David Gomez-Cabrero
| Snapshot Date: 2019-10-15 17:01:26 -0400 (Tue, 15 Oct 2019) |
| URL: https://git.bioconductor.org/packages/STATegRa |
| Branch: RELEASE_3_9 |
| Last Commit: 1457952 |
| Last Changed Date: 2019-05-02 11:53:50 -0400 (Thu, 02 May 2019) |
| Back to Multiple platform build/check report for BioC 3.9 |
|
This page was generated on 2019-10-16 12:54:22 -0400 (Wed, 16 Oct 2019).
| Package 1596/1741 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||
| STATegRa 1.20.0 David Gomez-Cabrero
| malbec2 | Linux (Ubuntu 18.04.2 LTS) / x86_64 | OK | OK | OK | | ||||||
| tokay2 | Windows Server 2012 R2 Standard / x64 | OK | OK | OK | OK | | ||||||
| celaya2 | OS X 10.11.6 El Capitan / x86_64 | OK | OK | [ OK ] | OK | |
| Package: STATegRa |
| Version: 1.20.0 |
| Command: /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/Library/Frameworks/R.framework/Versions/Current/Resources/library --no-vignettes --timings STATegRa_1.20.0.tar.gz |
| StartedAt: 2019-10-16 07:11:56 -0400 (Wed, 16 Oct 2019) |
| EndedAt: 2019-10-16 07:18:02 -0400 (Wed, 16 Oct 2019) |
| EllapsedTime: 367.0 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: STATegRa.Rcheck |
| Warnings: 0 |
##############################################################################
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###
### Running command:
###
### /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --install=check:STATegRa.install-out.txt --library=/Library/Frameworks/R.framework/Versions/Current/Resources/library --no-vignettes --timings STATegRa_1.20.0.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/Users/biocbuild/bbs-3.9-bioc/meat/STATegRa.Rcheck’
* using R version 3.6.1 (2019-07-05)
* 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 ‘1.20.0’
* 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 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 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 or elapsed time > 5s
user system elapsed
plotRes 8.044 0.809 9.070
plotVAF 6.603 0.724 7.440
omicsCompAnalysis 6.338 0.697 7.481
modelSelection 3.226 2.344 5.579
* 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: 1 NOTE
See
‘/Users/biocbuild/bbs-3.9-bioc/meat/STATegRa.Rcheck/00check.log’
for details.
STATegRa.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD INSTALL STATegRa ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/3.6/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 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 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")
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 -- Wed Oct 16 07:17:55 2019
***********************************************
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
4.392 0.613 4.958
STATegRa.Rcheck/tests/STATEgRa_Example.omicsCLUST.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 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, 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, 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 ( 0 * EPS) is small (axis 2)
5: In plot.window(...) :
