David Gomez-Cabrero
| Snapshot Date: 2018-04-11 16:45:18 -0400 (Wed, 11 Apr 2018) |
| URL: https://git.bioconductor.org/packages/STATegRa |
| Branch: RELEASE_3_6 |
| Last Commit: 393cfc1 |
| Last Changed Date: 2017-10-30 12:40:43 -0400 (Mon, 30 Oct 2017) |
| Back to Multiple platform build/check report for BioC 3.6 |
|
This page was generated on 2018-04-12 13:39:40 -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: /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --no-vignettes --timings STATegRa_1.12.0.tar.gz |
| StartedAt: 2018-04-12 10:02:21 -0400 (Thu, 12 Apr 2018) |
| EndedAt: 2018-04-12 10:06:34 -0400 (Thu, 12 Apr 2018) |
| EllapsedTime: 252.8 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: STATegRa.Rcheck |
| Warnings: 0 |
##############################################################################
##############################################################################
###
### Running command:
###
### /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --no-vignettes --timings STATegRa_1.12.0.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/Users/biocbuild/bbs-3.6-bioc/meat/STATegRa.Rcheck’
* using R version 3.4.4 (2018-03-15)
* using platform: x86_64-apple-darwin15.6.0 (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 6.5Mb
sub-directories of 1Mb or more:
data 2.4Mb
doc 3.7Mb
* 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 6.668 0.188 6.967
plotRes 5.224 0.258 5.552
plotVAF 4.910 0.263 5.219
* 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
‘/Users/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-apple-darwin15.6.0 (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 10:06:29 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
4.681 0.182 5.251
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-apple-darwin15.6.0 (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
30.946 0.734 32.680
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-apple-darwin15.6.0 (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
76.032 1.133 78.782
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-apple-darwin15.6.0 (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 0.0781574325 -0.0431501870
sample2 -0.1192218398 0.0294088886
sample3 -0.0531412124 -0.0746839836
sample4 0.0292975172 -0.0005959647
sample5 0.0202091782 0.0110463672
sample6 0.1226089051 0.1053466979
sample7 0.1078928093 -0.0322476029
sample8 0.1782895307 0.1449364210
sample9 0.0468698140 -0.0455174365
sample10 -0.0036030489 0.0420111120
sample11 -0.0035566462 -0.0566292661
sample12 0.1006128901 0.0641380795
sample13 -0.1174408349 0.0907488497
sample14 0.0981203250 0.0617738030
sample15 0.0085334303 -0.0087013776
sample16 0.0783148663 0.1581294298
sample17 -0.1483609924 0.0638581976
sample18 -0.0963086272 0.0556639953
sample19 -0.0217244083 -0.0720086011
sample20 -0.0635636418 -0.0779653336
sample21 -0.0201840319 0.1566391347
sample22 0.0218268707 -0.0764104642
sample23 0.0852042044 -0.0032688429
sample24 -0.1287170604 0.1924544249
sample25 -0.0430574137 -0.0456565784
sample26 -0.1453896837 0.0541512514
sample27 -0.0197488830 -0.1185657165
sample28 -0.1025336299 0.0650685736
sample29 0.0706018473 -0.0682988333
sample30 -0.1295627559 -0.0066769808
sample31 0.1147449113 0.1232686440
sample32 -0.0374310885 0.0380177548
sample33 0.0599515964 0.0136866857
sample34 -0.0984200823 0.0375320832
sample35 -0.0543098394 -0.0378106288
sample36 0.1403625416 -0.0343756799
sample37 0.0228941832 -0.0732847157
sample38 -0.0222077277 -0.0962594906
sample39 -0.0941738484 0.0215199297
sample40 0.0643801106 -0.0687871835
sample41 -0.0327638032 -0.1232188178
sample42 -0.0500431838 -0.0292473139
sample43 -0.0184498821 0.0233010836
sample44 0.1487898816 0.1171355138
sample45 -0.1050774152 0.1123201884
sample46 -0.1151195742 -0.1094028924
sample47 -0.0962593745 -0.0288463964
sample48 0.0004837362 -0.0310277302
sample49 0.1135207796 0.1213973179
sample50 -0.0123553126 -0.1740743561
sample51 0.0550529889 0.1258886382
sample52 0.0499121251 0.0728544245
sample53 0.1119773654 0.1588013569
sample54 -0.0360055671 0.0228575488
sample55 0.0210419010 0.0006731542
sample56 -0.0434169213 0.0633125980
sample57 0.0197824640 0.1150713263
sample58 0.0030439884 0.0326097803
sample59 0.0500253093 0.0129418157
sample60 0.0184278640 0.0136084445
sample61 0.0150299423 0.0635025293
sample62 -0.0304763936 -0.0201319825
sample63 0.1102252486 0.1285977086
sample64 0.1552588091 0.0971168287
sample65 -0.0058503040 0.0207115521
sample66 -0.0025605326 0.0424320130
sample67 0.1546634784 -0.0661717465
sample68 0.0536369241 -0.0923684333
sample69 0.0640330351 0.0081982962
sample70 0.0163517718 -0.0663230066
sample71 -0.0102537634 -0.1345921058
sample72 -0.0654196071 -0.0196120217
sample73 -0.1048556155 0.0220937782
sample74 0.0123799482 0.0586114738
sample75 0.0392077936 -0.0209755300
sample76 0.0648953362 -0.0524764461
sample77 0.1172922112 -0.0201186622
sample78 -0.1463068077 0.0708473060
sample79 0.0265211218 -0.1603307002
sample80 0.0279737152 -0.0214205553
sample81 0.0079211493 -0.0738450489
sample82 -0.1544236538 -0.0361468014
sample83 -0.0494211445 -0.0050049449
sample84 -0.0259038447 -0.0346549291
sample85 0.1116484318 -0.0031498548
sample86 -0.1306483076 -0.0377215679
sample87 -0.0554778203 -0.0459749064
sample88 -0.0301623830 0.0382197429
sample89 -0.1016866720 0.0694033408
sample90 0.0086819859 -0.0201320092
sample91 0.1578625307 -0.2097828213
sample92 0.0170936861 -0.1655805755
sample93 -0.0979806835 -0.0121512323
sample94 0.0131484079 -0.0114932076
sample95 0.0315682632 -0.0758858556
sample96 0.0024125618 -0.0470135105
sample97 0.0634545413 0.0270332174
sample98 -0.0359374653 -0.0135488638
sample99 -0.1009163295 0.1124780714
