David Gomez-Cabrero
| Snapshot Date: 2021-05-05 14:51:38 -0400 (Wed, 05 May 2021) |
| URL: https://git.bioconductor.org/packages/STATegRa |
| Branch: RELEASE_3_12 |
| Last Commit: 064ea6c |
| Last Changed Date: 2020-10-27 11:00:09 -0400 (Tue, 27 Oct 2020) |
| Back to Multiple platform build/check report for BioC 3.12 |
|
This page was generated on 2021-05-06 12:33:48 -0400 (Thu, 06 May 2021).
|
To the developers/maintainers of the STATegRa package: Please make sure to use the following settings in order to reproduce any error or warning you see on this page. |
| Package 1800/1974 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| STATegRa 1.26.0 (landing page) David Gomez-Cabrero
| malbec1 | Linux (Ubuntu 18.04.5 LTS) / x86_64 | OK | OK | OK | | ||||||||
| tokay1 | Windows Server 2012 R2 Standard / x64 | OK | OK | OK | OK | | ||||||||
| merida1 | macOS 10.14.6 Mojave / x86_64 | OK | OK | OK | OK | | ||||||||
| Package: STATegRa |
| Version: 1.26.0 |
| Command: C:\Users\biocbuild\bbs-3.12-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=C:\Users\biocbuild\bbs-3.12-bioc\R\library --no-vignettes --timings STATegRa_1.26.0.tar.gz |
| StartedAt: 2021-05-06 07:18:36 -0400 (Thu, 06 May 2021) |
| EndedAt: 2021-05-06 07:25:26 -0400 (Thu, 06 May 2021) |
| EllapsedTime: 410.0 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: STATegRa.Rcheck |
| Warnings: 0 |
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###
### Running command:
###
### C:\Users\biocbuild\bbs-3.12-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=C:\Users\biocbuild\bbs-3.12-bioc\R\library --no-vignettes --timings STATegRa_1.26.0.tar.gz
###
##############################################################################
##############################################################################
* using log directory 'C:/Users/biocbuild/bbs-3.12-bioc/meat/STATegRa.Rcheck'
* using R version 4.0.5 (2021-03-31)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'STATegRa/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'STATegRa' version '1.26.0'
* 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 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
* loading checks for arch 'i386'
** 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
* loading checks for arch 'x64'
** 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 ...
** running examples for arch 'i386' ... OK
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
plotRes 5.19 0.07 5.25
** running examples for arch 'x64' ... OK
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
plotRes 6.93 0.06 6.98
omicsCompAnalysis 5.34 0.20 5.55
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
** running tests for arch 'i386' ...
Running 'STATEgRa_Example.omicsCLUST.R'
Running 'STATEgRa_Example.omicsPCA.R'
Running 'STATegRa_Example.omicsNPC.R'
Running 'runTests.R'
OK
** running tests for arch 'x64' ...
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
'C:/Users/biocbuild/bbs-3.12-bioc/meat/STATegRa.Rcheck/00check.log'
for details.
STATegRa.Rcheck/00install.out
##############################################################################
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###
### Running command:
###
### C:\cygwin\bin\curl.exe -O http://172.29.0.3/BBS/3.12/bioc/src/contrib/STATegRa_1.26.0.tar.gz && rm -rf STATegRa.buildbin-libdir && mkdir STATegRa.buildbin-libdir && C:\Users\biocbuild\bbs-3.12-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=STATegRa.buildbin-libdir STATegRa_1.26.0.tar.gz && C:\Users\biocbuild\bbs-3.12-bioc\R\bin\R.exe CMD INSTALL STATegRa_1.26.0.zip && rm STATegRa_1.26.0.tar.gz STATegRa_1.26.0.zip
###
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% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
100 3179k 100 3179k 0 0 14.6M 0 --:--:-- --:--:-- --:--:-- 14.7M
install for i386
* installing *source* package 'STATegRa' ...
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
converting help for package 'STATegRa'
finding HTML links ... done
STATegRa-defunct html
STATegRa html
STATegRaUsersGuide html
STATegRa_data html
STATegRa_data_TCGA_BRCA html
bioDist html
bioDistFeature html
bioDistFeaturePlot html
bioDistW html
bioDistWPlot html
bioDistclass html
bioMap html
caClass-class html
combiningMappings html
createOmicsExpressionSet html
getInitialData html
getLoadings html
getMethodInfo html
getPreprocessing html
getScores html
getVAF html
holistOmics html
modelSelection html
finding level-2 HTML links ... done
omicsCompAnalysis html
omicsNPC html
plotRes html
plotVAF html
** 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
install for x64
* installing *source* package 'STATegRa' ...
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'STATegRa' as STATegRa_1.26.0.zip
* DONE (STATegRa)
* installing to library 'C:/Users/biocbuild/bbs-3.12-bioc/R/library'
package 'STATegRa' successfully unpacked and MD5 sums checked
|
STATegRa.Rcheck/tests_i386/runTests.Rout
R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-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 -- Thu May 06 07:23:11 2021
***********************************************
Number of test functions: 4
Number of errors: 0
Number of failures: 0
1 Test Suite :
STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures
Number of test functions: 4
Number of errors: 0
Number of failures: 0
Warning messages:
1: In rownames(pData) == colnames(exprs) :
longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
>
> proc.time()
user system elapsed
3.42 0.26 3.78
|
STATegRa.Rcheck/tests_x64/runTests.Rout
R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (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 -- Thu May 06 07:25:19 2021
***********************************************
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
4.32 0.34 4.75
|
|
STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsCLUST.Rout
R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-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.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
38.43 0.95 39.46
|
STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsCLUST.Rout
R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (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.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
25.21 0.82 26.17
|
|
STATegRa.Rcheck/tests_i386/STATegRa_Example.omicsNPC.Rout
R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-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
73.48 0.34 73.90
|
STATegRa.Rcheck/tests_x64/STATegRa_Example.omicsNPC.Rout
R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (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
81.42 0.29 81.79
|
|
STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsPCA.Rout
R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-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 0.0781574276 -0.0431500955
sample2 -0.1192218337 0.0294090217
sample3 -0.0531412202 -0.0746839845
sample4 0.0292975233 -0.0005958151
sample5 0.0202091792 0.0110463906
sample6 0.1226089070 0.1053466474
sample7 0.1078928033 -0.0322477162
sample8 0.1782895399 0.1449364861
sample9 0.0468698152 -0.0455174448
sample10 -0.0036030457 0.0420111829
sample11 -0.0035566469 -0.0566292842
sample12 0.1006128880 0.0641380453
sample13 -0.1174408233 0.0907488692
sample14 0.0981203251 0.0617737530
sample15 0.0085334238 -0.0087014961
sample16 0.0783148707 0.1581293491
sample17 -0.1483609904 0.0638581999
sample18 -0.0963086300 0.0556639013
sample19 -0.0217244120 -0.0720085095
sample20 -0.0635636463 -0.0779654242
sample21 -0.0201840248 0.1566391588
sample22 0.0218268603 -0.0764105598
sample23 0.0852042090 -0.0032686898
sample24 -0.1287170371 0.1924546472
sample25 -0.0430574116 -0.0456564290
sample26 -0.1453896774 0.0541513495
sample27 -0.0197488935 -0.1185658424
sample28 -0.1025336230 0.0650686284
sample29 0.0706018390 -0.0682989529
sample30 -0.1295627640 -0.0066771583
sample31 0.1147449116 0.1232685639
sample32 -0.0374310938 0.0380176163
sample33 0.0599515871 0.0136865528
sample34 -0.0984200853 0.0375319963
sample35 -0.0543098413 -0.0378107733
sample36 0.1403625393 -0.0343759121
sample37 0.0228941750 -0.0732850246
sample38 -0.0222077365 -0.0962595509
sample39 -0.0941738475 0.0215199662
sample40 0.0643801018 -0.0687874816
sample41 -0.0327638101 -0.1232188183
sample42 -0.0500431849 -0.0292472286
sample43 -0.0184498861 0.0233010235
sample44 0.1487899011 0.1171357979
sample45 -0.1050774062 0.1123203109
sample46 -0.1151195823 -0.1094029590
sample47 -0.0962593782 -0.0288464915
sample48 0.0004837444 -0.0310275304
sample49 0.1135207884 0.1213973955
sample50 -0.0123553210 -0.1740743238
sample51 0.0550529941 0.1258885513
sample52 0.0499121310 0.0728543520
sample53 0.1119773681 0.1588012553
sample54 -0.0360055671 0.0228575340
sample55 0.0210419006 0.0006731191
sample56 -0.0434169167 0.0633125891
sample57 0.0197824728 0.1150712436
sample58 0.0030439872 0.0326097232
sample59 0.0500253034 0.0129416127
sample60 0.0184278639 0.0136081682
sample61 0.0150299439 0.0635023861
sample62 -0.0304764032 -0.0201321485