relative range of values ( 0 * EPS) is small (axis 2)
6: In plot.window(...) :
relative range of values ( 0 * EPS) is small (axis 2)
7: In plot.window(...) :
relative range of values ( 0 * 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
60.999 1.922 63.690
STATegRa.Rcheck/tests/STATegRa_Example.omicsNPC.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 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
125.598 5.121 132.070
STATegRa.Rcheck/tests/STATEgRa_Example.omicsPCA.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 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$grobs[[leg]]
+ return(legend)}
>
> #########################
> ## PART 1. Load data
>
> ## Load data
> data(STATegRa_S3)
>
> ls()
[1] "Block1.PCA" "Block2.PCA" "ed.PCA" "g_legend"
>
> ## Create ExpressionSets
> # Block1 - Expression data
> B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname"))
>
> #########################
> ## PART 2. Model Selection
>
> require(grid)
Loading required package: grid
> require(gridExtra)
Loading required package: gridExtra
> require(ggplot2)
Loading required package: ggplot2
>
> ## Select the optimal components
> ms <- modelSelection(Input=list(B1,B2),Rmax=4,fac.sel="single%",varthreshold=0.03,center=TRUE,scale=TRUE,weight=TRUE)
Common components
[1] 2
Distinctive components
[[1]]
[1] 2
[[2]]
[1] 2
>
>
> #########################
> ## PART 3. Component Analysis
>
> ## 3.1 Component analysis of the three methods
> discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+ scale=TRUE,weight=TRUE)
> jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+ scale=TRUE,weight=TRUE)
> o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+ scale=TRUE,weight=TRUE)
>
> ## 3.2 Exploring scores structures
>
> # Exploring DISCO-SCA scores structure
> discoRes@scores$common ## Common scores
1 2
sample1 -0.0781574234 0.0431499519
sample2 0.1192218234 -0.0294092257
sample3 0.0531412305 0.0746839931
sample4 -0.0292975327 0.0005955792
sample5 -0.0202091790 -0.0110464317
sample6 -0.1226089099 -0.1053465625
sample7 -0.1078927939 0.0322478958
sample8 -0.1782895546 -0.1449365837
sample9 -0.0468698153 0.0455174430
sample10 0.0036030414 -0.0420112973
sample11 0.0035566490 0.0566293007
sample12 -0.1006128872 -0.0641379940
sample13 0.1174408071 -0.0907488999
sample14 -0.0981203254 -0.0617736687
sample15 -0.0085334141 0.0087016752
sample16 -0.0783148766 -0.1581292253
sample17 0.1483609866 -0.0638581991
sample18 0.0963086339 -0.0556637446
sample19 0.0217244165 0.0720083702
sample20 0.0635636538 0.0779655600
sample21 0.0201840151 -0.1566391962
sample22 -0.0218268457 0.0764107171
sample23 -0.0852042167 0.0032684452
sample24 0.1287170016 -0.1924550047
sample25 0.0430574074 0.0456561993
sample26 0.1453896660 -0.0541515044
sample27 0.0197489098 0.1185660412
sample28 0.1025336122 -0.0650687169
sample29 -0.0706018274 0.0682991318
sample30 0.1295627772 0.0066774470
sample31 -0.1147449124 -0.1232684312
sample32 0.0374311030 -0.0380173966
sample33 -0.0599515741 -0.0136863304
sample34 0.0984200901 -0.0375318599
sample35 0.0543098461 0.0378109991