sample100 0.0551753113 0.0246489485
sample101 -0.0080118915 -0.1627368080
sample102 -0.0046444243 0.0095633503
sample103 -0.0472523195 -0.0940393396
sample104 0.0198159510 -0.0591091055
sample105 -0.0400237780 -0.0160911699
sample106 -0.0923808405 0.0369017866
sample107 -0.1019373961 0.0224954035
sample108 -0.0877091655 -0.0128834032
sample109 0.0864824429 -0.0900940223
sample110 -0.1223115533 -0.0096085474
sample111 0.0257354666 -0.0936167957
sample112 -0.0765286612 0.0270347216
sample113 0.0258803283 0.0377497836
sample114 0.0021138905 -0.0882014623
sample115 0.0303460255 -0.0723584026
sample116 0.0780508455 -0.0685065275
sample117 0.0536898150 -0.0911907186
sample118 0.0666651156 -0.0236230688
sample119 0.1021871627 -0.2324935940
sample120 0.0750216554 0.0243379532
sample121 -0.0756936386 0.0942950423
sample122 -0.0259628039 0.0731987879
sample123 -0.1037846274 -0.0369197439
sample124 0.0611207951 0.0421724533
sample125 -0.0738472716 0.0066950132
sample126 0.0972916411 0.0762639430
sample127 0.0824697611 -0.0096637179
sample128 -0.1249407611 0.0929313391
sample129 -0.0734067560 -0.0434363481
sample130 -0.0003502024 -0.0309852631
sample131 0.0930182796 0.0155936790
sample132 0.0736222835 0.0733030482
sample133 -0.0498397968 -0.0462437304
sample134 0.1644873494 0.0720005188
sample135 -0.0752297223 0.0003817185
sample136 0.0227145731 -0.0495506417
sample137 0.0564717362 -0.0288916356
sample138 0.0255988131 -0.0610856213
sample139 0.0621217781 0.0235807097
sample140 -0.0604152568 -0.0435593922
sample141 0.0246743982 0.0532648859
sample142 -0.0409560289 0.0316280327
sample143 -0.0077355206 -0.0476896183
sample144 0.0173240830 -0.0156777873
sample145 0.0485474590 0.1202770944
sample146 0.0419645587 -0.0811281727
sample147 -0.0977308398 -0.0274840848
sample148 0.0368256218 0.0803979726
sample149 -0.0072865801 -0.1532985683
sample150 0.1020825279 0.0624774105
sample151 0.0305399090 -0.0289277556
sample152 -0.0533594794 -0.0638308754
sample153 -0.0891627542 0.1799579847
sample154 -0.0727557505 -0.0834161251
sample155 -0.0880668589 -0.0220820160
sample156 -0.0276561060 -0.0326625577
sample157 -0.1155032198 0.0183615897
sample158 -0.0281507523 -0.0104938960
sample159 0.0663235724 0.0443837626
sample160 -0.0302643880 0.0404265020
sample161 0.0114715595 -0.0591024846
sample162 -0.1337087094 0.1398135600
sample163 0.1330124509 0.1688781468
sample164 -0.0150336065 0.0028416586
sample165 0.0076520298 -0.0164128255
sample166 0.0367794397 0.0630662585
sample167 0.1111988856 0.0030057777
sample168 -0.0672981592 0.0446279433
sample169 -0.0413004988 0.0224393676
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 0.0420515337 0.0867863065
sample2 0.0820828370 -0.0410978156
sample3 -0.0155899106 -0.0195182297
sample4 0.1001337038 -0.0410786816
sample5 0.0153465806 -0.0253259708
sample6 -0.0340326762 -0.0408223219
sample7 -0.0722579529 0.0002332352
sample8 0.0457498700 -0.0370016383
sample9 0.0086249640 0.0820184916
sample10 0.0423598255 -0.0083923363
sample11 -0.0022548127 0.0787766080
sample12 -0.0322106051 0.1479824690
sample13 0.0293888958 -0.0306748721
sample14 -0.0337483232 -0.0367506854
sample15 -0.0815538861 0.1275622607
sample16 -0.0508453461 0.0540604622
sample17 -0.0062597130 0.0041023690
sample18 -0.0705640242 -0.0351047624
sample19 0.0476842308 -0.0509598101
sample20 -0.0522962096 0.0715521980
sample21 0.0119125326 -0.0376093201
sample22 -0.0724392412 -0.0095624966
sample23 0.0992532178 0.0134288656
sample24 0.1595116570 0.0728661678
sample25 0.0920693694 -0.0749757361
sample26 0.0595539864 0.0848965930
sample27 -0.0826484669 -0.0086735237
sample28 0.0384787718 0.0440966782
sample29 -0.0777670721 0.1735308663
sample30 -0.1229471278 -0.0819005342
sample31 -0.0579847430 -0.0238644754
sample32 -0.0970393479 -0.0111426186
sample33 -0.1017588111 -0.0630442433
sample34 -0.0637923079 0.0377941781
sample35 -0.0789984251 -0.0229723090
sample36 -0.1224939411 -0.1274954761
sample37 -0.1798820533 -0.1673427138
sample38 -0.0466303461 0.0888161073
sample39 0.0168687583 0.0421533722
sample40 -0.1756391860 -0.1526642074
sample41 -0.0042369680 0.0004928889
sample42 0.0447849964 -0.0651505045
sample43 -0.0482308632 -0.0253529215
sample44 0.1986713584 -0.0545778231
sample45 0.0741835516 0.0054703112
sample46 -0.0478771093 -0.0007071877
sample47 -0.0608188176 0.0481622743
sample48 0.1381489711 0.0578287597
sample49 0.0530519105 -0.1405532990
sample50 0.0173801645 0.1602389757
sample51 -0.0462562257 0.0303473819
sample52 -0.0280065861 0.0280388382
sample53 -0.0667622854 0.0237702032
sample54 -0.0121833841 -0.0521354317
sample55 -0.0182395972 0.0221328458
sample56 0.0001254918 0.0030907318
sample57 -0.0316676672 0.0530190247
sample58 -0.0393918501 -0.0297798710
sample59 -0.1278291165 -0.0546527782
sample60 -0.1486985324 0.1069156751
sample61 -0.0793123162 0.0569796594
sample62 -0.1172800765 -0.0149198305
sample63 0.0028725774 0.1300519744
sample64 -0.0237365481 0.1073287685
sample65 0.0126534807 0.0589808409
sample66 0.0468194276 -0.0771072784
sample67 -0.1494264339 -0.0769860052
sample68 -0.0977960452 -0.0577350856
sample69 -0.0403087252 0.0156042172
sample70 -0.0221530436 0.0315441037
sample71 0.0546435878 -0.0272396427
sample72 -0.1107487651 -0.0537319200
sample73 -0.0906761294 0.0579966728
sample74 -0.0586555934 0.0121421723
sample75 -0.0390493033 0.0349282883
sample76 0.0022960948 -0.1676558766
sample77 0.0232096114 -0.2067302820
sample78 0.0929754278 -0.0434939664
sample79 0.1619498241 -0.0378114364
sample80 -0.0680365163 0.1424663605
sample81 0.0530784796 -0.0358350872
sample82 -0.0266821589 -0.0577445042
sample83 -0.1517235118 -0.0448554115
sample84 0.0570967394 -0.0273813296
sample85 -0.1086289955 -0.1228119182
