373001859 5.868397e-03 sample60 -0.0142609099 5.831654e-03 sample61 -0.0122255144 2.663579e-02 sample62 0.0228002942 -8.692265e-03 sample63 -0.0833127581 5.473229e-02 sample64 -0.1166548159 4.196500e-02 sample65 0.0038808902 8.568590e-03 sample66 0.0011561811 1.766612e-02 sample67 -0.1129311062 -2.608702e-02 sample68 -0.0382526429 -3.804045e-02 sample69 -0.0476502440 4.003241e-03 sample70 -0.0110329882 -2.752719e-02 sample71 0.0096850282 -5.627056e-02 sample72 0.0487124704 -8.800131e-03 sample73 0.0773058132 8.239864e-03 sample74 -0.0102488176 2.454957e-02 sample75 -0.0286613976 -8.387293e-03 sample76 -0.0472655595 -2.129315e-02 sample77 -0.0865043074 -7.296820e-03 sample78 0.1070293698 2.818346e-02 sample79 -0.0165060681 -6.659721e-02 sample80 -0.0206765949 -8.712112e-03 sample81 -0.0050943615 -3.079175e-02 sample82 0.1153622361 -1.647054e-02 sample83 0.0367979217 -2.538114e-03 sample84 0.0199463070 -1.468961e-02 sample85 -0.0827122185 -2.709824e-04 sample86 0.0969487314 -1.699897e-02 sample87 0.0421957457 -1.965953e-02 sample88 0.0215934743 1.566050e-02 sample89 0.0751559502 2.811652e-02 sample90 -0.0057328000 -8.283795e-03 sample91 -0.1134005268 -8.603522e-02 sample92 -0.0101689918 -6.894992e-02 sample93 0.0725967502 -6.003176e-03 sample94 -0.0096878852 -4.693081e-03 sample95 -0.0223502239 -3.139636e-02 sample96 -0.0013232863 -1.963604e-02 sample97 -0.0476541710 1.183660e-02 sample98 0.0269546160 -5.978398e-03 sample99 0.0728179461 4.597884e-02 sample100 -0.0413398038 1.079347e-02 sample101 0.0087536994 -6.796076e-02 sample102 0.0032509529 3.932612e-03 sample103 0.0360342395 -3.973263e-02 sample104 -0.0141722563 -2.453107e-02 sample105 0.0294940465 -7.140722e-03 sample106 0.0686472054 1.462895e-02 sample107 0.0748635927 8.401339e-03 sample108 0.0650175850 -6.211942e-03 sample109 -0.0628017242 -3.681224e-02 sample110 0.0905513691 -5.169053e-03 sample111 -0.0176679473 -3.884777e-02 sample112 0.0570870472 1.066018e-02 sample113 -0.0200110554 1.596044e-02 sample114 -0.0001474542 -3.679272e-02 sample115 -0.0213333038 -2.991667e-02 sample116 -0.0567675453 -2.785636e-02 sample117 -0.0379865990 -3.752078e-02 sample118 -0.0484878786 -9.173691e-03 sample119 -0.0713511831 -9.598634e-02 sample120 -0.0555093586 1.089843e-02 sample121 0.0542443861 3.861344e-02 sample122 0.0178575357 3.027138e-02 sample123 0.0775020581 -1.636852e-02 sample124 -0.0460701050 1.814758e-02 sample125 0.0543846585 2.075898e-03 sample126 -0.0729417144 3.276659e-02 sample127 -0.0609509157 -3.270814e-03 sample128 0.0908136899 3.758801e-02 sample129 0.0552445878 -1.879062e-02 sample130 0.0007128089 -1.294308e-02 sample131 -0.0693311345 7.357082e-03 sample132 -0.0556565156 3.126995e-02 sample133 0.0375870104 -1.977240e-02 sample134 -0.1229130924 3.159495e-02 sample135 0.0555550315 -5.563250e-04 sample136 -0.0159768414 -2.046339e-02 sample137 -0.0412337694 -1.151652e-02 sample138 -0.0180604476 -2.526505e-02 sample139 -0.0465649201 1.040683e-02 sample140 0.0452288969 -1.876279e-02 sample141 -0.0189142561 2.247042e-02 sample142 0.0297545566 1.280524e-02 sample143 0.0064292003 -1.997706e-02 sample144 -0.0124284903 -6.369733e-03 sample145 -0.0377141491 5.066743e-02 sample146 -0.0296240067 -3.344465e-02 sample147 0.0726083535 -1.239968e-02 sample148 -0.0284795794 3.389732e-02 sample149 0.0082261455 -6.399305e-02 sample150 -0.0765013197 2.704021e-02 sample151 -0.0220567356 -1.178159e-02 