sample36 -0.1403625315 0.0343762771
sample37 -0.0228941585 0.0732855174
sample38 0.0222077482 0.0962596456
sample39 0.0941738456 -0.0215200250
sample40 -0.0643800850 0.0687879577
sample41 0.0327638200 0.1232188196
sample42 0.0500431851 0.0292471027
sample43 0.0184498929 -0.0233009272
sample44 -0.1487899317 -0.1171362444
sample45 0.1050773921 -0.1123205032
sample46 0.1151195939 0.1094030673
sample47 0.0962593842 0.0288466389
sample48 -0.0004837571 0.0310272053
sample49 -0.1135208016 -0.1213975074
sample50 0.0123553321 0.1740742616
sample51 -0.0550529990 -0.1258884245
sample52 -0.0499121362 -0.0728542525
sample53 -0.1119773715 -0.1588010956
sample54 0.0360055682 -0.0228575088
sample55 -0.0210418982 -0.0006730722
sample56 0.0434169115 -0.0633125810
sample57 -0.0197824819 -0.1150711302
sample58 -0.0030439850 -0.0326096299
sample59 -0.0500252926 -0.0129412897
sample60 -0.0184278594 -0.0136077510
sample61 -0.0150299428 -0.0635021737
sample62 0.0304764171 0.0201324180
sample63 -0.1102252614 -0.1285977382
sample64 -0.1552588165 -0.0971167292
sample65 0.0058503030 -0.0207116246
sample66 0.0025605246 -0.0424322127
sample67 -0.1546634628 0.0661724606
sample68 -0.0536368996 0.0923688396
sample69 -0.0640330225 -0.0081981835
sample70 -0.0163517523 0.0663230316
sample71 0.0102537722 0.1345918792
sample72 0.0654196289 0.0196124427
sample73 0.1048556308 -0.0220934608
sample74 -0.0123799399 -0.0586112740
sample75 -0.0392077819 0.0209756700
sample76 -0.0648953330 0.0524764705
sample77 -0.1172922116 0.0201185893
sample78 0.1463067808 -0.0708476668
sample79 -0.0265211319 0.1603300917
sample80 -0.0279737012 0.0214208166
sample81 -0.0079211450 0.0738448165
sample82 0.1544236589 0.0361469049
sample83 0.0494211740 0.0050055054
sample84 0.0259038425 0.0346546942
sample85 -0.1116484195 0.0031503365
sample86 0.1306483244 0.0377218817
sample87 0.0554778221 0.0459749445
sample88 0.0301623684 -0.0382197050
sample89 0.1016866747 -0.0694031168
sample90 -0.0086819779 0.0201319887
sample91 -0.1578625084 0.2097830368
sample92 -0.0170937027 0.1655799122
sample93 0.0979806918 0.0121513004
sample94 -0.0131483970 0.0114932280
sample95 -0.0315682633 0.0758855583
sample96 -0.0024125592 0.0470132515
sample97 -0.0634545379 -0.0270333638
sample98 0.0359374765 0.0135489792
sample99 0.1009163060 -0.1124784115
sample100 -0.0551753089 -0.0246488728
sample101 0.0080119067 0.1627366234
sample102 0.0046443922 -0.0095641802
sample103 0.0472523320 0.0940393905
sample104 -0.0198159592 0.0591088111
sample105 0.0400237744 0.0160909589
sample106 0.0923808307 -0.0369018561
sample107 0.1019374045 -0.0224953337
sample108 0.0877091662 0.0128833255
sample109 -0.0864824627 0.0900933132
sample110 0.1223115477 0.0096084322
sample111 -0.0257354739 0.0936162118
sample112 0.0765286645 -0.0270345598
sample113 -0.0258803425 -0.0377500960
sample114 -0.0021138782 0.0882013292
sample115 -0.0303460492 0.0723576382
sample116 -0.0780508686 0.0685060455
sample117 -0.0536898343 0.0911901040
sample118 -0.0666651246 0.0236229260
sample119 -0.1021871586 0.2324932777
sample120 -0.0750216573 -0.0243381198