sample86 -0.0833859532 -0.0442914838
sample87 -0.0022018143 -0.0943906822
sample88 0.0078224219 -0.1140506573
sample89 -0.0611057907 -0.0094585107
sample90 -0.0022927944 -0.0936253973
sample91 -0.0433588096 0.3205982973
sample92 0.1815337016 -0.0334680444
sample93 -0.0267630436 0.0614429077
sample94 -0.0181877448 0.0605090445
sample95 0.0720376504 -0.0013045705
sample96 0.0559715316 -0.0118791460
sample97 0.0217410900 0.0195414102
sample98 -0.0379177099 0.0588357172
sample99 0.0792426264 -0.0151273981
sample100 -0.0222116620 -0.0023321419
sample101 0.0387230488 0.1224226272
sample102 0.2094613998 -0.0516442914
sample103 -0.0138480224 0.0301052036
sample104 0.0807987511 -0.0162718993
sample105 0.0520493331 -0.1229665224
sample106 0.0192612915 -0.0185238235
sample107 -0.0319017181 0.0405123326
sample108 0.0140691219 0.0163421374
sample109 0.1831931070 0.0613007363
sample110 0.0292790701 -0.0199849113
sample111 0.1423253039 0.0327340196
sample112 -0.0426333103 -0.0029083397
sample113 0.0771904174 0.0268733517
sample114 0.0241642368 -0.0184080387
sample115 0.1959016373 0.0460130455
sample116 0.1394476490 -0.0530805963
sample117 0.1672362408 -0.1386536576
sample118 0.0448344450 -0.0117621985
sample119 0.0910388914 0.2217433400
sample120 0.0331392059 -0.0057274555
sample121 -0.0307575587 0.1392506527
sample122 0.0839780425 -0.0291994580
sample123 -0.0239650104 -0.0642163666
sample124 0.0909150305 0.0130419351
sample125 0.0065350677 -0.1092631822
sample126 -0.0935312298 0.1368284127
sample127 -0.0035387654 0.0292755649
sample128 0.0660294818 0.1018566179
sample129 -0.0693638187 -0.0695421612
sample130 -0.0008493150 -0.0669704303
sample131 -0.0431024161 0.0174064913
sample132 0.0637039495 0.0029374592
sample133 0.0289495145 -0.0390818839
sample134 -0.0446203790 0.0456334512
sample135 -0.0712336871 0.0521635053
sample136 -0.0596270379 0.0197299439
sample137 -0.0793151671 -0.0380628178
sample138 0.0973548877 -0.0454218354
sample139 -0.0539905257 -0.1534327310
sample140 -0.0850826403 0.0955814678
sample141 0.0192681258 -0.0554450124
sample142 0.0672261576 -0.0461321025
sample143 0.0303730711 -0.0519260252
sample144 0.0089364761 0.0145814914
sample145 0.0638768562 0.0122258239
sample146 -0.0585855452 0.0063083481
sample147 -0.0894133186 -0.1124615561
sample148 0.0216365990 -0.0615967207
sample149 0.0515422032 -0.0839903467
sample150 -0.0568284049 -0.0124468891
sample151 0.0789532625 -0.0261831272
sample152 0.0330754302 0.1306443581
sample153 0.1751929598 0.1497731746
sample154 -0.0421423485 -0.0037010083
sample155 -0.0680177225 0.0095711340
sample156 -0.0388910622 0.1057563037
sample157 -0.0314769442 0.0561367458
sample158 -0.0329620415 0.0353947375
sample159 0.0398415962 -0.1007373857
sample160 -0.0424939182 0.0108496210
sample161 0.0888371702 -0.0679700256
sample162 0.0027474743 0.1237843769
sample163 0.0126103681 0.0725434216
sample164 0.0566779547 -0.0458324257
sample165 0.0315336437 -0.0236362381
sample166 0.0612057471 -0.0425233159
sample167 -0.0142729871 0.0179308291
sample168 0.0169502967 -0.0769617947
sample169 -0.0675080564 0.0131505406
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 -0.001232964 1.635717e-01
sample2 -0.072435005 6.021261e-03
sample3 -0.018846045 1.080036e-01
sample4 0.039014529 -3.114142e-04
sample5 0.177481164 2.996384e-02
sample6 -0.045144440 3.455858e-02
sample7 -0.022646627 7.020162e-03
sample8 -0.103368020 9.856784e-03
sample9 0.135001171 -8.979098e-02
sample10 0.125988727 5.097852e-02
sample11 0.097978834 -7.086534e-02
sample12 -0.086301908 8.620317e-02
sample13 -0.138140111 -1.828007e-01
sample14 -0.061507385 2.642803e-02
sample15 0.038159895 3.101664e-02
sample16 -0.004877670 -1.271842e-03
sample17 -0.078848092 1.547554e-02
sample18 -0.088418874 3.795487e-02
sample19 0.070304442 1.084004e-01
sample20 -0.002558556 -7.975874e-02
sample21 0.094160171 4.126741e-02
sample22 -0.055027343 7.806744e-02
sample23 0.067949531 4.102005e-02
sample24 -0.131096275 -1.649309e-01
sample25 0.011358528 4.426863e-02
sample26 -0.140294589 -2.016543e-02
sample27 0.026156109 -1.588443e-03
sample28 -0.072419870 -5.850592e-02
sample29 -0.033005860 -2.060825e-03
sample30 -0.022875256 2.015430e-02
sample31 -0.063506791 6.670334e-02
sample32 0.068509966 4.955273e-02
sample33 -0.077776521 1.272078e-01
sample34 0.015784243 3.024314e-02
sample35 -0.052963279 -1.500972e-01
sample36 0.007090071 -2.025307e-01
sample37 -0.044242066 -1.802089e-01
sample38 -0.078151132 3.676420e-02
sample39 0.012033187 3.388841e-02
sample40 -0.047329213 -1.471561e-01
sample41 0.022818938 2.673555e-02
sample42 -0.024536023 7.960867e-02
sample43 0.103636282 8.229577e-02
sample44 -0.101222877 -7.049451e-02
sample45 0.001373209 2.450911e-02
sample46 -0.055851005 -2.947380e-03
sample47 -0.038048119 -4.554173e-02
sample48 0.078434208 -4.888981e-02
sample49 -0.060516393 1.162355e-02
sample50 0.053007922 2.737933e-02
sample51 0.151464655 -5.678345e-02
sample52 0.186093523 -1.246717e-01
sample53 -0.006417706 2.700993e-02
sample54 0.069703835 2.308389e-02
sample55 0.163357703 -1.366442e-02
sample56 0.101148511 -4.682205e-02
sample57 0.173037422 -1.609603e-01
sample58 -0.007138470 1.666955e-02
sample59 -0.003046170 -3.005285e-02
sample60 0.021583509 -2.665877e-01
sample61 0.151058363 -1.002385e-01
sample62 -0.092553396 4.845842e-02
sample63 -0.059631176 4.137022e-02
sample64 -0.044922578 2.600580e-03
sample65 0.093938377 4.406909e-02
sample66 0.106340078 5.709992e-02
sample67 -0.020159009 -2.361728e-01
sample68 0.003720316 -2.418389e-02
sample69 -0.064516120 1.155622e-01
sample70 -0.101344002 1.351789e-01
sample71 -0.001646790 2.976842e-02
sample72 0.032889301 2.835858e-02
sample73 0.027508005 5.148186e-02
sample74 0.134171971 7.895280e-02