sample63 0.1102252531 0.1285977182
sample64 0.1552588118 0.0971167878
sample65 -0.0058503036 0.0207115781
sample66 -0.0025605291 0.0424320917
sample67 0.1546634738 -0.0661720254
sample68 0.0536369148 -0.0923685899
sample69 0.0640330294 0.0081982550
sample70 0.0163517629 -0.0663230132
sample71 -0.0102537674 -0.1345920168
sample72 -0.0654196153 -0.0196121832
sample73 -0.1048556215 0.0220936547
sample74 0.0123799453 0.0586113954
sample75 0.0392077891 -0.0209755859
sample76 0.0648953350 -0.0524764508
sample77 0.1172922112 -0.0201186272
sample78 -0.1463067975 0.0708474487
sample79 0.0265211254 -0.1603304648
sample80 0.0279737101 -0.0214206622
sample81 0.0079211470 -0.0738449568
sample82 -0.1544236562 -0.0361468377
sample83 -0.0494211560 -0.0050051593
sample84 -0.0259038436 -0.0346548391
sample85 0.1116484277 -0.0031500392
sample86 -0.1306483144 -0.0377216862
sample87 -0.0554778204 -0.0459749204
sample88 -0.0301623760 0.0382197281
sample89 -0.1016866726 0.0694032543
sample90 0.0086819822 -0.0201319966
sample91 0.1578625213 -0.2097829145
sample92 0.0170936923 -0.1655803192
sample93 -0.0979806873 -0.0121512585
sample94 0.0131484028 -0.0114932145
sample95 0.0315682626 -0.0758857395
sample96 0.0024125601 -0.0470134083
sample97 0.0634545391 0.0270332762
sample98 -0.0359374700 -0.0135489089
sample99 -0.1009163208 0.1124782058
sample100 0.0551753098 0.0246489214
sample101 -0.0080118988 -0.1627367376
sample102 -0.0046444120 0.0095636726
sample103 -0.0472523245 -0.0940393603
sample104 0.0198159544 -0.0591089929
sample105 -0.0400237763 -0.0160910859
sample106 -0.0923808365 0.0369018140
sample107 -0.1019373999 0.0224953776
sample108 -0.0877091661 -0.0128833725
sample109 0.0864824498 -0.0900937491
sample110 -0.1223115511 -0.0096085020
sample111 0.0257354688 -0.0936165704
sample112 -0.0765286622 0.0270346589
sample113 0.0258803340 0.0377499028
sample114 0.0021138850 -0.0882014087
sample115 0.0303460340 -0.0723581076
sample116 0.0780508544 -0.0685063401
sample117 0.0536898227 -0.0911904787
sample118 0.0666651189 -0.0236230129
sample119 0.1021871600 -0.2324934777
sample120 0.0750216552 0.0243380206
sample121 -0.0756936353 0.0942949948
sample122 -0.0259627969 0.0731989249
sample123 -0.1037846302 -0.0369197774
sample124 0.0611207998 0.0421726059
sample125 -0.0738472721 0.0066950338
sample126 0.0972916354 0.0762638192
sample127 0.0824697568 -0.0096637045
sample128 -0.1249407536 0.0929314508
sample129 -0.0734067633 -0.0434364426
sample130 -0.0003502063 -0.0309852493
sample131 0.0930182776 0.0155936107
sample132 0.0736222868 0.0733031655
sample133 -0.0498397965 -0.0462436893
sample134 0.1644873523 0.0720004304
sample135 -0.0752297264 0.0003816125
sample136 0.0227145640 -0.0495507142
sample137 0.0564717277 -0.0288917438
sample138 0.0255988156 -0.0610854717
sample139 0.0621217780 0.0235806141
sample140 -0.0604152645 -0.0435595169
sample141 0.0246743992 0.0532649293
sample142 -0.0409560220 0.0316281277
sample143 -0.0077355180 -0.0476895907
sample144 0.0173240817 -0.0156777692
sample145 0.0485474735 0.1202771577
sample146 0.0419645499 -0.0811282542
sample147 -0.0977308460 -0.0274842175
sample148 0.0368256276 0.0803979987
sample149 -0.0072865817 -0.1532985045
sample150 0.1020825286 0.0624773133
sample151 0.0305399119 -0.0289276349
sample152 -0.0533594787 -0.0638308331
sample153 -0.0891627291 0.1799582194
sample154 -0.0727557564 -0.0834161888
sample155 -0.0880668647 -0.0220821118
sample156 -0.0276561119 -0.0326626055
sample157 -0.1155032208 0.0183615473
sample158 -0.0281507544 -0.0104939484
sample159 0.0663235767 0.0443838221
sample160 -0.0302643882 0.0404264343
sample161 0.0114715649 -0.0591023673
sample162 -0.1337087004 0.1398135582
sample163 0.1330124590 0.1688781648
sample164 -0.0150336033 0.0028417462
sample165 0.0076520319 -0.0164127858
sample166 0.0367794455 0.0630663555
sample167 0.1111988834 0.0030057597
sample168 -0.0672981559 0.0446279721
sample169 -0.0413005009 0.0224392615
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 -0.0420516330 0.0867863000
sample2 -0.0820827968 -0.0410978281
sample3 0.0155897790 -0.0195182276
sample4 -0.1001336957 -0.0410786959
sample5 -0.0153465576 -0.0253259732
sample6 0.0340328427 -0.0408223180
sample7 0.0722579084 0.0002332461
sample8 -0.0457496385 -0.0370016459
sample9 -0.0086250235 0.0820184915
sample10 -0.0423597642 -0.0083923431
sample11 0.0022547295 0.0787766093
sample12 0.0322106709 0.1479824727
sample13 -0.0293887362 -0.0306748764
sample14 0.0337484221 -0.0367506811
sample15 0.0815538515 0.1275622724
sample16 0.0508455838 0.0540604682
sample17 0.0062597957 0.0041023689
sample18 0.0705640992 -0.0351047533
sample19 -0.0476843490 -0.0509598170
sample20 0.0522960893 0.0715522067
sample21 -0.0119122932 -0.0376093236
sample22 0.0724391134 -0.0095624859
sample23 -0.0992532238 0.0134288513
sample24 -0.1595113608 0.0728661438
sample25 -0.0920694372 -0.0749757493
sample26 -0.0595539264 0.0848965837
sample27 0.0826482877 -0.0086735107
sample28 -0.0384786785 0.0440966722
sample29 0.0777669465 0.1735308783
sample30 0.1229471177 -0.0819005169
sample31 0.0579849282 -0.0238644684
sample32 0.0970393997 -0.0111426054
sample33 0.1017588219 -0.0630442295
sample34 0.0637923495 0.0377941865
sample35 0.0789983894 -0.0229722964
sample36 0.1224939475 -0.1274954565
sample37 0.1798819939 -0.1673426860
sample38 0.0466301771 0.0888161146
sample39 -0.0168687421 0.0421533692
sample40 0.1756391286 -0.1526641803
sample41 0.0042367750 0.0004928904
sample42 -0.0447850478 -0.0651505113
sample43 0.0482308918 -0.0253529154
sample44 -0.1986711558 -0.0545778520
sample45 -0.0741833915 0.0054702990
sample46 0.0478769346 -0.0007071800
sample47 0.0608187669 0.0481622834
sample48 -0.1381490163 0.0578287405
sample49 -0.0530517026 -0.1405533076
sample50 -0.0173804557 0.1602389747
sample51 0.0462564304 0.0303473878
sample52 0.0280067219 0.0280388425
sample53 0.0667625259 0.0237702113
sample54 0.0121834230 -0.0521354304
sample55 0.0182396041 0.0221328486
sample56 -0.0001253876 0.0030907314
sample57 0.0316678671 0.0530190293
sample58 0.0393919013 -0.0297798657
sample59 0.1278291517 -0.0546527596
sample60 0.1486985815 0.1069156982
sample61 0.0793124265 0.0569796709
sample62 0.1172800354 -0.0149198138
sample63 -0.0028724024 0.1300519728
sample64 0.0237366878 0.1073287713
sample65 -0.0126534617 0.0589808386
sample66 -0.0468193574 -0.0771072860
sample67 0.1494263899 -0.0769859811
sample68 0.0977959200 -0.0577350703
sample69 0.0403087189 0.0156042223
sample70 0.0221529143 0.0315441066
sample71 -0.0546437941 -0.0272396495
sample72 0.1107487358 -0.0537319042
sample73 0.0906761413 0.0579966851
sample74 0.0586556739 0.0121421797
sample75 0.0390492657 0.0349282940
sample76 -0.0022961450 -0.1676558761
sample77 -0.0232096129 -0.2067302850
sample78 -0.0929753201 -0.0434939805
sample79 -0.1619500528 -0.0378114578
sample80 0.0680364696 0.1424663706
sample81 -0.0530785967 -0.0358350945
sample82 0.0266821028 -0.0577445002
sample83 0.1517234952 -0.0448553903
sample84 -0.0570967895 -0.0273813377
sample85 0.1086290249 -0.1228119018
sample86 0.0833858904 -0.0442914717
sample87 0.0022017657 -0.0943906812
sample88 -0.0078223256 -0.1140506579
sample89 0.0611058906 -0.0094585029
sample90 0.0022927621 -0.0936253973
sample91 0.0433584720 0.3205983068
sample92 -0.1815339324 -0.0334680681
sample93 0.0267630020 0.0614429111
sample94 0.0181877011 0.0605090465
sample95 -0.0720377709 -0.0013045803
sample96 -0.0559716157 -0.0118791542
sample97 -0.0217410721 0.0195414059
sample98 0.0379176700 0.0588357223
sample99 -0.0792424679 -0.0151274110
sample100 0.0222116938 -0.0023321390
sample101 -0.0387233239 0.1224226229
sample102 -0.2094613805 -0.0516443218
sample103 0.0138478742 0.0301052065
sample104 -0.0807988287 -0.0162719100
sample105 -0.0520493443 -0.1229665300
sample106 -0.0192612333 -0.0185238267
sample107 0.0319017255 0.0405123361
sample108 -0.0140691535 0.0163421351
sample109 -0.1831932465 0.0613007111
sample110 -0.0292790856 -0.0199849156
sample111 -0.1423254567 0.0327339997
sample112 0.0426333487 -0.0029083340
sample113 -0.0771903632 0.0268733401
sample114 -0.0241643828 -0.0184080419
sample115 -0.1959017508 0.0460130182
sample116 -0.1394477253 -0.0530806147
sample117 -0.1672363468 -0.1386536802
sample118 -0.0448344690 -0.0117622042
sample119 -0.0910392560 0.2217433302
sample120 -0.0331391819 -0.0057274610
sample121 0.0307576822 0.1392506562
sample122 -0.0839779343 -0.0291994712
sample123 0.0239649584 -0.0642163630
sample124 -0.0909149797 0.0130419212
sample125 -0.0065350522 -0.1092631837
sample126 0.0935313126 0.1368284249
sample127 0.0035387312 0.0292755650
sample128 -0.0660293668 0.1018566070
sample129 0.0693637508 -0.0695421513
sample130 0.0008492638 -0.0669704304
sample131 0.0431024409 0.0174064976
sample132 -0.0637038544 0.0029374487
sample133 -0.0289495768 -0.0390818877
sample134 0.0446205025 0.0456334578
sample135 0.0712336772 0.0521635155
sample136 0.0596269444 0.0197299524
sample137 0.0793151179 -0.0380628065
sample138 -0.0973549752 -0.0454218489
sample139 0.0539905941 -0.1534327230
sample140 0.0850825564 0.0955814802
sample141 -0.0192680477 -0.0554450163