sample152 0.0403422737 -2.714879e-02 sample153 0.0629117719 7.425085e-02 sample154 0.0551622927 -3.548984e-02 sample155 0.0654439133 -1.005306e-02 sample156 0.0209310714 -1.390213e-02 sample157 0.0851522597 6.577150e-03 sample158 0.0208354599 -4.663078e-03 sample159 -0.0498794349 1.913257e-02 sample160 0.0216074437 1.656579e-02 sample161 -0.0075742328 -2.455676e-02 sample162 0.0963663017 5.705881e-02 sample163 -0.1009542191 7.174224e-02 sample164 0.0109881996 1.026806e-03 sample165 -0.0053146157 -6.772855e-03 sample166 -0.0275757357 2.673084e-02 sample167 -0.0825048036 2.278863e-03 sample168 0.0486147429 1.793843e-02 sample169 0.0302506727 8.984253e-03 > o2plsRes@scores$common[[2]] ## Common scores for Block 2 [,1] [,2] sample1 -0.0621842115 -1.364509e-02 sample2 0.0944623785 9.720892e-03 sample3 0.0406196267 -2.236338e-02 sample4 -0.0229316496 -3.932487e-04 sample5 -0.0157330047 3.231033e-03 sample6 -0.0945794025 3.120720e-02 sample7 -0.0854427118 -1.052880e-02 sample8 -0.1376625920 4.286608e-02 sample9 -0.0377115311 -1.415134e-02 sample10 0.0035244506 1.280825e-02 sample11 0.0016639987 -1.717895e-02 sample12 -0.0781403168 1.884368e-02 sample13 0.0938400516 2.838858e-02 sample14 -0.0759839772 1.810989e-02 sample15 -0.0068340837 -2.705361e-03 sample16 -0.0590150849 4.757848e-02 sample17 0.1178805097 2.040526e-02 sample18 0.0767858320 1.756604e-02 sample19 0.0157112113 -2.172867e-02 sample20 0.0485318300 -2.327033e-02 sample21 0.0185928176 4.777095e-02 sample22 -0.0191358702 -2.329775e-02 sample23 -0.0672994194 -1.535656e-03 sample24 0.1047476642 5.935707e-02 sample25 0.0329844953 -1.358036e-02 sample26 0.1154952052 1.741529e-02 sample27 0.0133849853 -3.590922e-02 sample28 0.0821554039 2.042376e-02 sample29 -0.0567643690 -2.123848e-02 sample30 0.1016073931 -1.134728e-03 sample31 -0.0880396372 3.670548e-02 sample32 0.0300363338 1.182406e-02 sample33 -0.0467252272 3.739254e-03 sample34 0.0783666394 1.203777e-02 sample35 0.0424227097 -1.118559e-02 sample36 -0.1107646166 -1.143464e-02 sample37 -0.0191667664 -2.246060e-02 sample38 0.0155968095 -2.909621e-02 sample39 0.0746847148 7.148218e-03 sample40 -0.0517028178 -2.137267e-02 sample41 0.0234979494 -3.723018e-02 sample42 0.0388797356 -8.557228e-03 sample43 0.0149555568 7.210002e-03 sample44 -0.1150305613 3.461805e-02 sample45 0.0846146236 3.486020e-02 sample46 0.0884426404 -3.246853e-02 sample47 0.0748644971 -8.083045e-03 sample48 -0.0012033198 -9.403647e-03 sample49 -0.0872662737 3.616245e-02 sample50 0.0066941314 -5.284863e-02 sample51 -0.0411777630 3.791830e-02 sample52 -0.0379355780 2.180834e-02 sample53 -0.0851639886 4.751761e-02 sample54 0.0288006248 7.184424e-03 sample55 -0.0164920835 5.919925e-05 sample56 0.0355115616 1.951043e-02 sample57 -0.0141146068 3.492409e-02 sample58 -0.0015636132 9.862883e-03 sample59 -0.0390656483 3.590929e-03 sample60 -0.0139454780 3.963030e-03 sample61 -0.0106410274 1.919705e-02 sample62 0.0236748439 -5.922677e-03 sample63 -0.0846790877 3.839102e-02 sample64 -0.1202581015 2.846469e-02 sample65 0.0050548584 6.328644e-03 sample66 0.0028013072 1.291807e-02 sample67 -0.1231623009 -2.112565e-02 sample68 -0.0437782161 -2.845072e-02 sample69 -0.0501199692 2.053469e-03 sample70 -0.0140278645 -2.027157e-02 sample71 0.0057489505 -4.085977e-02 sample72 0.0511212704 -5.522408e-03 sample73 0.0828141409 7.431582e-03 sample74 -0.0085959456 1.772951e-02 sample75 -0.0312180394 -6.636869e-03 sample76 -0.0519051781 -1.640191e-02 sample77 -0.0925924762 -6.907800e-03 sample78 0.1163971046 2.251122e-02 sample79 -0.0240906926 -4.887766e-02 sample80 -0.0221327065 -6.730703e-03 sample81 -0.0072114968 -2.254399e-02 sample82 0.1204416674 -9.907422e-03 sample83 0.0386739485 -1.171663e-03 sample84 0.0195988488 -1.033806e-02 sample85 -0.0877680171 -1.725057e-03 sample86 0.1023541048 -1.062501e-02 sample87 0.0425213089 -1.356865e-02 sample88 0.0244788514 1.180820e-02 sample89 0.0804276691 2.188588e-02 sample90 -0.0074639871 -6.140721e-03 sample91 -0.1278832404 -6.485140e-02 sample92 -0.0162199697 -5.048358e-02 sample93 0.0769344893 -3.045135e-03 sample94 -0.0104345587 -3.593172e-03 sample95 -0.0260058453 -2.330475e-02 sample96 -0.0025018700 -1.433516e-02 sample97 -0.0492358305 7.774183e-03 sample98 0.0279220220 -3.862141e-03 sample99 0.0813921923 3.487339e-02 sample100 -0.0428797405 7.112807e-03 sample101 0.0032855240 -4.940743e-02 sample102 0.0038439317 2.938008e-03 sample103 0.0358511139 -2.831881e-02 sample104 -0.0162784000 -1.815061e-02 sample105 0.0314853405 -4.656633e-03 sample106 0.0726456731 1.192390e-02 sample107 0.0807342975 7.508627e-03 sample108 0.0688338003 -3.336161e-03 sample109 -0.0694151950 -2.800146e-02 sample110 0.0961218924 -2.111997e-03 sample111 -0.0217900036 -2.864702e-02 sample112 0.0599954082 8.820317e-03 sample113 -0.0195006577 1.128215e-02 sample114 -0.0032126533 -2.682851e-02 sample115 -0.0251101087 -2.221077e-02 sample116 -0.0625141551 -2.137258e-02 sample117 -0.0440473375 -2.806256e-02 sample118 -0.0532042630 -7.590494e-03 sample119 -0.0848603028 -7.133574e-02 sample120 -0.0588832131 6.937326e-03 sample121 0.0613899126 2.915307e-02 sample122 0.0218424338 2.241775e-02 sample123 0.0809008460 -1.051759e-02 sample124 -0.0472109313 1.239887e-02 sample125 0.0583180947 2.521167e-03 sample126 -0.0753941872 2.256455e-02 sample127 -0.0649774209 -3.496964e-03 sample128 0.1000212216 2.908091e-02 sample129 0.0568033049 -1.269016e-02 sample130 -0.0002370832 -9.419675e-03 sample131 -0.0727030877 4.091672e-03 sample132 -0.0566219024 2.179861e-02 sample133 0.0384172955 -1.372840e-02 sample134 -0.1280862736 2.077912e-02 sample135 0.0592633273 6.106685e-04 sample136 -0.0187635410 -1.521173e-02 sample137 -0.0449958970 -9.152840e-03 sample138 -0.0211348699 -1.875415e-02 sample139 -0.0482882861 6.729304e-03 sample140 0.0468926306 -1.285498e-02 sample141 -0.0186248693 1.605439e-02 sample142 0.0328031246 9.887746e-03 sample143 0.0052919839 -1.445666e-02 sample144 -0.0140067923 -4.867248e-03 sample145 -0.0361804310 3.625323e-02 sample146 -0.0345286735 -2.493652e-02 sample147 0.0765025670 -7.714769e-03 sample148 -0.0276016641 2.420589e-02 sample149 0.0027545308 -4.653007e-02 sample150 -0.0792296010 1.831289e-02 sample151 -0.0245894512 -8.991738e-03 sample152 0.0409796547 -1.907063e-02 sample153 0.0734301757 5.528780e-02 sample154 0.0557740684 -2.487723e-02 sample155 0.0689436560 -6.127635e-03 sample156 0.0212272938 -9.747423e-03 sample157 0.0911931194 6.355708e-03 sample158 0.0220840645 -3.016357e-03 sample159 -0.0513244242 1.304175e-02 sample160 0.0246213576 1.248444e-02 sample161 -0.0100369130 -1.805391e-02 sample162 0.1078802043 4.337260e-02 sample163 -0.1017965082 5.047171e-02 sample164 0.0119430799 9.593002e-04 sample165 -0.0063708014 -5.032148e-03 sample166 -0.0283181180 1.899222e-02 sample167 -0.0872832229 