sample75 0.095157566 3.943184e-02
sample76 -0.086472198 -3.034991e-02
sample77 -0.103574957 2.545354e-02
sample78 -0.157564410 -4.939594e-02
sample79 0.018913704 -4.874679e-02
sample80 0.138414057 -4.265465e-05
sample81 -0.011884647 6.357932e-02
sample82 -0.167530818 -3.533911e-02
sample83 -0.006567341 7.812610e-02
sample84 0.148689161 3.109057e-02
sample85 -0.053272445 -7.417884e-02
sample86 -0.113847735 1.915793e-05
sample87 0.043286397 -6.080472e-02
sample88 0.043345036 -1.402491e-01
sample89 0.033120580 1.395401e-02
sample90 -0.060741281 8.610414e-02
sample91 -0.056627274 -1.303747e-01
sample92 -0.035958255 -1.061604e-01
sample93 -0.043364636 4.443635e-02
sample94 -0.047729129 1.059574e-01
sample95 -0.024959578 3.980525e-02
sample96 0.003521902 9.293928e-02
sample97 -0.006604872 1.527231e-01
sample98 0.002036682 5.579550e-02
sample99 -0.088661604 3.728226e-02
sample100 -0.109125913 3.560420e-02
sample101 -0.073972652 4.317999e-02
sample102 0.057446119 2.783914e-02
sample103 0.014273099 -9.705557e-03
sample104 0.071039519 -4.068351e-02
sample105 0.098083136 3.452952e-02
sample106 -0.025425930 -3.628984e-02
sample107 -0.016065342 9.173394e-02
sample108 -0.020098765 2.379692e-02
sample109 -0.038978067 -1.692359e-02
sample110 -0.032630484 -2.988109e-02
sample111 0.067693756 6.038212e-02
sample112 0.016788344 -5.336938e-03
sample113 0.096921704 2.757603e-02
sample114 -0.002639836 9.209157e-02
sample115 -0.030804732 -1.603823e-02
sample116 -0.124030720 -1.273000e-01
sample117 0.033472906 -5.392710e-02
sample118 -0.103715293 -6.252430e-02
sample119 -0.106417675 -1.196202e-01
sample120 -0.077135507 1.004933e-01
sample121 -0.012935073 -3.181976e-02
sample122 0.084749233 5.568326e-02
sample123 -0.004133679 -7.693179e-03
sample124 -0.058345797 8.396389e-02
sample125 0.063484462 5.232540e-02
sample126 -0.066258093 1.091733e-01
sample127 -0.086502460 1.094176e-01
sample128 -0.062781739 1.470963e-02
sample129 -0.033627646 4.007859e-02
sample130 -0.029351774 8.046117e-02
sample131 -0.046919767 2.209751e-03
sample132 -0.024174063 1.248598e-01
sample133 0.090730320 -1.466701e-02
sample134 -0.035084209 -7.539662e-02
sample135 0.000133340 -9.185378e-03
sample136 -0.033587607 9.860274e-02
sample137 -0.064014892 7.554470e-02
sample138 0.006096484 1.742762e-02
sample139 -0.059208447 -5.614969e-02
sample140 0.042798591 1.099551e-02
sample141 0.061879642 9.301038e-02
sample142 0.089855448 -3.573418e-02
sample143 0.081738919 -8.880524e-02
sample144 0.078775478 3.821391e-02
sample145 0.108582160 -1.569476e-01
sample146 -0.058955797 4.373360e-02
sample147 -0.049533045 -7.277198e-03
sample148 0.116159282 -9.079087e-03
sample149 -0.012157951 -7.788374e-02
sample150 -0.031451254 -3.520212e-02
sample151 0.057538217 1.945352e-02
sample152 -0.049454213 -7.025537e-02
sample153 -0.094133269 -2.153297e-01
sample154 -0.033593204 -2.078728e-02
sample155 0.069045764 2.780410e-02
sample156 0.103990162 6.292525e-02
sample157 -0.040864578 -8.065515e-03
sample158 0.101810530 -7.816877e-03
sample159 -0.028173052 1.207206e-02
sample160 0.164305302 -2.978108e-03
sample161 0.037432923 -8.524611e-02
sample162 -0.080453528 -8.349755e-02
sample163 -0.074322793 1.406225e-02
sample164 0.120880603 2.139460e-02
sample165 0.160811591 -2.025192e-02
sample166 -0.042594461 2.660714e-02
sample167 -0.022684948 4.464282e-02
sample168 -0.018073556 7.466190e-04
sample169 0.019077900 -2.645402e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
[,1] [,2]
sample1 -0.0572060227 -1.729087e-02
sample2 0.0875245208 1.112588e-02
sample3 0.0403482602 -3.168994e-02
sample4 -0.0218345996 4.052760e-06
sample5 -0.0150905011 4.795041e-03
sample6 -0.0924362933 4.511003e-02
sample7 -0.0793066751 -1.243823e-02
sample8 -0.1342997187 6.215220e-02
sample9 -0.0338886944 -1.854401e-02
sample10 0.0020547173 1.749421e-02
sample11 0.0037275602 -2.364116e-02
sample12 -0.0753094533 2.772698e-02
sample13 0.0856160091 3.679963e-02
sample14 -0.0737457307 2.668452e-02
sample15 -0.0062111746 -3.554864e-03
sample16 -0.0602355268 6.675115e-02
sample17 0.1086768843 2.524534e-02
sample18 0.0702999472 2.231671e-02
sample19 0.0173785882 -3.024846e-02
sample20 0.0484173812 -3.310904e-02
sample21 0.0124657042 6.517144e-02
sample22 -0.0140989936 -3.159137e-02
sample23 -0.0627028403 -5.393710e-04
sample24 0.0919972100 7.909297e-02
sample25 0.0326998483 -1.945206e-02
sample26 0.1064741246 2.120849e-02
sample27 0.0166058995 -4.964993e-02
sample28 0.0743504770 2.614211e-02
sample29 -0.0511008491 -2.782647e-02
sample30 0.0962250842 -3.974893e-03
sample31 -0.0869563008 5.250819e-02
sample32 0.0271858919 1.552005e-02
sample33 -0.0448364581 6.243160e-03
sample34 0.0718415218 1.469396e-02
sample35 0.0403086451 -1.632629e-02
sample36 -0.1036402827 -1.304320e-02
sample37 -0.0159385744 -3.036525e-02
sample38 0.0182198369 -4.034805e-02
sample39 0.0690363619 8.058350e-03
sample40 -0.0467312750 -2.810325e-02
sample41 0.0263674438 -5.171216e-02
sample42 0.0374578960 -1.268634e-02
sample43 0.0132336869 9.536642e-03
sample44 -0.1119154428 5.028683e-02
sample45 0.0759639367 4.587903e-02
sample46 0.0871885519 -4.670385e-02
sample47 0.0721490571 -1.288540e-02
sample48 0.0005086144 -1.290565e-02
sample49 -0.0858177028 5.173760e-02
sample50 0.0118992665 -7.276215e-02
sample51 -0.0426446855 5.306205e-02
sample52 -0.0381605826 3.086785e-02
sample53 -0.0855757630 6.730043e-02
sample54 0.0261723092 9.184260e-03
sample55 -0.0156418304 4.682404e-04
sample56 0.0307831193 2.597550e-02
sample57 -0.0157242103 4.829381e-02
sample58 -0.0031174404 1.359898e-02
sample59 -0.0373001859 5.868397e-03
sample60 -0.0142609099 5.831654e-03
sample61 -0.0122255144 2.663579e-02
sample62 0.0228002942 -8.692265e-03
sample63 -0.0833127581 5.473229e-02