sample142 -0.0672260966 -0.0461321123
sample143 -0.0303731199 -0.0519260285
sample144 -0.0089365043 0.0145814901
sample145 -0.0638766436 0.0122258147
sample146 0.0585854144 0.0063083571
sample147 0.0894132866 -0.1124615432
sample148 -0.0216364602 -0.0615967245
sample149 -0.0515424131 -0.0839903521
sample150 0.0568285123 -0.0124468810
sample151 -0.0789533020 -0.0261831384
sample152 -0.0330755400 0.1306443542
sample153 -0.1751926821 0.1497731488
sample154 0.0421422198 -0.0037010015
sample155 0.0680176803 0.0095711436
sample156 0.0388909893 0.1057563092
sample157 0.0314769571 0.0561367499
sample158 0.0329620235 0.0353947424
sample159 -0.0398415131 -0.1007373917
sample160 0.0424939825 0.0108496267
sample161 -0.0888372351 -0.0679700372
sample162 -0.0027472768 0.1237843754
sample163 -0.0126101183 0.0725434184
sample164 -0.0566779438 -0.0458324340
sample165 -0.0315336562 -0.0236362423
sample166 -0.0612056484 -0.0425233254
sample167 0.0142729879 0.0179308311
sample168 -0.0169502225 -0.0769617977
sample169 0.0675080910 0.0131505502
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 -0.0012329622 -1.635717e-01
sample2 -0.0724350008 -6.021239e-03
sample3 -0.0188460456 -1.080036e-01
sample4 0.0390145322 3.114296e-04
sample5 0.1774811655 -2.996384e-02
sample6 -0.0451444386 -3.455857e-02
sample7 -0.0226466302 -7.020183e-03
sample8 -0.1033680156 -9.856756e-03
sample9 0.1350011692 8.979098e-02
sample10 0.1259887301 -5.097851e-02
sample11 0.0979788320 7.086533e-02
sample12 -0.0863019071 -8.620317e-02
sample13 -0.1381401094 1.828007e-01
sample14 -0.0615073845 -2.642803e-02
sample15 0.0381598919 -3.101665e-02
sample16 -0.0048776680 1.271861e-03
sample17 -0.0788480906 -1.547552e-02
sample18 -0.0884188741 -3.795487e-02
sample19 0.0703044434 -1.084004e-01
sample20 -0.0025585602 7.975872e-02
sample21 0.0941601757 -4.126737e-02
sample22 -0.0550273465 -7.806746e-02
sample23 0.0679495352 -4.102004e-02
sample24 -0.1310962663 1.649310e-01
sample25 0.0113585311 -4.426862e-02
sample26 -0.1402945859 2.016545e-02
sample27 0.0261561033 1.588409e-03
sample28 -0.0724198676 5.850594e-02
sample29 -0.0330058648 2.060796e-03
sample30 -0.0228752600 -2.015432e-02
sample31 -0.0635067898 -6.670333e-02
sample32 0.0685099643 -4.955273e-02
sample33 -0.0777765225 -1.272079e-01
sample34 0.0157842419 -3.024314e-02
sample35 -0.0529632849 1.500972e-01
sample36 0.0070900628 2.025307e-01
sample37 -0.0442420759 1.802088e-01
sample38 -0.0781511359 -3.676423e-02
sample39 0.0120331891 -3.388840e-02
sample40 -0.0473292224 1.471561e-01
sample41 0.0228189354 -2.673557e-02
sample42 -0.0245360215 -7.960866e-02
sample43 0.1036362822 -8.229577e-02
sample44 -0.1012228681 7.049456e-02
sample45 0.0013732146 -2.450907e-02
sample46 -0.0558510088 2.947355e-03
sample47 -0.0380481226 4.554172e-02
sample48 0.0784342114 4.888982e-02
sample49 -0.0605163880 -1.162352e-02
sample50 0.0530079191 -2.737935e-02
sample51 0.1514646556 5.678347e-02
sample52 0.1860935223 1.246717e-01
sample53 -0.0064177044 -2.700992e-02
sample54 0.0697038359 -2.308388e-02
sample55 0.1633577019 1.366442e-02
sample56 0.1011485125 4.682207e-02
sample57 0.1730374213 1.609603e-01
sample58 -0.0071384703 -1.666955e-02
sample59 -0.0030461745 3.005283e-02
sample60 0.0215835004 2.665877e-01
sample61 0.1510583608 1.002385e-01
sample62 -0.0925533998 -4.845844e-02
sample63 -0.0596311721 -4.137020e-02
sample64 -0.0449225771 -2.600572e-03
sample65 0.0939383788 -4.406908e-02
sample66 0.1063400812 -5.709991e-02
sample67 -0.0201590189 2.361727e-01
sample68 0.0037203102 2.418386e-02
sample69 -0.0645161194 -1.155622e-01
sample70 -0.1013440024 -1.351789e-01
sample71 -0.0016467915 -2.976843e-02
sample72 0.0328892975 -2.835859e-02
sample73 0.0275080038 -5.148186e-02
sample74 0.1341719712 -7.895279e-02
sample75 0.0951575642 -3.943185e-02
sample76 -0.0864722002 3.034990e-02
sample77 -0.1035749566 -2.545354e-02
sample78 -0.1575644058 4.939597e-02
sample79 0.0189137050 4.874679e-02
sample80 0.1384140541 4.263994e-05
sample81 -0.0118846457 -6.357932e-02
sample82 -0.1675308198 3.533910e-02
sample83 -0.0065673453 -7.812612e-02
sample84 0.1486891630 -3.109056e-02
sample85 -0.0532724498 7.417882e-02
sample86 -0.1138477383 -1.917513e-05
sample87 0.0432863947 6.080472e-02
sample88 0.0433450357 1.402491e-01
sample89 0.0331205801 -1.395400e-02
sample90 -0.0607412802 -8.610415e-02
sample91 -0.0566272823 1.303747e-01
sample92 -0.0359582544 1.061604e-01
sample93 -0.0433646361 -4.443635e-02
sample94 -0.0477291288 -1.059574e-01
sample95 -0.0249595772 -3.980526e-02
sample96 0.0035219035 -9.293928e-02
sample97 -0.0066048692 -1.527231e-01
sample98 0.0020366810 -5.579551e-02
sample99 -0.0886615976 -3.728222e-02
sample100 -0.1091259126 -3.560420e-02
sample101 -0.0739726545 -4.318002e-02
sample102 0.0574461264 -2.783910e-02
sample103 0.0142730964 9.705539e-03
sample104 0.0710395202 4.068351e-02
sample105 0.0980831378 -3.452951e-02
sample106 -0.0254259285 3.628985e-02
sample107 -0.0160653417 -9.173394e-02
sample108 -0.0200987644 -2.379692e-02
sample109 -0.0389780634 1.692360e-02
sample110 -0.0326304836 2.988110e-02
sample111 0.0676937592 -6.038212e-02
sample112 0.0167883434 5.336939e-03
sample113 0.0969217079 -2.757601e-02
sample114 -0.0026398364 -9.209158e-02
sample115 -0.0308047277 1.603824e-02
sample116 -0.1240307191 1.273000e-01
sample117 0.0334729089 5.392711e-02
sample118 -0.1037152934 6.252430e-02
sample119 -0.1064176790 1.196202e-01
sample120 -0.0771355041 -1.004932e-01
sample121 -0.0129350719 3.181977e-02
sample122 0.0847492389 -5.568323e-02
sample123 -0.0041336802 7.693172e-03
sample124 -0.0583457917 -8.396387e-02
sample125 0.0634844630 -5.232539e-02
sample126 -0.0662580928 -1.091733e-01
sample127 -0.0865024589 -1.094176e-01
sample128 -0.0627817338 -1.470960e-02
sample129 -0.0336276488 -4.007861e-02
sample130 -0.0293517741 -8.046118e-02
sample131 -0.0469197684 -2.209759e-03
sample132 -0.0241740579 -1.248598e-01
sample133 0.0907303200 1.466700e-02
sample134 -0.0350842097 7.539662e-02
sample135 0.0001333376 9.185369e-03
sample136 -0.0335876089 -9.860276e-02
sample137 -0.0640148945 -7.554472e-02
sample138 0.0060964858 -1.742762e-02
sample139 -0.0592084491 5.614968e-02
sample140 0.0427985870 -1.099553e-02
sample141 0.0618796449 -9.301036e-02
sample142 0.0898554512 3.573420e-02
sample143 0.0817389179 8.880524e-02
sample144 0.0787754786 -3.821391e-02
sample145 0.1085821629 1.569477e-01
sample146 -0.0589558005 -4.373363e-02
sample147 -0.0495330491 7.277182e-03
sample148 0.1161592843 9.079106e-03
sample149 -0.0121579539 7.788372e-02
sample150 -0.0314512550 3.520212e-02
sample151 0.0575382196 -1.945351e-02
sample152 -0.0494542138 7.025536e-02
sample153 -0.0941332613 2.153298e-01
sample154 -0.0335932075 2.078726e-02
sample155 0.0690457620 -2.780411e-02
sample156 0.1039901603 -6.292526e-02
sample157 -0.0408645781 8.065516e-03
sample158 0.1018105285 7.816872e-03
sample159 -0.0281730495 -1.207205e-02
sample160 0.1643053014 2.978112e-03
sample161 0.0374329236 8.524611e-02
sample162 -0.0804535258 8.349758e-02
sample163 -0.0743227881 -1.406222e-02
sample164 0.1208806049 -2.139458e-02
sample165 0.1608115914 2.025193e-02
sample166 -0.0425944570 -2.660712e-02
sample167 -0.0226849483 -4.464282e-02
sample168 -0.0180735542 -7.466057e-04
sample169 0.0190778982 2.645402e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
[,1] [,2]
sample1 -0.0572060227 -1.729087e-02
sample2 0.0875245208 1.112588e-02
sample3 0.0403482602 -3.168994e-02
sample4 -0.0218345996 4.052760e-06
sample5 -0.0150905011 4.795041e-03
sample6 -0.0924362933 4.511003e-02
sample7 -0.0793066751 -1.243823e-02
sample8 -0.1342997187 6.215220e-02
sample9 -0.0338886944 -1.854401e-02
sample10 0.0020547173 1.749421e-02
sample11 0.0037275602 -2.364116e-02
sample12 -0.0753094533 2.772698e-02
sample13 0.0856160091 3.679963e-02
sample14 -0.0737457307 2.668452e-02
sample15 -0.0062111746 -3.554864e-03
sample16 -0.0602355268 6.675115e-02
sample17 0.1086768843 2.524534e-02
sample18 0.0702999472 2.231671e-02
sample19 0.0173785882 -3.024846e-02
sample20 0.0484173812 -3.310904e-02
sample21 0.0124657042 6.517144e-02
sample22 -0.0140989936 -3.159137e-02
sample23 -0.0627028403 -5.393710e-04
sample24 0.0919972100 7.909297e-02
sample25 0.0326998483 -1.945206e-02
sample26 0.1064741246 2.120849e-02
sample27 0.0166058995 -4.964993e-02
sample28 0.0743504770 2.614211e-02
sample29 -0.0511008491 -2.782647e-02
sample30 0.0962250842 -3.974893e-03
sample31 -0.0869563008 5.250819e-02
sample32 0.0271858919 1.552005e-02
sample33 -0.0448364581 6.243160e-03
sample34 0.0718415218 1.469396e-02
sample35 0.0403086451 -1.632629e-02
sample36 -0.1036402827 -1.304320e-02
sample37 -0.0159385744 -3.036525e-02
sample38 0.0182198369 -4.034805e-02
sample39 0.0690363619 8.058350e-03
sample40 -0.0467312750 -2.810325e-02
sample41 0.0263674438 -5.171216e-02
sample42 0.0374578960 -1.268634e-02
sample43 0.0132336869 9.536642e-03
sample44 -0.1119154428 5.028683e-02
sample45 0.0759639367 4.587903e-02
sample46 0.0871885519 -4.670385e-02
sample47 0.0721490571 -1.288540e-02
sample48 0.0005086144 -1.290565e-02
sample49 -0.0858177028 5.173760e-02