sample121 0.0756936305 -0.0942949289
sample122 0.0259627863 -0.0731991405
sample123 0.1037846349 0.0369198351
sample124 -0.0611208092 -0.0421728426
sample125 0.0738472735 -0.0066950590
sample126 -0.0972916284 -0.0762636253
sample127 -0.0824697528 0.0096636891
sample128 0.1249407406 -0.0929316300
sample129 0.0734067743 0.0434366002
sample130 0.0003502111 0.0309852366
sample131 -0.0930182747 -0.0155935025
sample132 -0.0736222939 -0.0733033452
sample133 0.0498397970 0.0462436229
sample134 -0.1644873557 -0.0720002966
sample135 0.0752297329 -0.0003814479
sample136 -0.0227145517 0.0495508332
sample137 -0.0564717158 0.0288919229
sample138 -0.0255988202 0.0610852382
sample139 -0.0621217763 -0.0235804537
sample140 0.0604152763 0.0435597074
sample141 -0.0246744010 -0.0532649928
sample142 0.0409560128 -0.0316282799
sample143 0.0077355159 0.0476895432
sample144 -0.0173240797 0.0156777381
sample145 -0.0485474926 -0.1202772702
sample146 -0.0419645376 0.0811283868
sample147 0.0977308562 0.0274844369
sample148 -0.0368256345 -0.0803980412
sample149 0.0072865845 0.1532984059
sample150 -0.1020825286 -0.0624771597
sample151 -0.0305399165 0.0289274441
sample152 0.0533594767 0.0638307570
sample153 0.0891626913 -0.1799586055
sample154 0.0727557652 0.0834162909
sample155 0.0880668743 0.0220822619
sample156 0.0276561206 0.0326626732
sample157 0.1155032219 -0.0183614817
sample158 0.0281507588 0.0104940250
sample159 -0.0663235832 -0.0443839085
sample160 0.0302643907 -0.0404263342
sample161 -0.0114715721 0.0591021807
sample162 0.1337086870 -0.1398135628
sample163 -0.1330124719 -0.1688781954
sample164 0.0150335993 -0.0028418854
sample165 -0.0076520334 0.0164127172
sample166 -0.0367794551 -0.0630665037
sample167 -0.1111988809 -0.0030057303
sample168 0.0672981513 -0.0446280116
sample169 0.0413005052 -0.0224390962
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 0.0420517926 0.0867862907
sample2 0.0820827297 -0.0410978543
sample3 -0.0155895743 -0.0195182169
sample4 0.1001336804 -0.0410787260
sample5 0.0153465212 -0.0253259796
sample6 -0.0340331110 -0.0408223162
sample7 -0.0722578387 0.0002332701
sample8 0.0457492672 -0.0370016709
sample9 0.0086251272 0.0820184918
sample10 0.0423596675 -0.0083923596
sample11 -0.0022545893 0.0787766141
sample12 -0.0322107679 0.1479824774
sample13 0.0293884838 -0.0306748914
sample14 -0.0337485830 -0.0367506758
sample15 -0.0815537877 0.1275622972
sample16 -0.0508459562 0.0540604708
sample17 -0.0062599270 0.0041023663
sample18 -0.0705642221 -0.0351047360
sample19 0.0476845313 -0.0509598259
sample20 -0.0522958921 0.0715522286
sample21 0.0119119125 -0.0376093400
sample22 -0.0724389144 -0.0095624580
sample23 0.0992532340 0.0134288214
sample24 0.1595108992 0.0728660818
sample25 0.0920695387 -0.0749757731
sample26 0.0595538366 0.0848965626
sample27 -0.0826480042 -0.0086734768
sample28 0.0384785346 0.0440966561
sample29 -0.0777667363 0.1735309070
sample30 -0.1229471079 -0.0819004793
sample31 -0.0579852257 -0.0238644607
sample32 -0.0970394823 -0.0111425796
sample33 -0.1017588475 -0.0630442000