1.516582e-04 sample168 0.0540714512 1.397701e-02 sample169 0.0328432652 7.104347e-03 > o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1 [,1] [,2] sample1 0.0133684846 2.195848e-02 sample2 0.0254157197 -1.058416e-02 sample3 -0.0049551479 -4.840017e-03 sample4 0.0310390570 -1.063929e-02 sample5 0.0046941318 -6.488426e-03 sample6 -0.0107406753 -1.026702e-02 sample7 -0.0225157631 2.624712e-04 sample8 0.0141320952 -9.505821e-03 sample9 0.0029681280 2.078210e-02 sample10 0.0131729174 -2.275042e-03 sample11 -0.0004164298 1.994019e-02 sample12 -0.0095211620 3.759883e-02 sample13 0.0091018604 -7.953956e-03 sample14 -0.0106557524 -9.181659e-03 sample15 -0.0249924121 3.262724e-02 sample16 -0.0156216400 1.375700e-02 sample17 -0.0019382446 1.073994e-03 sample18 -0.0221072481 -8.703592e-03 sample19 0.0146917619 -1.311712e-02 sample20 -0.0160353760 1.826290e-02 sample21 0.0035947899 -9.616341e-03 sample22 -0.0225060762 -2.532589e-03 sample23 0.0310000683 3.033060e-03 sample24 0.0499544372 1.809450e-02 sample25 0.0284442301 -1.932558e-02 sample26 0.0188220043 2.146985e-02 sample27 -0.0257763219 -1.999228e-03 sample28 0.0120888648 1.125834e-02 sample29 -0.0236482520 4.426726e-02 sample30 -0.0385486305 -2.055935e-02 sample31 -0.0181539336 -5.877838e-03 sample32 -0.0302630460 -2.607192e-03 sample33 -0.0319565715 -1.562628e-02 sample34 -0.0197970124 9.906813e-03 sample35 -0.0247412713 -5.434440e-03 sample36 -0.0386259060 -3.190394e-02 sample37 -0.0566199273 -4.192574e-02 sample38 -0.0142060273 2.259644e-02 sample39 0.0053589035 1.076485e-02 sample40 -0.0552546493 -3.819896e-02 sample41 -0.0013089975 9.278818e-05 sample42 0.0137252142 -1.664652e-02 sample43 -0.0151259626 -6.290953e-03 sample44 0.0617391754 -1.442883e-02 sample45 0.0231410886 1.163143e-03 sample46 -0.0148898209 -1.384176e-04 sample47 -0.0187252536 1.221690e-02 sample48 0.0432839432 1.416671e-02 sample49 0.0160818605 -3.588745e-02 sample50 0.0059333545 4.067003e-02 sample51 -0.0142914866 7.776270e-03 sample52 -0.0086339952 7.208917e-03 sample53 -0.0207386980 6.272432e-03 sample54 -0.0039856719 -1.316934e-02 sample55 -0.0056217017 5.692315e-03 sample56 0.0000123292 8.978290e-04 sample57 -0.0095805555 1.324253e-02 sample58 -0.0124160295 -7.326376e-03 sample59 -0.0400195442 -1.349736e-02 sample60 -0.0460063358 2.770091e-02 sample61 -0.0245266456 1.470710e-02 sample62 -0.0366022783 -3.437352e-03 sample63 0.0013742171 3.288796e-02 sample64 -0.0070599859 2.739588e-02 sample65 0.0041201911 1.498268e-02 sample66 0.0143173351 -1.968812e-02 sample67 -0.0467477531 -1.929938e-02 sample68 -0.0306751978 -1.436184e-02 sample69 -0.0125317217 4.130407e-03 sample70 -0.0068071487 8.080857e-03 sample71 0.0169170264 -7.027348e-03 sample72 -0.0346909749 -1.333770e-02 sample73 -0.0280506153 1.493843e-02 sample74 -0.0182611498 3.294697e-03 sample75 -0.0120563964 8.974612e-03 sample76 0.0001437236 -4.253184e-02 sample77 0.0065330299 -5.252886e-02 sample78 0.0288278141 -1.127782e-02 sample79 0.0503961481 -1.023318e-02 sample80 -0.0207693429 3.648391e-02 sample81 0.0163562768 -9.074596e-03 sample82 -0.0084317129 -1.478976e-02 sample83 -0.0474097918 -1.103126e-02 sample84 0.0177181395 -7.191197e-03 sample85 -0.0342718548 -3.082360e-02 sample86 -0.0261671791 -1.089491e-02 sample87 -0.0009486358 -2.411514e-02 sample88 0.0020528931 -2.894615e-02 sample89 -0.0189361111 -2.638639e-03 sample90 -0.0009863658 -2.390075e-02 sample91 -0.0124352695 8.153234e-02 sample92 0.0564264106 -8.909537e-03 sample93 -0.0081461774 1.570851e-02 sample94 -0.0054896581 1.547251e-02 sample95 0.0224073150 -4.374348e-04 sample96 0.0173528924 -3.050441e-03 sample97 0.0067948115 5.008237e-03 sample98 -0.0116030825 1.498764e-02 sample99 0.0246422688 -4.054795e-03 sample100 -0.0069420745 -4.846343e-04 sample101 0.0124923691 3.091503e-02 sample102 0.0650835386 -1.367400e-02 sample103 -0.0042741828 7.855985e-03 sample104 0.0250591040 -4.171938e-03 sample105 0.0157516368 -3.121990e-02 sample106 0.0060593853 -5.101693e-03 sample107 -0.0098329626 1.044506e-02 sample108 0.0044269853 4.142036e-03 sample109 0.0572473486 1.517542e-02 sample110 0.0090474827 -5.119868e-03 sample111 0.0444263015 7.983232e-03 sample112 -0.0131765484 -9.696342e-04 sample113 0.0241047399 6.706740e-03 sample114 0.0074558775 -4.728652e-03 sample115 0.0611851433 1.117210e-02 sample116 0.0432646951 -1.380556e-02 sample117 0.0516750066 -3.575617e-02 sample118 0.0139942100 -3.279138e-03 sample119 0.0291722987 5.587946e-02 sample120 0.0103515853 -1.690016e-03 sample121 -0.0091396331 3.552116e-02 sample122 0.0260431679 -7.583975e-03 sample123 -0.0076666389 -1.628489e-02 sample124 0.0283466326 3.127845e-03 sample125 0.0016472378 -2.770692e-02 sample126 -0.0286529417 3.489336e-02 sample127 -0.0010224500 7.483214e-03 sample128 0.0209049296 2.572016e-02 sample129 -0.0218184878 -1.755347e-02 sample130 -0.0005009620 -1.697978e-02 sample131 -0.0134032968 4.637390e-03 sample132 0.0198526786 5.723983e-04 sample133 0.0088812957 -9.988115e-03 sample134 -0.0137484514 1.172591e-02 sample135 -0.0220314568 1.347465e-02 sample136 -0.0185173353 5.168079e-03 sample137 -0.0248352123 -9.472788e-03 sample138 0.0301635767 -1.175283e-02 sample139 -0.0173576929 -3.872592e-02 sample140 -0.0262157762 2.456863e-02 sample141 0.0058369763 -1.420854e-02 sample142 0.0207886071 -1.188764e-02 sample143 0.0092832598 -1.324238e-02 sample144 0.0028442140 3.627979e-03 sample145 0.0199749569 2.862202e-03 sample146 -0.0182236697 1.726556e-03 sample147 -0.0282519995 -2.825595e-02 sample148 0.0065435868 -1.572917e-02 sample149 0.0158233820 -2.159451e-02 sample150 -0.0177383738 -3.020633e-03 sample151 0.0245166984 -6.888241e-03 sample152 0.0107259913 3.314630e-02 sample153 0.0550963965 3.758760e-02 sample154 -0.0131452472 -8.153903e-04 sample155 -0.0211742574 2.642246e-03 sample156 -0.0117803505 2.698265e-02 sample157 -0.0096167165 1.433840e-02 sample158 -0.0101754772 9.137620e-03 sample159 0.0120662931 -2.565236e-02 sample160 -0.0132238202 2.916023e-03 sample161 0.0274491966 -1.748284e-02 sample162 0.0012482909 3.152261e-02 sample163 0.0042031315 1.830701e-02 sample164 0.0174896157 -1.175915e-02 sample165 0.0097517662 -6.119019e-03 sample166 0.0190134679 -1.121582e-02 sample167 -0.0044140836 4.665585e-03 sample168 0.0049689168 -1.941822e-02 sample169 -0.0209802098 3.498729e-03 > o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2 [,1] [,2] sample1 -0.0515543627 -0.0305856787 sample2 -0.0144993256 0.0236342950 sample3 -0.0371833108 -0.0140263348 sample4 0.0068945388 -0.0132539692 sample5 0.0215035333 -0.0663338101 sample6 -0.0187055152 0.0088773016 sample7 -0.0061521552 0.0064029054 sample8 -0.0210874459 0.0334652901 sample9 0.0516865043 -0.0291142799 sample10 0.0059440366 -0.0527217447 sample11 0.0393010793 -0.0200624712 sample12 -0.0420837100 0.0131331362 sample13 