sample50 0.0118992665 -7.276215e-02
sample51 -0.0426446855 5.306205e-02
sample52 -0.0381605826 3.086785e-02
sample53 -0.0855757630 6.730043e-02
sample54 0.0261723092 9.184260e-03
sample55 -0.0156418304 4.682404e-04
sample56 0.0307831193 2.597550e-02
sample57 -0.0157242103 4.829381e-02
sample58 -0.0031174404 1.359898e-02
sample59 -0.0373001859 5.868397e-03
sample60 -0.0142609099 5.831654e-03
sample61 -0.0122255144 2.663579e-02
sample62 0.0228002942 -8.692265e-03
sample63 -0.0833127581 5.473229e-02
sample64 -0.1166548159 4.196500e-02
sample65 0.0038808902 8.568590e-03
sample66 0.0011561811 1.766612e-02
sample67 -0.1129311062 -2.608702e-02
sample68 -0.0382526429 -3.804045e-02
sample69 -0.0476502440 4.003241e-03
sample70 -0.0110329882 -2.752719e-02
sample71 0.0096850282 -5.627056e-02
sample72 0.0487124704 -8.800131e-03
sample73 0.0773058132 8.239864e-03
sample74 -0.0102488176 2.454957e-02
sample75 -0.0286613976 -8.387293e-03
sample76 -0.0472655595 -2.129315e-02
sample77 -0.0865043074 -7.296820e-03
sample78 0.1070293698 2.818346e-02
sample79 -0.0165060681 -6.659721e-02
sample80 -0.0206765949 -8.712112e-03
sample81 -0.0050943615 -3.079175e-02
sample82 0.1153622361 -1.647054e-02
sample83 0.0367979217 -2.538114e-03
sample84 0.0199463070 -1.468961e-02
sample85 -0.0827122185 -2.709824e-04
sample86 0.0969487314 -1.699897e-02
sample87 0.0421957457 -1.965953e-02
sample88 0.0215934743 1.566050e-02
sample89 0.0751559502 2.811652e-02
sample90 -0.0057328000 -8.283795e-03
sample91 -0.1134005268 -8.603522e-02
sample92 -0.0101689918 -6.894992e-02
sample93 0.0725967502 -6.003176e-03
sample94 -0.0096878852 -4.693081e-03
sample95 -0.0223502239 -3.139636e-02
sample96 -0.0013232863 -1.963604e-02
sample97 -0.0476541710 1.183660e-02
sample98 0.0269546160 -5.978398e-03
sample99 0.0728179461 4.597884e-02
sample100 -0.0413398038 1.079347e-02
sample101 0.0087536994 -6.796076e-02
sample102 0.0032509529 3.932612e-03
sample103 0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105 0.0294940465 -7.140722e-03
sample106 0.0686472054 1.462895e-02
sample107 0.0748635927 8.401339e-03
sample108 0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110 0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112 0.0570870472 1.066018e-02
sample113 -0.0200110554 1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586 1.089843e-02
sample121 0.0542443861 3.861344e-02
sample122 0.0178575357 3.027138e-02
sample123 0.0775020581 -1.636852e-02
sample124 -0.0460701050 1.814758e-02
sample125 0.0543846585 2.075898e-03
sample126 -0.0729417144 3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128 0.0908136899 3.758801e-02
sample129 0.0552445878 -1.879062e-02
sample130 0.0007128089 -1.294308e-02
sample131 -0.0693311345 7.357082e-03
sample132 -0.0556565156 3.126995e-02
sample133 0.0375870104 -1.977240e-02
sample134 -0.1229130924 3.159495e-02
sample135 0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201 1.040683e-02
sample140 0.0452288969 -1.876279e-02
sample141 -0.0189142561 2.247042e-02
sample142 0.0297545566 1.280524e-02
sample143 0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491 5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147 0.0726083535 -1.239968e-02
sample148 -0.0284795794 3.389732e-02
sample149 0.0082261455 -6.399305e-02
sample150 -0.0765013197 2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152 0.0403422737 -2.714879e-02
sample153 0.0629117719 7.425085e-02
sample154 0.0551622927 -3.548984e-02
sample155 0.0654439133 -1.005306e-02
sample156 0.0209310714 -1.390213e-02
sample157 0.0851522597 6.577150e-03
sample158 0.0208354599 -4.663078e-03
sample159 -0.0498794349 1.913257e-02
sample160 0.0216074437 1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162 0.0963663017 5.705881e-02
sample163 -0.1009542191 7.174224e-02
sample164 0.0109881996 1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357 2.673084e-02
sample167 -0.0825048036 2.278863e-03
sample168 0.0486147429 1.793843e-02
sample169 0.0302506727 8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
[,1] [,2]
sample1 -0.0621842115 -1.364509e-02
sample2 0.0944623785 9.720892e-03
sample3 0.0406196267 -2.236338e-02
sample4 -0.0229316496 -3.932487e-04
sample5 -0.0157330047 3.231033e-03
sample6 -0.0945794025 3.120720e-02
sample7 -0.0854427118 -1.052880e-02
sample8 -0.1376625920 4.286608e-02
sample9 -0.0377115311 -1.415134e-02
sample10 0.0035244506 1.280825e-02
sample11 0.0016639987 -1.717895e-02
sample12 -0.0781403168 1.884368e-02
sample13 0.0938400516 2.838858e-02
sample14 -0.0759839772 1.810989e-02
sample15 -0.0068340837 -2.705361e-03
sample16 -0.0590150849 4.757848e-02
sample17 0.1178805097 2.040526e-02
sample18 0.0767858320 1.756604e-02
sample19 0.0157112113 -2.172867e-02
sample20 0.0485318300 -2.327033e-02
sample21 0.0185928176 4.777095e-02
sample22 -0.0191358702 -2.329775e-02
sample23 -0.0672994194 -1.535656e-03
sample24 0.1047476642 5.935707e-02
sample25 0.0329844953 -1.358036e-02
sample26 0.1154952052 1.741529e-02
sample27 0.0133849853 -3.590922e-02
sample28 0.0821554039 2.042376e-02
sample29 -0.0567643690 -2.123848e-02
sample30 0.1016073931 -1.134728e-03
sample31 -0.0880396372 3.670548e-02
sample32 0.0300363338 1.182406e-02
sample33 -0.0467252272 3.739254e-03
sample34 0.0783666394 1.203777e-02
sample35 0.0424227097 -1.118559e-02
sample36 -0.1107646166 -1.143464e-02
sample37 -0.0191667664 -2.246060e-02
sample38 0.0155968095 -2.909621e-02
sample39 0.0746847148 7.148218e-03
sample40 -0.0517028178 -2.137267e-02
sample41 0.0234979494 -3.723018e-02
sample42 0.0388797356 -8.557228e-03
sample43 0.0149555568 7.210002e-03
sample44 -0.1150305613 3.461805e-02
sample45 0.0846146236 3.486020e-02
sample46 0.0884426404 -3.246853e-02
sample47 0.0748644971 -8.083045e-03
sample48 -0.0012033198 -9.403647e-03
sample49 -0.0872662737 3.616245e-02
sample50 0.0066941314 -5.284863e-02
sample51 -0.0411777630 3.791830e-02
sample52 -0.0379355780 2.180834e-02
sample53 -0.0851639886 4.751761e-02
sample54 0.0288006248 7.184424e-03
sample55 -0.0164920835 5.919925e-05
sample56 0.0355115616 1.951043e-02
sample57 -0.0141146068 3.492409e-02
sample58 -0.0015636132 9.862883e-03
sample59 -0.0390656483 3.590929e-03
sample60 -0.0139454780 3.963030e-03
sample61 -0.0106410274 1.919705e-02
sample62 0.0236748439 -5.922677e-03
sample63 -0.0846790877 3.839102e-02
sample64 -0.1202581015 2.846469e-02
sample65 0.0050548584 6.328644e-03
sample66 0.0028013072 1.291807e-02
sample67 -0.1231623009 -2.112565e-02
sample68 -0.0437782161 -2.845072e-02
sample69 -0.0501199692 2.053469e-03
sample70 -0.0140278645 -2.027157e-02
sample71 0.0057489505 -4.085977e-02
sample72 0.0511212704 -5.522408e-03
sample73 0.0828141409 7.431582e-03
sample74 -0.0085959456 1.772951e-02
sample75 -0.0312180394 -6.636869e-03
sample76 -0.0519051781 -1.640191e-02
sample77 -0.0925924762 -6.907800e-03
sample78 0.1163971046 2.251122e-02
sample79 -0.0240906926 -4.887766e-02
sample80 -0.0221327065 -6.730703e-03
sample81 -0.0072114968 -2.254399e-02
sample82 0.1204416674 -9.907422e-03
sample83 0.0386739485 -1.171663e-03
sample84 0.0195988488 -1.033806e-02
sample85 -0.0877680171 -1.725057e-03
sample86 0.1023541048 -1.062501e-02
sample87 0.0425213089 -1.356865e-02
sample88 0.0244788514 1.180820e-02
sample89 0.0804276691 2.188588e-02
sample90 -0.0074639871 -6.140721e-03
sample91 -0.1278832404 -6.485140e-02
sample92 -0.0162199697 -5.048358e-02
sample93 0.0769344893 -3.045135e-03
sample94 -0.0104345587 -3.593172e-03
sample95 -0.0260058453 -2.330475e-02
sample96 -0.0025018700 -1.433516e-02
sample97 -0.0492358305 7.774183e-03
sample98 0.0279220220 -3.862141e-03
sample99 0.0813921923 3.487339e-02
sample100 -0.0428797405 7.112807e-03
sample101 0.0032855240 -4.940743e-02
sample102 0.0038439317 2.938008e-03
sample103 0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105 0.0314853405 -4.656633e-03
sample106 0.0726456731 1.192390e-02
sample107 0.0807342975 7.508627e-03
sample108 0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110 0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112 0.0599954082 8.820317e-03
sample113 -0.0195006577 1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131 6.937326e-03
sample121 0.0613899126 2.915307e-02
sample122 0.0218424338 2.241775e-02
sample123 0.0809008460 -1.051759e-02
sample124 -0.0472109313 1.239887e-02
sample125 0.0583180947 2.521167e-03
sample126 -0.0753941872 2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128 0.1000212216 2.908091e-02
sample129 0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877 4.091672e-03
sample132 -0.0566219024 2.179861e-02
sample133 0.0384172955 -1.372840e-02
sample134 -0.1280862736 2.077912e-02
sample135 0.0592633273 6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861 6.729304e-03
sample140 0.0468926306 -1.285498e-02
sample141 -0.0186248693 1.605439e-02
sample142 0.0328031246 9.887746e-03
sample143 0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310 3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147 0.0765025670 -7.714769e-03
sample148 -0.0276016641 2.420589e-02
sample149 0.0027545308 -4.653007e-02
sample150 -0.0792296010 1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152 0.0409796547 -1.907063e-02