sample34 -0.0637924127 0.0377942027
sample35 -0.0789983321 -0.0229722694
sample36 -0.1224939632 -0.1274954174
sample37 -0.1798819091 -0.1673426263
sample38 -0.0466299050 0.0888161365
sample39 0.0168687194 0.0421533625
sample40 -0.1756390466 -0.1526641223
sample41 -0.0042364697 0.0004929014
sample42 0.0447851229 -0.0651505222
sample43 -0.0482309390 -0.0253529033
sample44 0.1986708307 -0.0545779205
sample45 0.0741831385 0.0054702682
sample46 -0.0478766585 -0.0007071566
sample47 -0.0608186825 0.0481623042
sample48 0.1381490939 0.0578287013
sample49 0.0530513622 -0.1405533330
sample50 0.0173809282 0.1602389830
sample51 -0.0462567489 0.0303473909
sample52 -0.0280069298 0.0280388442
sample53 -0.0667629058 0.0237702185
sample54 -0.0121834876 -0.0521354288
sample55 -0.0182396107 0.0221328532
sample56 0.0001252256 0.0030907260
sample57 -0.0316681740 0.0530190289
sample58 -0.0393919849 -0.0297798565
sample59 -0.1278292109 -0.0546527225
sample60 -0.1486986459 0.1069157415
sample61 -0.0793125928 0.0569796889
sample62 -0.1172799736 -0.0149197767
sample63 0.0028721325 0.1300519618
sample64 -0.0237369025 0.1073287707
sample65 0.0126534364 0.0589808327
sample66 0.0468192413 -0.0771073038
sample67 -0.1494263219 -0.0769859314
sample68 -0.0977957257 -0.0577350339
sample69 -0.0403087113 0.0156042338
sample70 -0.0221527119 0.0315441189
sample71 0.0546441179 -0.0272396551
sample72 -0.1107486930 -0.0537318696
sample73 -0.0906761562 0.0579967105
sample74 -0.0586557999 0.0121421920
sample75 -0.0390492034 0.0349283068
sample76 0.0022962114 -0.1676558724
sample77 0.0232095980 -0.2067302902
sample78 0.0929751458 -0.0434940129
sample79 0.1619504131 -0.0378114936
sample80 -0.0680363837 0.1424663919
sample81 0.0530787781 -0.0358351045
sample82 -0.0266820193 -0.0577444882
sample83 -0.1517234735 -0.0448553444
sample84 0.0570968685 -0.0273813527
sample85 -0.1086290798 -0.1228118690
sample86 -0.0833857955 -0.0442914430
sample87 -0.0022016933 -0.0943906770
sample88 0.0078221687 -0.1140506634
sample89 -0.0611060487 -0.0094584901
sample90 -0.0022927204 -0.0936253948
sample91 -0.0433579141 0.3205983363
sample92 0.1815342966 -0.0334681091
sample93 -0.0267629329 0.0614429205
sample94 -0.0181876306 0.0605090530
sample95 0.0720379603 -0.0013045958
sample96 0.0559717463 -0.0118791672
sample97 0.0217410417 0.0195413971
sample98 -0.0379176036 0.0588357348
sample99 0.0792422145 -0.0151274430
sample100 -0.0222117468 -0.0023321340
sample101 0.0387237660 0.1224226246
sample102 0.2094613466 -0.0516443854
sample103 -0.0138476369 0.0301052181
sample104 0.0807989521 -0.0162719299
sample105 0.0520493542 -0.1229665447
sample106 0.0192611407 -0.0185238350
sample107 -0.0319017360 0.0405123441
sample108 0.0140692043 0.0163421322
sample109 0.1831934710 0.0613006635
sample110 0.0292791092 -0.0199849231
sample111 0.1423257006 0.0327339642
sample112 -0.0426334091 -0.0029083233
sample113 0.0771902803 0.0268733136
sample114 0.0241646106 -0.0184080422
sample115 0.1959019334 0.0460129655
sample116 0.1394478426 -0.0530806505
sample117 0.1672365061 -0.1386537232
sample118 0.0448345057 -0.0117622153