sample64 -0.1166548159 4.196500e-02
sample65 0.0038808902 8.568590e-03
sample66 0.0011561811 1.766612e-02
sample67 -0.1129311062 -2.608702e-02
sample68 -0.0382526429 -3.804045e-02
sample69 -0.0476502440 4.003241e-03
sample70 -0.0110329882 -2.752719e-02
sample71 0.0096850282 -5.627056e-02
sample72 0.0487124704 -8.800131e-03
sample73 0.0773058132 8.239864e-03
sample74 -0.0102488176 2.454957e-02
sample75 -0.0286613976 -8.387293e-03
sample76 -0.0472655595 -2.129315e-02
sample77 -0.0865043074 -7.296820e-03
sample78 0.1070293698 2.818346e-02
sample79 -0.0165060681 -6.659721e-02
sample80 -0.0206765949 -8.712112e-03
sample81 -0.0050943615 -3.079175e-02
sample82 0.1153622361 -1.647054e-02
sample83 0.0367979217 -2.538114e-03
sample84 0.0199463070 -1.468961e-02
sample85 -0.0827122185 -2.709824e-04
sample86 0.0969487314 -1.699897e-02
sample87 0.0421957457 -1.965953e-02
sample88 0.0215934743 1.566050e-02
sample89 0.0751559502 2.811652e-02
sample90 -0.0057328000 -8.283795e-03
sample91 -0.1134005268 -8.603522e-02
sample92 -0.0101689918 -6.894992e-02
sample93 0.0725967502 -6.003176e-03
sample94 -0.0096878852 -4.693081e-03
sample95 -0.0223502239 -3.139636e-02
sample96 -0.0013232863 -1.963604e-02
sample97 -0.0476541710 1.183660e-02
sample98 0.0269546160 -5.978398e-03
sample99 0.0728179461 4.597884e-02
sample100 -0.0413398038 1.079347e-02
sample101 0.0087536994 -6.796076e-02
sample102 0.0032509529 3.932612e-03
sample103 0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105 0.0294940465 -7.140722e-03
sample106 0.0686472054 1.462895e-02
sample107 0.0748635927 8.401339e-03
sample108 0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110 0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112 0.0570870472 1.066018e-02
sample113 -0.0200110554 1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586 1.089843e-02
sample121 0.0542443861 3.861344e-02
sample122 0.0178575357 3.027138e-02
sample123 0.0775020581 -1.636852e-02
sample124 -0.0460701050 1.814758e-02
sample125 0.0543846585 2.075898e-03
sample126 -0.0729417144 3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128 0.0908136899 3.758801e-02
sample129 0.0552445878 -1.879062e-02
sample130 0.0007128089 -1.294308e-02
sample131 -0.0693311345 7.357082e-03
sample132 -0.0556565156 3.126995e-02
sample133 0.0375870104 -1.977240e-02
sample134 -0.1229130924 3.159495e-02
sample135 0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201 1.040683e-02
sample140 0.0452288969 -1.876279e-02
sample141 -0.0189142561 2.247042e-02
sample142 0.0297545566 1.280524e-02
sample143 0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491 5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147 0.0726083535 -1.239968e-02
sample148 -0.0284795794 3.389732e-02
sample149 0.0082261455 -6.399305e-02
sample150 -0.0765013197 2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152 0.0403422737 -2.714879e-02
sample153 0.0629117719 7.425085e-02
sample154 0.0551622927 -3.548984e-02
sample155 0.0654439133 -1.005306e-02
sample156 0.0209310714 -1.390213e-02
sample157 0.0851522597 6.577150e-03
sample158 0.0208354599 -4.663078e-03
sample159 -0.0498794349 1.913257e-02
sample160 0.0216074437 1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162 0.0963663017 5.705881e-02
sample163 -0.1009542191 7.174224e-02
sample164 0.0109881996 1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357 2.673084e-02
sample167 -0.0825048036 2.278863e-03
sample168 0.0486147429 1.793843e-02
sample169 0.0302506727 8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
[,1] [,2]
sample1 -0.0621842115 -1.364509e-02
sample2 0.0944623785 9.720892e-03
sample3 0.0406196267 -2.236338e-02
sample4 -0.0229316496 -3.932487e-04
sample5 -0.0157330047 3.231033e-03
sample6 -0.0945794025 3.120720e-02
sample7 -0.0854427118 -1.052880e-02
sample8 -0.1376625920 4.286608e-02
sample9 -0.0377115311 -1.415134e-02
sample10 0.0035244506 1.280825e-02
sample11 0.0016639987 -1.717895e-02
sample12 -0.0781403168 1.884368e-02
sample13 0.0938400516 2.838858e-02
sample14 -0.0759839772 1.810989e-02
sample15 -0.0068340837 -2.705361e-03
sample16 -0.0590150849 4.757848e-02
sample17 0.1178805097 2.040526e-02
sample18 0.0767858320 1.756604e-02
sample19 0.0157112113 -2.172867e-02
sample20 0.0485318300 -2.327033e-02
sample21 0.0185928176 4.777095e-02
sample22 -0.0191358702 -2.329775e-02
sample23 -0.0672994194 -1.535656e-03
sample24 0.1047476642 5.935707e-02
sample25 0.0329844953 -1.358036e-02
sample26 0.1154952052 1.741529e-02
sample27 0.0133849853 -3.590922e-02
sample28 0.0821554039 2.042376e-02
sample29 -0.0567643690 -2.123848e-02
sample30 0.1016073931 -1.134728e-03
sample31 -0.0880396372 3.670548e-02
sample32 0.0300363338 1.182406e-02
sample33 -0.0467252272 3.739254e-03
sample34 0.0783666394 1.203777e-02
sample35 0.0424227097 -1.118559e-02
sample36 -0.1107646166 -1.143464e-02
sample37 -0.0191667664 -2.246060e-02
sample38 0.0155968095 -2.909621e-02
sample39 0.0746847148 7.148218e-03
sample40 -0.0517028178 -2.137267e-02
sample41 0.0234979494 -3.723018e-02
sample42 0.0388797356 -8.557228e-03
sample43 0.0149555568 7.210002e-03
sample44 -0.1150305613 3.461805e-02
sample45 0.0846146236 3.486020e-02
sample46 0.0884426404 -3.246853e-02
sample47 0.0748644971 -8.083045e-03
sample48 -0.0012033198 -9.403647e-03
sample49 -0.0872662737 3.616245e-02
sample50 0.0066941314 -5.284863e-02
sample51 -0.0411777630 3.791830e-02
sample52 -0.0379355780 2.180834e-02
sample53 -0.0851639886 4.751761e-02
sample54 0.0288006248 7.184424e-03
sample55 -0.0164920835 5.919925e-05
sample56 0.0355115616 1.951043e-02
sample57 -0.0141146068 3.492409e-02
sample58 -0.0015636132 9.862883e-03
sample59 -0.0390656483 3.590929e-03
sample60 -0.0139454780 3.963030e-03
sample61 -0.0106410274 1.919705e-02