sample153 0.0734301757 5.528780e-02
sample154 0.0557740684 -2.487723e-02
sample155 0.0689436560 -6.127635e-03
sample156 0.0212272938 -9.747423e-03
sample157 0.0911931194 6.355708e-03
sample158 0.0220840645 -3.016357e-03
sample159 -0.0513244242 1.304175e-02
sample160 0.0246213576 1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162 0.1078802043 4.337260e-02
sample163 -0.1017965082 5.047171e-02
sample164 0.0119430799 9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180 1.899222e-02
sample167 -0.0872832229 1.516582e-04
sample168 0.0540714512 1.397701e-02
sample169 0.0328432652 7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
[,1] [,2]
sample1 0.0133684846 2.195848e-02
sample2 0.0254157197 -1.058416e-02
sample3 -0.0049551479 -4.840017e-03
sample4 0.0310390570 -1.063929e-02
sample5 0.0046941318 -6.488426e-03
sample6 -0.0107406753 -1.026702e-02
sample7 -0.0225157631 2.624712e-04
sample8 0.0141320952 -9.505821e-03
sample9 0.0029681280 2.078210e-02
sample10 0.0131729174 -2.275042e-03
sample11 -0.0004164298 1.994019e-02
sample12 -0.0095211620 3.759883e-02
sample13 0.0091018604 -7.953956e-03
sample14 -0.0106557524 -9.181659e-03
sample15 -0.0249924121 3.262724e-02
sample16 -0.0156216400 1.375700e-02
sample17 -0.0019382446 1.073994e-03
sample18 -0.0221072481 -8.703592e-03
sample19 0.0146917619 -1.311712e-02
sample20 -0.0160353760 1.826290e-02
sample21 0.0035947899 -9.616341e-03
sample22 -0.0225060762 -2.532589e-03
sample23 0.0310000683 3.033060e-03
sample24 0.0499544372 1.809450e-02
sample25 0.0284442301 -1.932558e-02
sample26 0.0188220043 2.146985e-02
sample27 -0.0257763219 -1.999228e-03
sample28 0.0120888648 1.125834e-02
sample29 -0.0236482520 4.426726e-02
sample30 -0.0385486305 -2.055935e-02
sample31 -0.0181539336 -5.877838e-03
sample32 -0.0302630460 -2.607192e-03
sample33 -0.0319565715 -1.562628e-02
sample34 -0.0197970124 9.906813e-03
sample35 -0.0247412713 -5.434440e-03
sample36 -0.0386259060 -3.190394e-02
sample37 -0.0566199273 -4.192574e-02
sample38 -0.0142060273 2.259644e-02
sample39 0.0053589035 1.076485e-02
sample40 -0.0552546493 -3.819896e-02
sample41 -0.0013089975 9.278818e-05
sample42 0.0137252142 -1.664652e-02
sample43 -0.0151259626 -6.290953e-03
sample44 0.0617391754 -1.442883e-02
sample45 0.0231410886 1.163143e-03
sample46 -0.0148898209 -1.384176e-04
sample47 -0.0187252536 1.221690e-02
sample48 0.0432839432 1.416671e-02
sample49 0.0160818605 -3.588745e-02
sample50 0.0059333545 4.067003e-02
sample51 -0.0142914866 7.776270e-03
sample52 -0.0086339952 7.208917e-03
sample53 -0.0207386980 6.272432e-03
sample54 -0.0039856719 -1.316934e-02
sample55 -0.0056217017 5.692315e-03
sample56 0.0000123292 8.978290e-04
sample57 -0.0095805555 1.324253e-02
sample58 -0.0124160295 -7.326376e-03
sample59 -0.0400195442 -1.349736e-02
sample60 -0.0460063358 2.770091e-02
sample61 -0.0245266456 1.470710e-02
sample62 -0.0366022783 -3.437352e-03
sample63 0.0013742171 3.288796e-02
sample64 -0.0070599859 2.739588e-02
sample65 0.0041201911 1.498268e-02
sample66 0.0143173351 -1.968812e-02
sample67 -0.0467477531 -1.929938e-02
sample68 -0.0306751978 -1.436184e-02
sample69 -0.0125317217 4.130407e-03
sample70 -0.0068071487 8.080857e-03
sample71 0.0169170264 -7.027348e-03
sample72 -0.0346909749 -1.333770e-02
sample73 -0.0280506153 1.493843e-02
sample74 -0.0182611498 3.294697e-03
sample75 -0.0120563964 8.974612e-03
sample76 0.0001437236 -4.253184e-02
sample77 0.0065330299 -5.252886e-02
sample78 0.0288278141 -1.127782e-02
sample79 0.0503961481 -1.023318e-02
sample80 -0.0207693429 3.648391e-02
sample81 0.0163562768 -9.074596e-03
sample82 -0.0084317129 -1.478976e-02
sample83 -0.0474097918 -1.103126e-02
sample84 0.0177181395 -7.191197e-03
sample85 -0.0342718548 -3.082360e-02
sample86 -0.0261671791 -1.089491e-02
sample87 -0.0009486358 -2.411514e-02
sample88 0.0020528931 -2.894615e-02
sample89 -0.0189361111 -2.638639e-03
sample90 -0.0009863658 -2.390075e-02
sample91 -0.0124352695 8.153234e-02
sample92 0.0564264106 -8.909537e-03
sample93 -0.0081461774 1.570851e-02
sample94 -0.0054896581 1.547251e-02
sample95 0.0224073150 -4.374348e-04
sample96 0.0173528924 -3.050441e-03
sample97 0.0067948115 5.008237e-03
sample98 -0.0116030825 1.498764e-02
sample99 0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101 0.0124923691 3.091503e-02
sample102 0.0650835386 -1.367400e-02
sample103 -0.0042741828 7.855985e-03
sample104 0.0250591040 -4.171938e-03
sample105 0.0157516368 -3.121990e-02
sample106 0.0060593853 -5.101693e-03
sample107 -0.0098329626 1.044506e-02
sample108 0.0044269853 4.142036e-03
sample109 0.0572473486 1.517542e-02
sample110 0.0090474827 -5.119868e-03
sample111 0.0444263015 7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113 0.0241047399 6.706740e-03
sample114 0.0074558775 -4.728652e-03
sample115 0.0611851433 1.117210e-02
sample116 0.0432646951 -1.380556e-02
sample117 0.0516750066 -3.575617e-02
sample118 0.0139942100 -3.279138e-03
sample119 0.0291722987 5.587946e-02
sample120 0.0103515853 -1.690016e-03
sample121 -0.0091396331 3.552116e-02
sample122 0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124 0.0283466326 3.127845e-03
sample125 0.0016472378 -2.770692e-02
sample126 -0.0286529417 3.489336e-02
sample127 -0.0010224500 7.483214e-03
sample128 0.0209049296 2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968 4.637390e-03
sample132 0.0198526786 5.723983e-04
sample133 0.0088812957 -9.988115e-03
sample134 -0.0137484514 1.172591e-02
sample135 -0.0220314568 1.347465e-02
sample136 -0.0185173353 5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138 0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762 2.456863e-02
sample141 0.0058369763 -1.420854e-02
sample142 0.0207886071 -1.188764e-02
sample143 0.0092832598 -1.324238e-02
sample144 0.0028442140 3.627979e-03
sample145 0.0199749569 2.862202e-03
sample146 -0.0182236697 1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148 0.0065435868 -1.572917e-02
sample149 0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151 0.0245166984 -6.888241e-03
sample152 0.0107259913 3.314630e-02
sample153 0.0550963965 3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574 2.642246e-03
sample156 -0.0117803505 2.698265e-02
sample157 -0.0096167165 1.433840e-02
sample158 -0.0101754772 9.137620e-03
sample159 0.0120662931 -2.565236e-02
sample160 -0.0132238202 2.916023e-03
sample161 0.0274491966 -1.748284e-02
sample162 0.0012482909 3.152261e-02
sample163 0.0042031315 1.830701e-02
sample164 0.0174896157 -1.175915e-02
sample165 0.0097517662 -6.119019e-03
sample166 0.0190134679 -1.121582e-02
sample167 -0.0044140836 4.665585e-03
sample168 0.0049689168 -1.941822e-02
sample169 -0.0209802098 3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
[,1] [,2]
sample1 -0.0515543627 -0.0305856787
sample2 -0.0144993256 0.0236342950
sample3 -0.0371833108 -0.0140263348
sample4 0.0068945388 -0.0132539692
sample5 0.0215035333 -0.0663338101
sample6 -0.0187055152 0.0088773016
sample7 -0.0061521552 0.0064029054
sample8 -0.0210874459 0.0334652901
sample9 0.0516865043 -0.0291142799
sample10 0.0059440366 -0.0527217447
sample11 0.0393010793 -0.0200624712
sample12 -0.0420837100 0.0131331362
sample13 0.0333252565 0.0818552509
sample14 -0.0190062644 0.0160202175
sample15 -0.0030968049 -0.0189230681
sample16 -0.0004452158 0.0018880102
sample17 -0.0185848615 0.0240170131
sample18 -0.0273093598 0.0230213640
sample19 -0.0217761111 -0.0445894441
sample20 0.0245820821 0.0159812738
sample21 0.0034527644 -0.0400016054
sample22 -0.0340789054 0.0039289109
sample23 -0.0010344929 -0.0310161212
sample24 0.0289468503 0.0760962436
sample25 -0.0119098496 -0.0122798760
sample26 -0.0181001057 0.0517892852
sample27 0.0050465417 -0.0086515844
sample28 0.0057491502 0.0358830107
sample29 -0.0051104246 0.0116605117
sample30 -0.0103085904 0.0039678538
sample31 -0.0319929858 0.0090606113
sample32 -0.0036232521 -0.0328202010
sample33 -0.0534742153 0.0024751837
sample34 -0.0067495749 -0.0111000311
sample35 0.0378745721 0.0465929296
sample36 0.0647886800 0.0359987924
sample37 0.0488441236 0.0492906912
sample38 -0.0251514062 0.0197110110
sample39 -0.0085428066 -0.0105117852
sample40 0.0379324087 0.0440810741
sample41 -0.0044199152 -0.0128820644
sample42 -0.0292553573 -0.0067045265
sample43 -0.0077829155 -0.0510178219
sample44 0.0045122248 0.0479660309
sample45 -0.0074444298 -0.0051116726
sample46 -0.0088025512 0.0196186661
sample47 0.0076696301 0.0215947965
sample48 0.0290108585 -0.0175568376
sample49 -0.0141754858 0.0184717099
sample50 0.0006282201 -0.0233054373
sample51 0.0441995177 -0.0410022921
sample52 0.0715329391 -0.0399499475
sample53 -0.0095954087 -0.0029140909
sample54 0.0048933768 -0.0281884386
sample55 0.0327325487 -0.0532290012
sample56 0.0323068984 -0.0256595538
sample57 0.0806603122 -0.0286748097
sample58 -0.0064792049 -0.0006945349
sample59 0.0088958941 0.0067389649
sample60 0.0874124612 0.0431964341
sample61 0.0577604571 -0.0326112099
sample62 -0.0313318464 0.0224391756
sample63 -0.0233625220 0.0125110562
sample64 -0.0086426068 0.0148770341
sample65 0.0025256193 -0.0404466327