sample119 0.0910398490 0.2217433217
sample120 0.0331391403 -0.0057274727
sample121 -0.0307578670 0.1392506582
sample122 0.0839777612 -0.0291995024
sample123 -0.0239648801 -0.0642163527
sample124 0.0909148975 0.0130418908
sample125 0.0065350202 -0.1092631864
sample126 -0.0935314374 0.1368284469
sample127 -0.0035386789 0.0292755670
sample128 0.0660291916 0.1018565805
sample129 -0.0693636492 -0.0695421268
sample130 -0.0008491896 -0.0669704276
sample131 -0.0431024801 0.0174065092
sample132 0.0637037010 0.0029374238
sample133 0.0289496744 -0.0390818930
sample134 -0.0446206942 0.0456334653
sample135 -0.0712336572 0.0521635369
sample136 -0.0596267975 0.0197299742
sample137 -0.0793150454 -0.0380627804
sample138 0.0973551099 -0.0454218732
sample139 -0.0539907133 -0.1534327086
sample140 -0.0850824162 0.0955815090
sample141 0.0192679193 -0.0554450266
sample142 0.0672259989 -0.0461321352
sample143 0.0303731965 -0.0519260341
sample144 0.0089365504 0.0145814883
sample145 0.0638763127 0.0122257855
sample146 -0.0585852089 0.0063083812
sample147 -0.0894132441 -0.1124615139
sample148 0.0216362379 -0.0615967379
sample149 0.0515427405 -0.0839903553
sample150 -0.0568286833 -0.0124468690
sample151 0.0789533630 -0.0261831600
sample152 0.0330757238 0.1306443498
sample153 0.1751922568 0.1497730830
sample154 -0.0421420163 -0.0037009820
sample155 -0.0680176120 0.0095711655
sample156 -0.0388908661 0.1057563229
sample157 -0.0314769734 0.0561367583
sample158 -0.0329619907 0.0353947526
sample159 0.0398413735 -0.1007374072
sample160 -0.0424940808 0.0108496355
sample161 0.0888373350 -0.0679700592
sample162 0.0027469744 0.1237843642
sample163 0.0126097264 0.0725434013
sample164 0.0566779249 -0.0458324517
sample165 0.0315336769 -0.0236362511
sample166 0.0612054880 -0.0425233486
sample167 -0.0142729896 0.0179308350
sample168 0.0169500995 -0.0769618061
sample169 -0.0675081439 0.0131505686
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 0.0012329587 -1.635717e-01
sample2 0.0724349986 -6.021209e-03
sample3 0.0188460461 -1.080036e-01
sample4 -0.0390145362 3.114516e-04
sample5 -0.1774811663 -2.996383e-02
sample6 0.0451444391 -3.455856e-02
sample7 0.0226466323 -7.020211e-03
sample8 0.1033680126 -9.856717e-03
sample9 -0.1350011698 8.979097e-02
sample10 -0.1259887320 -5.097849e-02
sample11 -0.0979788319 7.086532e-02
sample12 0.0863019066 -8.620316e-02
sample13 0.1381401102 1.828008e-01
sample14 0.0615073852 -2.642802e-02
sample15 -0.0381598895 -3.101667e-02
sample16 0.0048776693 1.271886e-03
sample17 0.0788480918 -1.547551e-02
sample18 0.0884188776 -3.795487e-02
sample19 -0.0703044454 -1.084004e-01
sample20 0.0025585628 7.975869e-02
sample21 -0.0941601760 -4.126733e-02
sample22 0.0550273489 -7.806750e-02
sample23 -0.0679495400 -4.102002e-02
sample24 0.1310962615 1.649311e-01
sample25 -0.0113585343 -4.426861e-02
sample26 0.1402945843 2.016548e-02
sample27 -0.0261560998 1.588362e-03
sample28 0.0724198670 5.850597e-02
sample29 0.0330058665 2.060759e-03
sample30 0.0228752660 -2.015434e-02
sample31 0.0635067911 -6.670331e-02
sample32 -0.0685099604 -4.955274e-02