sample62 0.0236748439 -5.922677e-03
sample63 -0.0846790877 3.839102e-02
sample64 -0.1202581015 2.846469e-02
sample65 0.0050548584 6.328644e-03
sample66 0.0028013072 1.291807e-02
sample67 -0.1231623009 -2.112565e-02
sample68 -0.0437782161 -2.845072e-02
sample69 -0.0501199692 2.053469e-03
sample70 -0.0140278645 -2.027157e-02
sample71 0.0057489505 -4.085977e-02
sample72 0.0511212704 -5.522408e-03
sample73 0.0828141409 7.431582e-03
sample74 -0.0085959456 1.772951e-02
sample75 -0.0312180394 -6.636869e-03
sample76 -0.0519051781 -1.640191e-02
sample77 -0.0925924762 -6.907800e-03
sample78 0.1163971046 2.251122e-02
sample79 -0.0240906926 -4.887766e-02
sample80 -0.0221327065 -6.730703e-03
sample81 -0.0072114968 -2.254399e-02
sample82 0.1204416674 -9.907422e-03
sample83 0.0386739485 -1.171663e-03
sample84 0.0195988488 -1.033806e-02
sample85 -0.0877680171 -1.725057e-03
sample86 0.1023541048 -1.062501e-02
sample87 0.0425213089 -1.356865e-02
sample88 0.0244788514 1.180820e-02
sample89 0.0804276691 2.188588e-02
sample90 -0.0074639871 -6.140721e-03
sample91 -0.1278832404 -6.485140e-02
sample92 -0.0162199697 -5.048358e-02
sample93 0.0769344893 -3.045135e-03
sample94 -0.0104345587 -3.593172e-03
sample95 -0.0260058453 -2.330475e-02
sample96 -0.0025018700 -1.433516e-02
sample97 -0.0492358305 7.774183e-03
sample98 0.0279220220 -3.862141e-03
sample99 0.0813921923 3.487339e-02
sample100 -0.0428797405 7.112807e-03
sample101 0.0032855240 -4.940743e-02
sample102 0.0038439317 2.938008e-03
sample103 0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105 0.0314853405 -4.656633e-03
sample106 0.0726456731 1.192390e-02
sample107 0.0807342975 7.508627e-03
sample108 0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110 0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112 0.0599954082 8.820317e-03
sample113 -0.0195006577 1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131 6.937326e-03
sample121 0.0613899126 2.915307e-02
sample122 0.0218424338 2.241775e-02
sample123 0.0809008460 -1.051759e-02
sample124 -0.0472109313 1.239887e-02
sample125 0.0583180947 2.521167e-03
sample126 -0.0753941872 2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128 0.1000212216 2.908091e-02
sample129 0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877 4.091672e-03
sample132 -0.0566219024 2.179861e-02
sample133 0.0384172955 -1.372840e-02
sample134 -0.1280862736 2.077912e-02
sample135 0.0592633273 6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861 6.729304e-03
sample140 0.0468926306 -1.285498e-02
sample141 -0.0186248693 1.605439e-02
sample142 0.0328031246 9.887746e-03
sample143 0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310 3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147 0.0765025670 -7.714769e-03
sample148 -0.0276016641 2.420589e-02
sample149 0.0027545308 -4.653007e-02
sample150 -0.0792296010 1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152 0.0409796547 -1.907063e-02
sample153 0.0734301757 5.528780e-02
sample154 0.0557740684 -2.487723e-02
sample155 0.0689436560 -6.127635e-03
sample156 0.0212272938 -9.747423e-03
sample157 0.0911931194 6.355708e-03
sample158 0.0220840645 -3.016357e-03
sample159 -0.0513244242 1.304175e-02
sample160 0.0246213576 1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162 0.1078802043 4.337260e-02
sample163 -0.1017965082 5.047171e-02
sample164 0.0119430799 9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180 1.899222e-02
sample167 -0.0872832229 1.516582e-04
sample168 0.0540714512 1.397701e-02
sample169 0.0328432652 7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
[,1] [,2]
sample1 0.0133684846 2.195848e-02
sample2 0.0254157197 -1.058416e-02
sample3 -0.0049551479 -4.840017e-03
sample4 0.0310390570 -1.063929e-02
sample5 0.0046941318 -6.488426e-03
sample6 -0.0107406753 -1.026702e-02
sample7 -0.0225157631 2.624712e-04
sample8 0.0141320952 -9.505821e-03
sample9 0.0029681280 2.078210e-02
sample10 0.0131729174 -2.275042e-03
sample11 -0.0004164298 1.994019e-02
sample12 -0.0095211620 3.759883e-02
sample13 0.0091018604 -7.953956e-03
sample14 -0.0106557524 -9.181659e-03
sample15 -0.0249924121 3.262724e-02
sample16 -0.0156216400 1.375700e-02
sample17 -0.0019382446 1.073994e-03
sample18 -0.0221072481 -8.703592e-03
sample19 0.0146917619 -1.311712e-02
sample20 -0.0160353760 1.826290e-02
sample21 0.0035947899 -9.616341e-03
sample22 -0.0225060762 -2.532589e-03
sample23 0.0310000683 3.033060e-03
sample24 0.0499544372 1.809450e-02
sample25 0.0284442301 -1.932558e-02
sample26 0.0188220043 2.146985e-02
sample27 -0.0257763219 -1.999228e-03
sample28 0.0120888648 1.125834e-02
sample29 -0.0236482520 4.426726e-02
sample30 -0.0385486305 -2.055935e-02
sample31 -0.0181539336 -5.877838e-03
sample32 -0.0302630460 -2.607192e-03
sample33 -0.0319565715 -1.562628e-02
sample34 -0.0197970124 9.906813e-03
sample35 -0.0247412713 -5.434440e-03
sample36 -0.0386259060 -3.190394e-02
sample37 -0.0566199273 -4.192574e-02
sample38 -0.0142060273 2.259644e-02
sample39 0.0053589035 1.076485e-02
sample40 -0.0552546493 -3.819896e-02
sample41 -0.0013089975 9.278818e-05
sample42 0.0137252142 -1.664652e-02
sample43 -0.0151259626 -6.290953e-03
sample44 0.0617391754 -1.442883e-02
sample45 0.0231410886 1.163143e-03
sample46 -0.0148898209 -1.384176e-04
sample47 -0.0187252536 1.221690e-02
sample48 0.0432839432 1.416671e-02
sample49 0.0160818605 -3.588745e-02
sample50 0.0059333545 4.067003e-02
sample51 -0.0142914866 7.776270e-03
sample52 -0.0086339952 7.208917e-03
sample53 -0.0207386980 6.272432e-03
sample54 -0.0039856719 -1.316934e-02
sample55 -0.0056217017 5.692315e-03
sample56 0.0000123292 8.978290e-04
sample57 -0.0095805555 1.324253e-02
sample58 -0.0124160295 -7.326376e-03
sample59 -0.0400195442 -1.349736e-02