sample66 0.0006014071 -0.0471576264
sample67 0.0706087042 0.0516228406
sample68 0.0082301011 0.0033109509
sample69 -0.0475076743 0.0001452708
sample70 -0.0600773716 0.0089986962
sample71 -0.0096321627 -0.0050761187
sample72 -0.0031773546 -0.0166221542
sample73 -0.0113700517 -0.0191726684
sample74 -0.0014179662 -0.0608101325
sample75 0.0041911740 -0.0399981269
sample76 -0.0055326449 0.0353114263
sample77 -0.0260214459 0.0305731380
sample78 -0.0119267436 0.0632236007
sample79 0.0186017239 0.0027402910
sample80 0.0241047889 -0.0472697181
sample81 -0.0220288317 -0.0079577210
sample82 -0.0180751258 0.0639051029
sample83 -0.0256671713 -0.0125898269
sample84 0.0161392598 -0.0567222449
sample85 0.0139988188 0.0322763454
sample86 -0.0198382995 0.0389225776
sample87 0.0266270281 -0.0032979996
sample88 0.0515677078 0.0117902495
sample89 0.0014022125 -0.0140510488
sample90 -0.0375949749 0.0044004551
sample91 0.0310397965 0.0440610926
sample92 0.0270570567 0.0324380452
sample93 -0.0215009202 0.0063993941
sample94 -0.0415702912 -0.0037692077
sample95 -0.0168416047 0.0010019120
sample96 -0.0285582661 -0.0187991000
sample97 -0.0490843868 -0.0266760748
sample98 -0.0171579033 -0.0112897471
sample99 -0.0271316525 0.0232395583
sample100 -0.0301789816 0.0305498693
sample101 -0.0264371151 0.0170723968
sample102 0.0012767734 -0.0248949597
sample103 0.0055214687 -0.0030040587
sample104 0.0251346074 -0.0165212671
sample105 0.0062424215 -0.0400309901
sample106 0.0069768684 0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679 0.0023637162
sample109 -0.0014762845 0.0165583675
sample110 0.0036971063 0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112 0.0046098120 -0.0048009350
sample113 0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453 0.0135805368
sample116 0.0183575759 0.0665377581
sample117 0.0227640036 -0.0012287760
sample118 0.0015695248 0.0472617382
sample119 0.0190084932 0.0590034062
sample120 -0.0449645755 0.0072755697
sample121 0.0077307184 0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123 0.0016959300 0.0028593594
sample124 -0.0365091615 0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408 0.0019165544
sample127 -0.0494064872 0.0088209044
sample128 -0.0155454766 0.0186819802
sample129 -0.0184340400 0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422 0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133 0.0204029276 -0.0282209049
sample134 0.0175513332 0.0262883962
sample135 0.0029009925 0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323 0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139 0.0073103935 0.0308956174
sample140 0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142 0.0268670676 -0.0239229634
sample143 0.0421049126 -0.0110888235
sample144 0.0017253664 -0.0341766012
sample145 0.0681741320 -0.0073526377
sample146 -0.0239965222 0.0118396767
sample147 -0.0063453522 0.0183130585
sample148 0.0230825251 -0.0379753037
sample149 0.0223298673 0.0188909118
sample150 0.0055709108 0.0174179009
sample151 0.0039177786 -0.0233533275
sample152 0.0134325667 0.0302344591
sample153 0.0511990309 0.0730230140
sample154 0.0006698324 0.0154177486
sample155 0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599 0.0154934573
sample158 0.0201775524 -0.0332982124
sample159 -0.0086909001 0.0073496711
sample160 0.0295437331 -0.0555734536
sample161 0.0332754288 0.0033779619
sample162 0.0121954537 0.0433540412
sample163 -0.0173490933 0.0227219128
sample164 0.0143374783 -0.0453542590
sample165 0.0343612593 -0.0511194536
sample166 -0.0157536004 0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919 0.0060747155
sample169 0.0116231468 -0.0015112800
>
> ## 3.3 Plotting VAF
>
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
>
> # JIVE plotVAF
> plotVAF(jiveRes)
>
>
> #########################
> ## PART 4. Plot Results
>
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
>
> # Scores for common part. JIVE
> plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
>
> # Scores for common part. O2PLS.
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for common part. O2PLS.
> plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common",
+ combined=TRUE,block=NULL,color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
>
> # Scores for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for distinctive part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual",
+ combined=TRUE,block=NULL,color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
> # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block)
> p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for common and distinctive part. DISCO (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Loadings for common part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> # Loadings for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> # Combined plot for loadings from common and distinctive part (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
>
> ## Plot scores and loadings togheter: Common components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> ## Plot scores and loadings togheter: Common components O2PLS
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> ## Plot scores and loadings togheter: Distintive components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
>
>
> proc.time()
user system elapsed
10.78 0.57 11.43
|
STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsPCA.Rout
R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (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 0.0781575685 -0.0431550514
sample2 -0.1192221481 0.0294018969
sample3 -0.0531408557 -0.0746837533
sample4 0.0292971668 -0.0006037144
sample5 0.0202090717 0.0110454866
sample6 0.1226088418 0.1053494280
sample7 0.1078931452 -0.0322416667
sample8 0.1782890986 0.1449328992
sample9 0.0468697264 -0.0455171563
sample10 -0.0036032760 0.0420076335
sample11 -0.0035566356 -0.0566284758
sample12 0.1006129711 0.0641394700
sample13 -0.1174412981 0.0907474861
sample14 0.0981203562 0.0617764661
sample15 0.0085337369 -0.0086953590
sample16 0.0783146745 0.1581335245
sample17 -0.1483610696 0.0638580055
sample18 -0.0963084335 0.0556690015
sample19 -0.0217243028 -0.0720130513
sample20 -0.0635633844 -0.0779608294
sample21 -0.0201844128 0.1566381549
sample22 0.0218274027 -0.0764053670
sample23 0.0852038948 -0.0032768373
sample24 -0.1287181899 0.1924421107
sample25 -0.0430575690 -0.0456641893
sample26 -0.1453899888 0.0541456527
sample27 -0.0197483427 -0.1185590016
sample28 -0.1025339538 0.0650654044
sample29 0.0706022606 -0.0682930359
sample30 -0.1295622826 -0.0066673609
sample31 0.1147449310 0.1232729075
sample32 -0.0374308120 0.0380252767
sample33 0.0599520935 0.0136939189
sample34 -0.0984199253 0.0375366582
sample35 -0.0543096358 -0.0378032000
sample36 0.1403628125 -0.0343633739
sample37 0.0228947867 -0.0732682634
sample38 -0.0222072869 -0.0962565928
sample39 -0.0941739242 0.0215179717
sample40 0.0643807274 -0.0687713122
sample41 -0.0327634860 -0.1232187170
sample42 -0.0500431622 -0.0292515645
sample43 -0.0184497121 0.0233045860
sample44 0.1487889005 0.1171203654
sample45 -0.1050778960 0.1123137612
sample46 -0.1151191448 -0.1093994274
sample47 -0.0962591449 -0.0288415932
sample48 0.0004832454 -0.0310383970
sample49 0.1135203723 0.1213934890
sample50 -0.0123549791 -0.1740763742
sample51 0.0550527347 0.1258932792
sample52 0.0499118385 0.0728582613
sample53 0.1119772624 0.1588066606
sample54 -0.0360055719 0.0228586048
sample55 0.0210418827 0.0006751581
sample56 -0.0434171580 0.0633131494
sample57 0.0197820543 0.1150755817
sample58 0.0030440731 0.0326128891
sample59 0.0500256865 0.0129526295
sample60 0.0184280129 0.0136224232
sample61 0.0150298901 0.0635100207
sample62 -0.0304758568 -0.0201232322
sample63 0.1102250035 0.1285968068
sample64 0.1552586782 0.0971186091
sample65 -0.0058503840 0.0207102172
sample66 -0.0025607547 0.0424282895
sample67 0.1546638797 -0.0661571900
sample68 0.0536374434 -0.0923600482
sample69 0.0640333096 0.0082004827
sample70 0.0163521890 -0.0663227047
sample71 -0.0102536054 -0.1345966943
sample72 -0.0654191599 -0.0196032438
sample73 -0.1048553136 0.0221002896
sample74 0.0123800544 0.0586158778
sample75 0.0392079809 -0.0209724851
sample76 0.0648954612 -0.0524759326
sample77 0.1172922660 -0.0201201070
sample78 -0.1463072795 0.0708396217
sample79 0.0265208759 -0.1603430831
sample80 0.0279739301 -0.0214150920
sample81 0.0079212180 -0.0738497571
sample82 -0.1544234535 -0.0361449802
sample83 -0.0494205197 -0.0049933906
sample84 -0.0259039770 -0.0346593539
sample85 0.1116487547 -0.0031399934
sample86 -0.1306478912 -0.0377153274
sample87 -0.0554777854 -0.0459739265
sample88 -0.0301626637 0.0382207078
sample89 -0.1016866173 0.0694080258
sample90 0.0086821706 -0.0201324030
sample91 0.1578629954 -0.2097790347
sample92 0.0170933392 -0.1655942321
sample93 -0.0979805032 -0.0121499895
sample94 0.0131486303 -0.0114929308
sample95 0.0315682462 -0.0758919781
sample96 0.0024125855 -0.0470187459
sample97 0.0634545827 0.0270302419
sample98 -0.0359372474 -0.0135465472
sample99 -0.1009167777 0.1124709594