sample33 0.0777765258 -1.272079e-01
sample34 -0.0157842390 -3.024314e-02
sample35 0.0529632893 1.500971e-01
sample36 -0.0070900574 2.025307e-01
sample37 0.0442420845 1.802088e-01
sample38 0.0781511373 -3.676426e-02
sample39 -0.0120331895 -3.388839e-02
sample40 0.0473292303 1.471560e-01
sample41 -0.0228189351 -2.673560e-02
sample42 0.0245360200 -7.960866e-02
sample43 -0.1036362804 -8.229577e-02
sample44 0.1012228598 7.049463e-02
sample45 -0.0013732168 -2.450902e-02
sample46 0.0558510115 2.947321e-03
sample47 0.0380481257 4.554170e-02
sample48 -0.0784342170 4.888985e-02
sample49 0.0605163857 -1.162349e-02
sample50 -0.0530079207 -2.737939e-02
sample51 -0.1514646540 5.678349e-02
sample52 -0.1860935210 1.246717e-01
sample53 0.0064177060 -2.700990e-02
sample54 -0.0697038350 -2.308388e-02
sample55 -0.1633577014 1.366442e-02
sample56 -0.1011485120 4.682209e-02
sample57 -0.1730374196 1.609604e-01
sample58 0.0071384719 -1.666955e-02
sample59 0.0030461796 3.005281e-02
sample60 -0.0215834939 2.665877e-01
sample61 -0.1510583575 1.002385e-01
sample62 0.0925534045 -4.845847e-02
sample63 0.0596311704 -4.137017e-02
sample64 0.0449225764 -2.600559e-03
sample65 -0.0939383798 -4.406907e-02
sample66 -0.1063400830 -5.709989e-02
sample67 0.0201590252 2.361727e-01
sample68 -0.0037203064 2.418381e-02
sample69 0.0645161198 -1.155622e-01
sample70 0.1013440023 -1.351789e-01
sample71 0.0016467893 -2.976845e-02
sample72 -0.0328892926 -2.835862e-02
sample73 -0.0275079999 -5.148188e-02
sample74 -0.1341719694 -7.895279e-02
sample75 -0.0951575633 -3.943186e-02
sample76 0.0864722005 3.034989e-02
sample77 0.1035749557 -2.545354e-02
sample78 0.1575644037 4.939601e-02
sample79 -0.0189137112 4.874678e-02
sample80 -0.1384140522 4.261874e-05
sample81 0.0118846434 -6.357933e-02
sample82 0.1675308225 3.533909e-02
sample83 0.0065673514 -7.812616e-02
sample84 -0.1486891651 -3.109055e-02
sample85 0.0532724542 7.417879e-02
sample86 0.1138477428 -1.919963e-05
sample87 -0.0432863935 6.080471e-02
sample88 -0.0433450346 1.402491e-01
sample89 -0.0331205769 -1.395399e-02
sample90 0.0607412802 -8.610415e-02
sample91 0.0566272819 1.303746e-01
sample92 0.0359582476 1.061604e-01
sample93 0.0433646374 -4.443636e-02
sample94 0.0477291286 -1.059574e-01
sample95 0.0249595739 -3.980526e-02
sample96 -0.0035219061 -9.293928e-02
sample97 0.0066048670 -1.527230e-01
sample98 -0.0020366797 -5.579552e-02
sample99 0.0886615952 -3.728217e-02
sample100 0.1091259129 -3.560420e-02
sample101 0.0739726522 -4.318005e-02
sample102 -0.0574461345 -2.783905e-02
sample103 -0.0142730957 9.705514e-03
sample104 -0.0710395233 4.068352e-02
sample105 -0.0980831392 -3.452950e-02
sample106 0.0254259287 3.628987e-02
sample107 0.0160653431 -9.173394e-02
sample108 0.0200987642 -2.379692e-02
sample109 0.0389780553 1.692361e-02
sample110 0.0326304836 2.988111e-02
sample111 -0.0676937655 -6.038211e-02
sample112 -0.0167883411 5.336939e-03
sample113 -0.0969217113 -2.757598e-02
sample114 0.0026398351 -9.209160e-02
sample115 0.0308047195 1.603827e-02
sample116 0.1240307139 1.273000e-01
sample117 -0.0334729151 5.392713e-02