sample60 -0.0460063358 2.770091e-02
sample61 -0.0245266456 1.470710e-02
sample62 -0.0366022783 -3.437352e-03
sample63 0.0013742171 3.288796e-02
sample64 -0.0070599859 2.739588e-02
sample65 0.0041201911 1.498268e-02
sample66 0.0143173351 -1.968812e-02
sample67 -0.0467477531 -1.929938e-02
sample68 -0.0306751978 -1.436184e-02
sample69 -0.0125317217 4.130407e-03
sample70 -0.0068071487 8.080857e-03
sample71 0.0169170264 -7.027348e-03
sample72 -0.0346909749 -1.333770e-02
sample73 -0.0280506153 1.493843e-02
sample74 -0.0182611498 3.294697e-03
sample75 -0.0120563964 8.974612e-03
sample76 0.0001437236 -4.253184e-02
sample77 0.0065330299 -5.252886e-02
sample78 0.0288278141 -1.127782e-02
sample79 0.0503961481 -1.023318e-02
sample80 -0.0207693429 3.648391e-02
sample81 0.0163562768 -9.074596e-03
sample82 -0.0084317129 -1.478976e-02
sample83 -0.0474097918 -1.103126e-02
sample84 0.0177181395 -7.191197e-03
sample85 -0.0342718548 -3.082360e-02
sample86 -0.0261671791 -1.089491e-02
sample87 -0.0009486358 -2.411514e-02
sample88 0.0020528931 -2.894615e-02
sample89 -0.0189361111 -2.638639e-03
sample90 -0.0009863658 -2.390075e-02
sample91 -0.0124352695 8.153234e-02
sample92 0.0564264106 -8.909537e-03
sample93 -0.0081461774 1.570851e-02
sample94 -0.0054896581 1.547251e-02
sample95 0.0224073150 -4.374348e-04
sample96 0.0173528924 -3.050441e-03
sample97 0.0067948115 5.008237e-03
sample98 -0.0116030825 1.498764e-02
sample99 0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101 0.0124923691 3.091503e-02
sample102 0.0650835386 -1.367400e-02
sample103 -0.0042741828 7.855985e-03
sample104 0.0250591040 -4.171938e-03
sample105 0.0157516368 -3.121990e-02
sample106 0.0060593853 -5.101693e-03
sample107 -0.0098329626 1.044506e-02
sample108 0.0044269853 4.142036e-03
sample109 0.0572473486 1.517542e-02
sample110 0.0090474827 -5.119868e-03
sample111 0.0444263015 7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113 0.0241047399 6.706740e-03
sample114 0.0074558775 -4.728652e-03
sample115 0.0611851433 1.117210e-02
sample116 0.0432646951 -1.380556e-02
sample117 0.0516750066 -3.575617e-02
sample118 0.0139942100 -3.279138e-03
sample119 0.0291722987 5.587946e-02
sample120 0.0103515853 -1.690016e-03
sample121 -0.0091396331 3.552116e-02
sample122 0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124 0.0283466326 3.127845e-03
sample125 0.0016472378 -2.770692e-02
sample126 -0.0286529417 3.489336e-02
sample127 -0.0010224500 7.483214e-03
sample128 0.0209049296 2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968 4.637390e-03
sample132 0.0198526786 5.723983e-04
sample133 0.0088812957 -9.988115e-03
sample134 -0.0137484514 1.172591e-02
sample135 -0.0220314568 1.347465e-02
sample136 -0.0185173353 5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138 0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762 2.456863e-02
sample141 0.0058369763 -1.420854e-02
sample142 0.0207886071 -1.188764e-02
sample143 0.0092832598 -1.324238e-02
sample144 0.0028442140 3.627979e-03
sample145 0.0199749569 2.862202e-03
sample146 -0.0182236697 1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148 0.0065435868 -1.572917e-02
sample149 0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151 0.0245166984 -6.888241e-03
sample152 0.0107259913 3.314630e-02
sample153 0.0550963965 3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574 2.642246e-03
sample156 -0.0117803505 2.698265e-02
sample157 -0.0096167165 1.433840e-02
sample158 -0.0101754772 9.137620e-03
sample159 0.0120662931 -2.565236e-02
sample160 -0.0132238202 2.916023e-03
sample161 0.0274491966 -1.748284e-02
sample162 0.0012482909 3.152261e-02
sample163 0.0042031315 1.830701e-02
sample164 0.0174896157 -1.175915e-02
sample165 0.0097517662 -6.119019e-03
sample166 0.0190134679 -1.121582e-02
sample167 -0.0044140836 4.665585e-03
sample168 0.0049689168 -1.941822e-02
sample169 -0.0209802098 3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
[,1] [,2]
sample1 -0.0515543627 -0.0305856787
sample2 -0.0144993256 0.0236342950
sample3 -0.0371833108 -0.0140263348
sample4 0.0068945388 -0.0132539692
sample5 0.0215035333 -0.0663338101
sample6 -0.0187055152 0.0088773016
sample7 -0.0061521552 0.0064029054
sample8 -0.0210874459 0.0334652901
sample9 0.0516865043 -0.0291142799
sample10 0.0059440366 -0.0527217447
sample11 0.0393010793 -0.0200624712
sample12 -0.0420837100 0.0131331362
sample13 0.0333252565 0.0818552509
sample14 -0.0190062644 0.0160202175
sample15 -0.0030968049 -0.0189230681
sample16 -0.0004452158 0.0018880102
sample17 -0.0185848615 0.0240170131
sample18 -0.0273093598 0.0230213640
sample19 -0.0217761111 -0.0445894441
sample20 0.0245820821 0.0159812738
sample21 0.0034527644 -0.0400016054
sample22 -0.0340789054 0.0039289109
sample23 -0.0010344929 -0.0310161212
sample24 0.0289468503 0.0760962436
sample25 -0.0119098496 -0.0122798760
sample26 -0.0181001057 0.0517892852
sample27 0.0050465417 -0.0086515844
sample28 0.0057491502 0.0358830107
sample29 -0.0051104246 0.0116605117
sample30 -0.0103085904 0.0039678538
sample31 -0.0319929858 0.0090606113
sample32 -0.0036232521 -0.0328202010
sample33 -0.0534742153 0.0024751837
sample34 -0.0067495749 -0.0111000311
sample35 0.0378745721 0.0465929296
sample36 0.0647886800 0.0359987924
sample37 0.0488441236 0.0492906912
sample38 -0.0251514062 0.0197110110
sample39 -0.0085428066 -0.0105117852
sample40 0.0379324087 0.0440810741
sample41 -0.0044199152 -0.0128820644
sample42 -0.0292553573 -0.0067045265
sample43 -0.0077829155 -0.0510178219
sample44 0.0045122248 0.0479660309
sample45 -0.0074444298 -0.0051116726
sample46 -0.0088025512 0.0196186661
sample47 0.0076696301 0.0215947965
sample48 0.0290108585 -0.0175568376
sample49 -0.0141754858 0.0184717099
sample50 0.0006282201 -0.0233054373