sample100 0.0551754128 0.0246502709
sample101 -0.0080115873 -0.1627408918
sample102 -0.0046451431 0.0095465747
sample103 -0.0472520777 -0.0940383070
sample104 0.0198157386 -0.0591149713
sample105 -0.0400239033 -0.0160950862
sample106 -0.0923810207 0.0369003232
sample107 -0.1019372307 0.0224967904
sample108 -0.0877091506 -0.0128850416
sample109 0.0864820137 -0.0901087160
sample110 -0.1223116515 -0.0096109537
sample111 0.0257352367 -0.0936286149
sample112 -0.0765285898 0.0270380763
sample113 0.0258799728 0.0377435785
sample114 0.0021141182 -0.0882041289
sample115 0.0303455120 -0.0723741835
sample116 0.0780504313 -0.0685166588
sample117 0.0536893921 -0.0912030641
sample118 0.0666649844 -0.0236262387
sample119 0.1021872622 -0.2325006589
sample120 0.0750216326 0.0243344173
sample121 -0.0756937940 0.0942971390
sample122 -0.0259632208 0.0731918247
sample123 -0.1037844630 -0.0369177962
sample124 0.0611205072 0.0421643701
sample125 -0.0738472617 0.0066943933
sample126 0.0972919234 0.0762701534
sample127 0.0824699510 -0.0096644977
sample128 -0.1249411668 0.0929251278
sample129 -0.0734063555 -0.0434311468
sample130 -0.0003500186 -0.0309857445
sample131 0.0930184092 0.0155971569
sample132 0.0736220527 0.0732969607
sample133 -0.0498398370 -0.0462456790
sample134 0.1644872589 0.0720048807
sample135 -0.0752294965 0.0003871907
sample136 0.0227150132 -0.0495467808
sample137 0.0564721866 -0.0288858199
sample138 0.0255986431 -0.0610933814
sample139 0.0621218802 0.0235859913
sample140 -0.0604148806 -0.0435529639
sample141 0.0246743011 0.0532629479
sample142 -0.0409564000 0.0316232232
sample143 -0.0077356434 -0.0476909426
sample144 0.0173241017 -0.0156786220
sample145 0.0485467449 0.1202736499
sample146 0.0419650146 -0.0811239142
sample147 -0.0977304520 -0.0274768337
sample148 0.0368253174 0.0803969017
sample149 -0.0072864839 -0.1533018399
sample150 0.1020825514 0.0624824652
sample151 0.0305397097 -0.0289339626
sample152 -0.0533595219 -0.0638336086
sample153 -0.0891639881 0.1799447834
sample154 -0.0727554283 -0.0834128043
sample155 -0.0880665717 -0.0220768234
sample156 -0.0276558756 -0.0326600756
sample157 -0.1155031540 0.0183636391
sample158 -0.0281506667 -0.0104910780
sample159 0.0663233700 0.0443808476
sample160 -0.0302644011 0.0404302877
sample161 0.0114712859 -0.0591085674
sample162 -0.1337091153 0.1398131322
sample163 0.1330120606 0.1688768857
sample164 -0.0150338218 0.0028373568
sample165 0.0076518788 -0.0164146626
sample166 0.0367791386 0.0630612124
sample167 0.1111989856 0.0030066832
sample168 -0.0672983034 0.0446266136
sample169 -0.0413003590 0.0224449375
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 -0.0420461462 0.0867866196
sample2 -0.0820850148 -0.0410968636
sample3 0.0155967664 -0.0195186395
sample4 -0.1001342867 -0.0410776164
sample5 -0.0153479979 -0.0253257697
sample6 0.0340237244 -0.0408223414
sample7 0.0722602896 0.0002323759
sample8 -0.0457622064 -0.0370006800
sample9 -0.0086216018 0.0820184490
sample10 -0.0423631885 -0.0083917542
sample11 0.0022594392 0.0787764088
sample12 0.0322075434 0.1479823347
sample13 -0.0293971988 -0.0306742812
sample14 0.0337429792 -0.0367508414
sample15 0.0815560598 0.1275613672
sample16 0.0508329364 0.0540604279
sample17 0.0062554313 0.0041024879
sample18 0.0705600032 -0.0351053484
sample19 -0.0476782395 -0.0509595351
sample20 0.0523028499 0.0715513932
sample21 -0.0119253690 -0.0376087020
sample22 0.0724459420 -0.0095635080
sample23 -0.0992529478 0.0134299175
sample24 -0.1595268813 0.0728684600
sample25 -0.0920660333 -0.0749749073
sample26 -0.0595567671 0.0848973811
sample27 0.0826579043 -0.0086747769
sample28 -0.0384834660 0.0440972831
sample29 0.0777742312 0.1735298314
sample30 0.1229474445 -0.0819018748
sample31 0.0579748473 -0.0238646948
sample32 0.0970365170 -0.0111435340
sample33 0.1017579988 -0.0630452805
sample34 0.0637902203 0.0377936084
sample35 0.0790003786 -0.0229732685
sample36 0.1224932995 -0.1274968655
sample37 0.1798848111 -0.1673448482
sample38 0.0466395760 0.0888153043
sample39 -0.0168694889 0.0421536092
sample40 0.1756418583 -0.1526662796
sample41 0.0042471472 0.0004924519
sample42 -0.0447824952 -0.0651501286
sample43 0.0482291689 -0.0253533636
sample44 -0.1986821674 -0.0545753281
sample45 -0.0741919849 0.0054714423
sample46 0.0478864032 -0.0007080528
sample47 0.0608217171 0.0481615290
sample48 -0.1381464244 0.0578301322
sample49 -0.0530633214 -0.1405523431
sample50 -0.0173643337 0.1602386105
sample51 0.0462454386 0.0303472962
sample52 0.0279994536 0.0280387815
sample53 0.0667496064 0.0237700041
sample54 0.0121811262 -0.0521354924
sample55 0.0182392136 0.0221326588
sample56 -0.0001310039 0.0030909312
sample57 0.0316572589 0.0530190602
sample58 0.0393890454 -0.0297801871
sample59 0.1278271004 -0.0546540896
sample60 0.1486964095 0.1069141490
sample61 0.0793066329 0.0569790235
sample62 0.1172822329 -0.0149211455
sample63 -0.0028814550 0.1300524187
sample64 0.0237294749 0.1073288367
sample65 -0.0126543843 0.0589810398
sample66 -0.0468234644 -0.0771066481
sample67 0.1494286437 -0.0769877788
sample68 0.0978024838 -0.0577364095
sample69 0.0403090552 0.0156038164
sample70 0.0221599376 0.0315436552
sample71 -0.0546327834 -0.0272394882
sample72 0.1107501340 -0.0537331652
sample73 0.0906756555 0.0579957677
sample74 0.0586512542 0.0121417343
sample75 0.0390513052 0.0349278111
sample76 -0.0022939039 -0.1676560115
sample77 -0.0232101469 -0.2067300897
sample78 -0.0929810960 -0.0434927700
sample79 -0.1619378390 -0.0378102167
sample80 0.0680393284 0.1424655747
sample81 -0.0530724302 -0.0358347574
sample82 0.0266851044 -0.0577449175
sample83 0.1517242218 -0.0448570465
sample84 -0.0570942684 -0.0273808338
sample85 0.1086271200 -0.1228130696
sample86 0.0833892316 -0.0442925026
sample87 0.0022041281 -0.0943908533
sample88 -0.0078277863 -0.1140504516
sample89 0.0611004822 -0.0094589510
sample90 0.0022941957 -0.0936254878
sample91 0.0433776840 0.3205971944
sample92 -0.1815215271 -0.0334666330
sample93 0.0267654518 0.0614425736
sample94 0.0181901953 0.0605088123
sample95 -0.0720313022 -0.0013040428
sample96 -0.0559671780 -0.0118787022
sample97 -0.0217420827 0.0195417274
sample98 0.0379199711 0.0588352664
sample99 -0.0792509934 -0.0151262139
sample100 0.0222099996 -0.0023322974
sample101 -0.0387081235 0.1224225227
sample102 -0.2094626236 -0.0516420367
sample103 0.0138559586 0.0301047561
sample104 -0.0807947181 -0.0162712231
sample105 -0.0520491761 -0.1229660238
sample106 -0.0192643607 -0.0185235107
sample107 0.0319014294 0.0405120515
sample108 -0.0140673828 0.0163422368
sample109 -0.1831855404 0.0613024069
sample110 -0.0292782455 -0.0199846418
sample111 -0.1423172096 0.0327352380
sample112 0.0426312843 -0.0029087144
sample113 -0.0771932742 0.0268742935
sample114 -0.0241566306 -0.0184080627
sample115 -0.1958954811 0.0460149017
sample116 -0.1394436521 -0.0530793207
sample117 -0.1672310551 -0.1386521609
sample118 -0.0448331310 -0.0117617895
sample119 -0.0910188496 0.2217435870
sample120 -0.0331405237 -0.0057270238
sample121 0.0307514970 0.1392506166
sample122 -0.0839839249 -0.0291983384
sample123 0.0239676017 -0.0642167473
sample124 -0.0909177077 0.0130430362
sample125 -0.0065362641 -0.1092630997
sample126 0.0935272145 0.1368276644
sample127 0.0035406197 0.0292755002
sample128 -0.0660351870 0.1018575988
sample129 0.0693672113 -0.0695430438
sample130 0.0008517854 -0.0669705400
sample131 0.0431011584 0.0174060909
sample132 -0.0637090474 0.0029383697
sample133 -0.0289463685 -0.0390817253
sample134 0.0446140161 0.0456332219
sample135 0.0712343955 0.0521627428
sample136 0.0596319935 0.0197291529
sample137 0.0793176270 -0.0380637493
sample138 -0.0973504241 -0.0454209917
sample139 0.0539864962 -0.1534332232
sample140 0.0850873477 0.0955804210
sample141 -0.0192725148 -0.0554446397
sample142 -0.0672295402 -0.0461312903
sample143 -0.0303706373 -0.0519258496
sample144 -0.0089350123 0.0145815384
sample145 -0.0638880304 0.0122268926
sample146 0.0585924710 0.0063074720
sample147 0.0894147285 -0.1124626024
sample148 -0.0216441911 -0.0615962322
sample149 -0.0515313099 -0.0839902805
sample150 0.0568227037 -0.0124472853
sample151 -0.0789513046 -0.0261823766
sample152 -0.0330691490 0.1306445048
sample153 -0.1752069717 0.1497755999
sample154 0.0421491862 -0.0037017257
sample155 0.0680199497 0.0095703333
sample156 0.0388951438 0.1057557843
sample157 0.0314764916 0.0561364578
sample158 0.0329630555 0.0353943532
sample159 -0.0398463023 -0.1007368169
sample160 0.0424904716 0.0108492962
sample161 -0.0888339215 -0.0679692637
sample162 -0.0027574011 0.1237848354
sample163 -0.0126233492 0.0725440991
sample164 -0.0566787413 -0.0458318152
sample165 -0.0315331381 -0.0236359513
sample166 -0.0612110908 -0.0425224649
sample167 0.0142729545 0.0179306960
sample168 -0.0169544263 -0.0769614808
sample169 0.0675062828 0.0131498913
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 -0.0012331732 1.635716e-01
sample2 -0.0724353325 6.022164e-03
sample3 -0.0188459923 1.080029e-01
sample4 0.0390143047 -3.106240e-04
sample5 0.1774810593 2.996430e-02
sample6 -0.0451446526 3.455900e-02
sample7 -0.0226463385 7.019174e-03