sample118 0.1037152915 6.252431e-02
sample119 0.1064176742 1.196202e-01
sample120 0.0771355017 -1.004932e-01
sample121 0.0129350732 3.181979e-02
sample122 -0.0847492421 -5.568319e-02
sample123 0.0041336822 7.693161e-03
sample124 0.0583457872 -8.396384e-02
sample125 -0.0634844625 -5.232539e-02
sample126 0.0662580947 -1.091733e-01
sample127 0.0865024577 -1.094176e-01
sample128 0.0627817317 -1.470956e-02
sample129 0.0336276522 -4.007863e-02
sample130 0.0293517740 -8.046119e-02
sample131 0.0469197694 -2.209768e-03
sample132 0.0241740543 -1.248598e-01
sample133 -0.0907303205 1.466700e-02
sample134 0.0350842104 7.539662e-02
sample135 -0.0001333344 9.185355e-03
sample136 0.0335876105 -9.860279e-02
sample137 0.0640148970 -7.554475e-02
sample138 -0.0060964897 -1.742761e-02
sample139 0.0592084518 5.614967e-02
sample140 -0.0427985837 -1.099556e-02
sample141 -0.0618796460 -9.301034e-02
sample142 -0.0898554531 3.573422e-02
sample143 -0.0817389183 8.880524e-02
sample144 -0.0787754794 -3.821392e-02
sample145 -0.1085821650 1.569477e-01
sample146 0.0589558022 -4.373366e-02
sample147 0.0495330539 7.277158e-03
sample148 -0.1161592850 9.079130e-03
sample149 0.0121579526 7.788369e-02
sample150 0.0314512567 3.520212e-02
sample151 -0.0575382230 -1.945350e-02
sample152 0.0494542126 7.025536e-02
sample153 0.0941332555 2.153299e-01
sample154 0.0335932098 2.078723e-02
sample155 -0.0690457589 -2.780413e-02
sample156 -0.1039901593 -6.292527e-02
sample157 0.0408645800 8.065517e-03
sample158 -0.1018105272 7.816863e-03
sample159 0.0281730479 -1.207203e-02
sample160 -0.1643052995 2.978114e-03
sample161 -0.0374329265 8.524611e-02
sample162 0.0804535266 8.349762e-02
sample163 0.0743227863 -1.406218e-02
sample164 -0.1208806069 -2.139457e-02
sample165 -0.1608115925 2.025193e-02
sample166 0.0425944544 -2.660709e-02
sample167 0.0226849477 -4.464283e-02
sample168 0.0180735544 -7.465885e-04
sample169 -0.0190778952 2.645401e-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
21.044 1.639 23.306
STATegRa.Rcheck/STATegRa-Ex.timings
| name | user | system | elapsed | |
| STATegRaUsersGuide | 0.001 | 0.000 | 0.001 | |
| STATegRa_data | 0.250 | 0.039 | 0.290 | |
| STATegRa_data_TCGA_BRCA | 0.003 | 0.001 | 0.003 | |
| bioDist | 0.777 | 0.060 | 0.837 | |
| bioDistFeature | 0.649 | 0.044 | 0.693 | |
| bioDistFeaturePlot | 0.544 | 0.084 | 0.630 | |
| bioDistW | 0.542 | 0.085 | 0.628 | |
| bioDistWPlot | 0.551 | 0.070 | 0.621 | |
| bioMap | 0.005 | 0.003 | 0.009 | |
| combiningMappings | 0.026 | 0.003 | 0.029 | |
| createOmicsExpressionSet | 0.202 | 0.013 | 0.215 | |
| getInitialData | 0.901 | 0.453 | 1.354 | |
| getLoadings | 1.150 | 0.488 | 1.639 | |
| getMethodInfo | 0.810 | 0.319 | 1.129 | |
| getPreprocessing | 1.931 | 1.189 | 3.158 | |
| getScores | 0.944 | 0.333 | 1.279 | |
| getVAF | 0.787 | 0.292 | 1.078 | |
| holistOmics | 0.002 | 0.001 | 0.004 | |
| modelSelection | 3.226 | 2.344 | 5.579 | |
| omicsCompAnalysis | 6.338 | 0.697 | 7.481 | |
| omicsNPC | 0.004 | 0.003 | 0.007 | |
| plotRes | 8.044 | 0.809 | 9.070 | |
| plotVAF | 6.603 | 0.724 | 7.440 | |