0.0333252565 0.0818552509 sample14 -0.0190062644 0.0160202175 sample15 -0.0030968049 -0.0189230681 sample16 -0.0004452158 0.0018880102 sample17 -0.0185848615 0.0240170131 sample18 -0.0273093598 0.0230213640 sample19 -0.0217761111 -0.0445894441 sample20 0.0245820821 0.0159812738 sample21 0.0034527644 -0.0400016054 sample22 -0.0340789054 0.0039289109 sample23 -0.0010344929 -0.0310161212 sample24 0.0289468503 0.0760962436 sample25 -0.0119098496 -0.0122798760 sample26 -0.0181001057 0.0517892852 sample27 0.0050465417 -0.0086515844 sample28 0.0057491502 0.0358830107 sample29 -0.0051104246 0.0116605117 sample30 -0.0103085904 0.0039678538 sample31 -0.0319929858 0.0090606113 sample32 -0.0036232521 -0.0328202010 sample33 -0.0534742153 0.0024751837 sample34 -0.0067495749 -0.0111000311 sample35 0.0378745721 0.0465929296 sample36 0.0647886800 0.0359987924 sample37 0.0488441236 0.0492906912 sample38 -0.0251514062 0.0197110110 sample39 -0.0085428066 -0.0105117852 sample40 0.0379324087 0.0440810741 sample41 -0.0044199152 -0.0128820644 sample42 -0.0292553573 -0.0067045265 sample43 -0.0077829155 -0.0510178219 sample44 0.0045122248 0.0479660309 sample45 -0.0074444298 -0.0051116726 sample46 -0.0088025512 0.0196186661 sample47 0.0076696301 0.0215947965 sample48 0.0290108585 -0.0175568376 sample49 -0.0141754858 0.0184717099 sample50 0.0006282201 -0.0233054373 sample51 0.0441995177 -0.0410022921 sample52 0.0715329391 -0.0399499475 sample53 -0.0095954087 -0.0029140909 sample54 0.0048933768 -0.0281884386 sample55 0.0327325487 -0.0532290012 sample56 0.0323068984 -0.0256595538 sample57 0.0806603122 -0.0286748097 sample58 -0.0064792049 -0.0006945349 sample59 0.0088958941 0.0067389649 sample60 0.0874124612 0.0431964341 sample61 0.0577604571 -0.0326112099 sample62 -0.0313318464 0.0224391756 sample63 -0.0233625220 0.0125110562 sample64 -0.0086426068 0.0148770341 sample65 0.0025256193 -0.0404466327 sample66 0.0006014071 -0.0471576264 sample67 0.0706087042 0.0516228406 sample68 0.0082301011 0.0033109509 sample69 -0.0475076743 0.0001452708 sample70 -0.0600773716 0.0089986962 sample71 -0.0096321627 -0.0050761187 sample72 -0.0031773546 -0.0166221542 sample73 -0.0113700517 -0.0191726684 sample74 -0.0014179662 -0.0608101325 sample75 0.0041911740 -0.0399981269 sample76 -0.0055326449 0.0353114263 sample77 -0.0260214459 0.0305731380 sample78 -0.0119267436 0.0632236007 sample79 0.0186017239 0.0027402910 sample80 0.0241047889 -0.0472697181 sample81 -0.0220288317 -0.0079577210 sample82 -0.0180751258 0.0639051029 sample83 -0.0256671713 -0.0125898269 sample84 0.0161392598 -0.0567222449 sample85 0.0139988188 0.0322763454 sample86 -0.0198382995 0.0389225776 sample87 0.0266270281 -0.0032979996 sample88 0.0515677078 0.0117902495 sample89 0.0014022125 -0.0140510488 sample90 -0.0375949749 0.0044004551 sample91 0.0310397965 0.0440610926 sample92 0.0270570567 0.0324380452 sample93 -0.0215009202 0.0063993941 sample94 -0.0415702912 -0.0037692077 sample95 -0.0168416047 0.0010019120 sample96 -0.0285582661 -0.0187991000 sample97 -0.0490843868 -0.0266760748 sample98 -0.0171579033 -0.0112897471 sample99 -0.0271316525 0.0232395583 sample100 -0.0301789816 0.0305498693 sample101 -0.0264371151 0.0170723968 sample102 0.0012767734 -0.0248949597 sample103 0.0055214687 -0.0030040587 sample104 0.0251346074 -0.0165212671 sample105 0.0062424215 -0.0400309901 sample106 0.0069768684 0.0154982315 sample107 -0.0315912602 -0.0118883820 sample108 -0.0109690679 0.0023637162 sample109 -0.0014762845 0.0165583675 sample110 0.0036971063 0.0168260726 sample111 -0.0071624739 -0.0345651461 sample112 0.0046098120 -0.0048009350 sample113 0.0082236008 -0.0383233357 sample114 -0.0293642209 -0.0165595240 sample115 -0.0003260453 0.0135805368 sample116 0.0183575759 0.0665377581 sample117 0.0227640036 -0.0012287760 sample118 0.0015695248 0.0472617382 sample119 0.0190084932 0.0590034062 sample120 -0.0449645755 0.0072755697 sample121 0.0077307184 0.0104738937 sample122 -0.0027132063 -0.0394983138 sample123 0.0016959300 0.0028593594 sample124 -0.0365091615 0.0040382925 sample125 -0.0053658663 -0.0316029164 sample126 -0.0458032408 0.0019165544 sample127 -0.0494064872 0.0088209044 sample128 -0.0155454766 0.0186819802 sample129 -0.0184340400 0.0038684312 sample130 -0.0303640987 -0.0052225766 sample131 -0.0088697422 0.0156339713 sample132 -0.0433916471 -0.0154075483 sample133 0.0204029276 -0.0282209049 sample134 0.0175513332 0.0262883962 sample135 0.0029009925 0.0017003151 sample136 -0.0367997573 -0.0072249751 sample137 -0.0348600323 0.0075400273 sample138 -0.0044063824 -0.0053752428 sample139 0.0073103935 0.0308956174 sample140 0.0039925654 -0.0167019605 sample141 -0.0184093462 -0.0387953445 sample142 0.0268670676 -0.0239229634 sample143 0.0421049126 -0.0110888235 sample144 0.0017253664 -0.0341766012 sample145 0.0681741320 -0.0073526377 sample146 -0.0239965222 0.0118396767 sample147 -0.0063453522 0.0183130585 sample148 0.0230825251 -0.0379753037 sample149 0.0223298673 0.0188909118 sample150 0.0055709108 0.0174179009 sample151 0.0039177786 -0.0233533275 sample152 0.0134325667 0.0302344591 sample153 0.0511990309 0.0730230140 sample154 0.0006698324 0.0154177486 sample155 0.0032926626 -0.0288651601 sample156 -0.0016463495 -0.0474657733 sample157 -0.0045857599 0.0154934573 sample158 0.0201775524 -0.0332982124 sample159 -0.0086909001 0.0073496711 sample160 0.0295437331 -0.0555734536 sample161 0.0332754288 0.0033779619 sample162 0.0121954537 0.0433540412 sample163 -0.0173490933 0.0227219128 sample164 0.0143374783 -0.0453542590 sample165 0.0343612593 -0.0511194536 sample166 -0.0157536004 0.0094621170 sample167 -0.0179654624 -0.0006982358 sample168 -0.0033829919 0.0060747155 sample169 0.0116231468 -0.0015112800 > > ## 3.3 Plotting VAF > > # DISCO-SCA plotVAF > plotVAF(discoRes) > > # JIVE plotVAF > plotVAF(jiveRes) > > > ######################### > ## PART 4. Plot Results > > # Scores for common part. DISCO-SCA > plotRes(object=discoRes,comps=c(1,2),what="scores",type="common", + combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > > # Scores for common part. JIVE > plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common", + combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > > # Scores for common part. O2PLS. > p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > legend <- g_legend(p1) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + legend,heights=c(6/7,1/7)) > > # Combined plot of scores for common part. O2PLS. > plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common", + combined=TRUE,block=NULL,color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > > > # Scores for distinctive part. DISCO-SCA. (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > legend <- g_legend(p1) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + legend,heights=c(6/7,1/7)) > > # Combined plot of scores for distinctive part. DISCO-SCA > plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual", + combined=TRUE,block=NULL,color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > > # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block) > p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > legend <- g_legend(p1) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + legend,heights=c(6/7,1/7)) > > # Combined plot of scores for common and distinctive part. DISCO (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > legend <- g_legend(p1) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + legend,heights=c(6/7,1/7)) > > # Loadings for common part. DISCO-SCA. (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > # Loadings for distinctive part. DISCO-SCA. (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > # Combined plot for loadings from common and distinctive part (two plots one for each block) > p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both", + combined=FALSE,block="expr",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both", + combined=FALSE,block="mirna",color="classname",shape=NULL, + labels=NULL,background=TRUE,palette=NULL,pointSize=4, + labelSize=NULL,axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > > ## Plot scores and loadings togheter: Common components DISCO-SCA > p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common", + combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common", + combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > ## Plot scores and loadings togheter: Common components O2PLS > p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common", + combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common", + combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > ## Plot scores and loadings togheter: Distintive components DISCO-SCA > p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual", + combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual", + combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL, + background=TRUE,palette=NULL,pointSize=4,labelSize=NULL, + axisSize=NULL,titleSize=NULL) > grid.arrange(arrangeGrob(p1+theme(legend.position="none"), + p2+theme(legend.position="none"),nrow=1), + heights=c(6/7,1/7)) > > > > > proc.time() user system elapsed 14.678 0.726 15.554
STATegRa.Rcheck/STATegRa-Ex.timings
name | user | system | elapsed | |
STATegRaUsersGuide | 0.000 | 0.000 | 0.001 | |
STATegRa_data | 0.249 | 0.019 | 0.271 | |
STATegRa_data_TCGA_BRCA | 0.002 | 0.001 | 0.004 | |
bioDist | 0.608 | 0.046 | 0.663 | |
bioDistFeature | 0.357 | 0.029 | 0.390 | |
bioDistFeaturePlot | 0.355 | 0.024 | 0.382 | |
bioDistW | 0.354 | 0.024 | 0.387 | |
bioDistWPlot | 0.342 | 0.024 | 0.370 | |
bioMap | 0.003 | 0.002 | 0.004 | |
combiningMappings | 0.012 | 0.002 | 0.013 | |
createOmicsExpressionSet | 0.133 | 0.004 | 0.138 | |
getInitialData | 0.727 | 0.167 | 0.903 | |
getLoadings | 0.770 | 0.202 | 0.982 | |
getMethodInfo | 0.873 | 0.136 | 1.020 | |
getPreprocessing | 0.985 | 0.705 | 1.718 | |
getScores | 0.888 | 0.137 | 1.037 | |
getVAF | 0.763 | 0.131 | 0.900 | |
holistOmics | 0.003 | 0.002 | 0.004 | |
modelSelection | 1.762 | 1.539 | 3.342 | |
omicsCompAnalysis | 4.576 | 0.266 | 4.891 | |
omicsNPC | 0.002 | 0.002 | 0.004 | |
plotRes | 5.523 | 0.248 | 5.826 | |
plotVAF | 4.967 | 0.237 | 5.245 | |