sample51 0.0441995177 -0.0410022921
sample52 0.0715329391 -0.0399499475
sample53 -0.0095954087 -0.0029140909
sample54 0.0048933768 -0.0281884386
sample55 0.0327325487 -0.0532290012
sample56 0.0323068984 -0.0256595538
sample57 0.0806603122 -0.0286748097
sample58 -0.0064792049 -0.0006945349
sample59 0.0088958941 0.0067389649
sample60 0.0874124612 0.0431964341
sample61 0.0577604571 -0.0326112099
sample62 -0.0313318464 0.0224391756
sample63 -0.0233625220 0.0125110562
sample64 -0.0086426068 0.0148770341
sample65 0.0025256193 -0.0404466327
sample66 0.0006014071 -0.0471576264
sample67 0.0706087042 0.0516228406
sample68 0.0082301011 0.0033109509
sample69 -0.0475076743 0.0001452708
sample70 -0.0600773716 0.0089986962
sample71 -0.0096321627 -0.0050761187
sample72 -0.0031773546 -0.0166221542
sample73 -0.0113700517 -0.0191726684
sample74 -0.0014179662 -0.0608101325
sample75 0.0041911740 -0.0399981269
sample76 -0.0055326449 0.0353114263
sample77 -0.0260214459 0.0305731380
sample78 -0.0119267436 0.0632236007
sample79 0.0186017239 0.0027402910
sample80 0.0241047889 -0.0472697181
sample81 -0.0220288317 -0.0079577210
sample82 -0.0180751258 0.0639051029
sample83 -0.0256671713 -0.0125898269
sample84 0.0161392598 -0.0567222449
sample85 0.0139988188 0.0322763454
sample86 -0.0198382995 0.0389225776
sample87 0.0266270281 -0.0032979996
sample88 0.0515677078 0.0117902495
sample89 0.0014022125 -0.0140510488
sample90 -0.0375949749 0.0044004551
sample91 0.0310397965 0.0440610926
sample92 0.0270570567 0.0324380452
sample93 -0.0215009202 0.0063993941
sample94 -0.0415702912 -0.0037692077
sample95 -0.0168416047 0.0010019120
sample96 -0.0285582661 -0.0187991000
sample97 -0.0490843868 -0.0266760748
sample98 -0.0171579033 -0.0112897471
sample99 -0.0271316525 0.0232395583
sample100 -0.0301789816 0.0305498693
sample101 -0.0264371151 0.0170723968
sample102 0.0012767734 -0.0248949597
sample103 0.0055214687 -0.0030040587
sample104 0.0251346074 -0.0165212671
sample105 0.0062424215 -0.0400309901
sample106 0.0069768684 0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679 0.0023637162
sample109 -0.0014762845 0.0165583675
sample110 0.0036971063 0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112 0.0046098120 -0.0048009350
sample113 0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453 0.0135805368
sample116 0.0183575759 0.0665377581
sample117 0.0227640036 -0.0012287760
sample118 0.0015695248 0.0472617382
sample119 0.0190084932 0.0590034062
sample120 -0.0449645755 0.0072755697
sample121 0.0077307184 0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123 0.0016959300 0.0028593594
sample124 -0.0365091615 0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408 0.0019165544
sample127 -0.0494064872 0.0088209044
sample128 -0.0155454766 0.0186819802
sample129 -0.0184340400 0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422 0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133 0.0204029276 -0.0282209049
sample134 0.0175513332 0.0262883962
sample135 0.0029009925 0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323 0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139 0.0073103935 0.0308956174
sample140 0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142 0.0268670676 -0.0239229634
sample143 0.0421049126 -0.0110888235
sample144 0.0017253664 -0.0341766012
sample145 0.0681741320 -0.0073526377
sample146 -0.0239965222 0.0118396767
sample147 -0.0063453522 0.0183130585
sample148 0.0230825251 -0.0379753037
sample149 0.0223298673 0.0188909118
sample150 0.0055709108 0.0174179009
sample151 0.0039177786 -0.0233533275
sample152 0.0134325667 0.0302344591
sample153 0.0511990309 0.0730230140
sample154 0.0006698324 0.0154177486
sample155 0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599 0.0154934573
sample158 0.0201775524 -0.0332982124
sample159 -0.0086909001 0.0073496711
sample160 0.0295437331 -0.0555734536
sample161 0.0332754288 0.0033779619
sample162 0.0121954537 0.0433540412
sample163 -0.0173490933 0.0227219128
sample164 0.0143374783 -0.0453542590
sample165 0.0343612593 -0.0511194536
sample166 -0.0157536004 0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919 0.0060747155
sample169 0.0116231468 -0.0015112800
>
> ## 3.3 Plotting VAF
>
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
>
> # JIVE plotVAF
> plotVAF(jiveRes)
>
>
> #########################
> ## PART 4. Plot Results
>
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block="",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
18.114 0.413 19.142
STATegRa.Rcheck/STATegRa-Ex.timings
| name | user | system | elapsed | |
| PCA.selection | 0.164 | 0.009 | 0.180 | |
| STATegRaUsersGuide | 0.001 | 0.001 | 0.002 | |
| STATegRa_data | 0.211 | 0.017 | 0.232 | |
| STATegRa_data_TCGA_BRCA | 0.003 | 0.001 | 0.004 | |
| bioDist | 0.703 | 0.039 | 0.761 | |
| bioDistFeature | 1.396 | 0.028 | 1.454 | |
| bioDistFeaturePlot | 0.369 | 0.015 | 0.387 | |
| bioDistW | 0.370 | 0.015 | 0.397 | |
| bioDistWPlot | 0.458 | 0.015 | 0.478 | |
| bioMap | 0.004 | 0.001 | 0.007 | |
| biplotRes | 6.668 | 0.188 | 6.967 | |
| combiningMappings | 0.085 | 0.001 | 0.087 | |
| createOmicsExpressionSet | 0.135 | 0.005 | 0.146 | |
| getInitialData | 1.242 | 0.151 | 1.423 | |
| getLoadings | 2.536 | 1.671 | 4.296 | |
| getMethodInfo | 0.703 | 0.137 | 0.857 | |
| getPreprocessing | 1.181 | 0.533 | 1.737 | |
| getScores | 0.711 | 0.132 | 0.864 | |
| getVAF | 0.714 | 0.138 | 0.866 | |
| holistOmics | 0.004 | 0.001 | 0.005 | |
| modelSelection | 0.437 | 0.019 | 0.469 | |
| omicsCompAnalysis | 4.331 | 0.275 | 4.693 | |
| omicsNPC | 0.003 | 0.001 | 0.004 | |
| plotRes | 5.224 | 0.258 | 5.552 | |
| plotVAF | 4.910 | 0.263 | 5.219 | |
| selectCommonComps | 0.702 | 0.014 | 0.727 | |