sample8 -0.1033684706 9.857994e-03
sample9 0.1350014301 -8.979115e-02
sample10 0.1259884321 5.097940e-02
sample11 0.0979791010 -7.086568e-02
sample12 -0.0863021042 8.620322e-02
sample13 -0.1381401861 -1.827998e-01
sample14 -0.0615074764 2.642808e-02
sample15 0.0381600651 3.101599e-02
sample16 -0.0048779477 -1.270992e-03
sample17 -0.0788483301 1.547608e-02
sample18 -0.0884189515 3.795477e-02
sample19 0.0703043485 1.084003e-01
sample20 -0.0025581208 -7.975973e-02
sample21 0.0941596466 4.126899e-02
sample22 -0.0550270768 7.806612e-02
sample23 0.0679492726 4.102080e-02
sample24 -0.1310969682 -1.649281e-01
sample25 0.0113583499 4.426901e-02
sample26 -0.1402949015 -2.016456e-02
sample27 0.0261566220 -1.590018e-03
sample28 -0.0724200858 -5.850507e-02
sample29 -0.0330054631 -2.062128e-03
sample30 -0.0228750246 2.015341e-02
sample31 -0.0635070486 6.670370e-02
sample32 0.0685100025 4.955245e-02
sample33 -0.0777764907 1.272070e-01
sample34 0.0157842081 3.024311e-02
sample35 -0.0529627763 -1.500981e-01
sample36 0.0070908231 -2.025321e-01
sample37 -0.0442411494 -1.802110e-01
sample38 -0.0781508271 3.676294e-02
sample39 0.0120330007 3.388885e-02
sample40 -0.0473283547 -1.471582e-01
sample41 0.0228192287 2.673454e-02
sample42 -0.0245361918 7.960878e-02
sample43 0.1036361992 8.229577e-02
sample44 -0.1012235003 -7.049233e-02
sample45 0.0013726507 2.451075e-02
sample46 -0.0558506326 -2.948627e-03
sample47 -0.0380478626 -4.554239e-02
sample48 0.0784340396 -4.888888e-02
sample49 -0.0605168218 1.162476e-02
sample50 0.0530083042 2.737809e-02
sample51 0.1514645315 -5.678257e-02
sample52 0.1860936038 -1.246711e-01
sample53 -0.0064179789 2.701062e-02
sample54 0.0697037537 2.308413e-02
sample55 0.1633577730 -1.366433e-02
sample56 0.1011483959 -4.682131e-02
sample57 0.1730374413 -1.609593e-01
sample58 -0.0071384892 1.666951e-02
sample59 -0.0030458356 -3.005379e-02
sample60 0.0215842441 -2.665888e-01
sample61 0.1510585398 -1.002384e-01
sample62 -0.0925531493 4.845724e-02
sample63 -0.0596315561 4.137112e-02
sample64 -0.0449227330 2.600973e-03
sample65 0.0939382198 4.406951e-02
sample66 0.1063397630 5.710081e-02
sample67 -0.0201580494 -2.361747e-01
sample68 0.0037208573 -2.418548e-02
sample69 -0.0645162018 1.155618e-01
sample70 -0.1013439733 1.351780e-01
sample71 -0.0016466048 2.976771e-02
sample72 0.0328895497 2.835768e-02
sample73 0.0275080411 5.148151e-02
sample74 0.1341718330 7.895304e-02
sample75 0.0951576682 3.943146e-02
sample76 -0.0864719881 -3.035055e-02
sample77 -0.1035749507 2.545324e-02
sample78 -0.1575648006 -4.939470e-02
sample79 0.0189138405 -4.874690e-02
sample80 0.1384142853 -4.317173e-05
sample81 -0.0118846675 6.357907e-02
sample82 -0.1675306585 -3.533970e-02
sample83 -0.0065671064 7.812493e-02
sample84 0.1486890623 3.109097e-02
sample85 -0.0532720200 -7.417992e-02
sample86 -0.1138474827 1.817052e-05
sample87 0.0432866014 -6.080500e-02
sample88 0.0433451219 -1.402486e-01
sample89 0.0331204775 1.395429e-02
sample90 -0.0607413499 8.610385e-02
sample91 -0.0566263446 -1.303771e-01
sample92 -0.0359580654 -1.061605e-01
sample93 -0.0433646455 4.443609e-02
sample94 -0.0477292152 1.059570e-01
sample95 -0.0249595934 3.980509e-02
sample96 0.0035217530 9.293931e-02
sample97 -0.0066052110 1.527234e-01
sample98 0.0020367078 5.579514e-02
sample99 -0.0886621923 3.728379e-02
sample100 -0.1091259616 3.560401e-02
sample101 -0.0739723745 4.317881e-02
sample102 0.0574455511 2.784092e-02
sample103 0.0142733843 -9.706381e-03
sample104 0.0710395587 -4.068330e-02
sample105 0.0980829891 3.452998e-02
sample106 -0.0254260535 -3.628931e-02
sample107 -0.0160655087 9.173398e-02
sample108 -0.0200988333 2.379699e-02
sample109 -0.0389781999 -1.692310e-02
sample110 -0.0326305266 -2.988086e-02
sample111 0.0676935874 6.038250e-02
sample112 0.0167883518 -5.336923e-03
sample113 0.0969213848 2.757706e-02
sample114 -0.0026397963 9.209100e-02
sample115 -0.0308049655 -1.603741e-02
sample116 -0.1240306383 -1.272998e-01
sample117 0.0334728621 -5.392660e-02
sample118 -0.1037152138 -6.252440e-02
sample119 -0.1064170393 -1.196218e-01
sample120 -0.0771357796 1.004935e-01
sample121 -0.0129352362 -3.181912e-02
sample122 0.0847487398 5.568468e-02
sample123 -0.0041335475 -7.693564e-03
sample124 -0.0583462348 8.396479e-02
sample125 0.0634843208 5.232569e-02
sample126 -0.0662582125 1.091730e-01
sample127 -0.0865025646 1.094172e-01
sample128 -0.0627822184 1.471098e-02
sample129 -0.0336274522 4.007772e-02
sample130 -0.0293518125 8.046086e-02
sample131 -0.0469196761 2.209372e-03
sample132 -0.0241745798 1.248608e-01
sample133 0.0907303816 -1.466698e-02
sample134 -0.0350841202 -7.539660e-02
sample135 0.0001334936 -9.185833e-03
sample136 -0.0335874758 9.860178e-02
sample137 -0.0640147210 7.554368e-02
sample138 0.0060964014 1.742783e-02
sample139 -0.0592082705 -5.615007e-02
sample140 0.0427988700 1.099463e-02
sample141 0.0618793171 9.301104e-02
sample142 0.0898552439 -3.573321e-02
sample143 0.0817391120 -8.880528e-02
sample144 0.0787754465 3.821395e-02
sample145 0.1085819435 -1.569460e-01
sample146 -0.0589554901 4.373233e-02
sample147 -0.0495327823 -7.278086e-03
sample148 0.1161590409 -9.078105e-03
sample149 -0.0121575383 -7.788465e-02
sample150 -0.0314511956 -3.520220e-02
sample151 0.0575380900 1.945394e-02
sample152 -0.0494540304 -7.025567e-02
sample153 -0.0941338741 -2.153269e-01
sample154 -0.0335928724 -2.078828e-02
sample155 0.0690459096 2.780360e-02
sample156 0.1039902331 6.292487e-02
sample157 -0.0408645840 -8.065530e-03
sample158 0.1018106379 -7.817027e-03
sample159 -0.0281732597 1.207262e-02
sample160 0.1643052863 -2.977802e-03
sample161 0.0374330100 -8.524588e-02
sample162 -0.0804538361 -8.349631e-02
sample163 -0.0743232538 1.406350e-02
sample164 0.1208804221 2.139526e-02
sample165 0.1608115953 -2.025158e-02
sample166 -0.0425948043 2.660804e-02
sample167 -0.0226849506 4.464257e-02
sample168 -0.0180737426 7.471739e-04
sample169 0.0190780252 -2.645427e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
[,1] [,2]
sample1 -0.0572060227 -1.729087e-02
sample2 0.0875245208 1.112588e-02
sample3 0.0403482602 -3.168994e-02
sample4 -0.0218345996 4.052760e-06
sample5 -0.0150905011 4.795041e-03
sample6 -0.0924362933 4.511003e-02
sample7 -0.0793066751 -1.243823e-02
sample8 -0.1342997187 6.215220e-02
sample9 -0.0338886944 -1.854401e-02
sample10 0.0020547173 1.749421e-02
sample11 0.0037275602 -2.364116e-02
sample12 -0.0753094533 2.772698e-02
sample13 0.0856160091 3.679963e-02
sample14 -0.0737457307 2.668452e-02
sample15 -0.0062111746 -3.554864e-03
sample16 -0.0602355268 6.675115e-02
sample17 0.1086768843 2.524534e-02
sample18 0.0702999472 2.231671e-02
sample19 0.0173785882 -3.024846e-02
sample20 0.0484173812 -3.310904e-02
sample21 0.0124657042 6.517144e-02
sample22 -0.0140989936 -3.159137e-02
sample23 -0.0627028403 -5.393710e-04
sample24 0.0919972100 7.909297e-02
sample25 0.0326998483 -1.945206e-02
sample26 0.1064741246 2.120849e-02
sample27 0.0166058995 -4.964993e-02
sample28 0.0743504770 2.614211e-02
sample29 -0.0511008491 -2.782647e-02
sample30 0.0962250842 -3.974893e-03
sample31 -0.0869563008 5.250819e-02
sample32 0.0271858919 1.552005e-02
sample33 -0.0448364581 6.243160e-03
sample34 0.0718415218 1.469396e-02
sample35 0.0403086451 -1.632629e-02
sample36 -0.1036402827 -1.304320e-02
sample37 -0.0159385744 -3.036525e-02
sample38 0.0182198369 -4.034805e-02
sample39 0.0690363619 8.058350e-03
sample40 -0.0467312750 -2.810325e-02
sample41 0.0263674438 -5.171216e-02
sample42 0.0374578960 -1.268634e-02
sample43 0.0132336869 9.536642e-03
sample44 -0.1119154428 5.028683e-02
sample45 0.0759639367 4.587903e-02
sample46 0.0871885519 -4.670385e-02
sample47 0.0721490571 -1.288540e-02
sample48 0.0005086144 -1.290565e-02
sample49 -0.0858177028 5.173760e-02
sample50 0.0118992665 -7.276215e-02
sample51 -0.0426446855 5.306205e-02
sample52 -0.0381605826 3.086785e-02
sample53 -0.0855757630 6.730043e-02
sample54 0.0261723092 9.184260e-03
sample55 -0.0156418304 4.682404e-04
sample56 0.0307831193 2.597550e-02
sample57 -0.0157242103 4.829381e-02
sample58 -0.0031174404 1.359898e-02
sample59 -0.0373001859 5.868397e-03
sample60 -0.0142609099 5.831654e-03
sample61 -0.0122255144 2.663579e-02
sample62 0.0228002942 -8.692265e-03
sample63 -0.0833127581 5.473229e-02
sample64 -0.1166548159 4.196500e-02
sample65 0.0038808902 8.568590e-03
sample66 0.0011561811 1.766612e-02
sample67 -0.1129311062 -2.608702e-02
sample68 -0.0382526429 -3.804045e-02
sample69 -0.0476502440 4.003241e-03
sample70 -0.0110329882 -2.752719e-02
sample71 0.0096850282 -5.627056e-02
sample72 0.0487124704 -8.800131e-03
sample73 0.0773058132 8.239864e-03
sample74 -0.0102488176 2.454957e-02
sample75 -0.0286613976 -8.387293e-03
sample76 -0.0472655595 -2.129315e-02
sample77 -0.0865043074 -7.296820e-03
sample78 0.1070293698 2.818346e-02
sample79 -0.0165060681 -6.659721e-02
sample80 -0.0206765949 -8.712112e-03
sample81 -0.0050943615 -3.079175e-02
sample82 0.1153622361 -1.647054e-02
sample83 0.0367979217 -2.538114e-03
sample84 0.0199463070 -1.468961e-02
sample85 -0.0827122185 -2.709824e-04
sample86 0.0969487314 -1.699897e-02
sample87 0.0421957457 -1.965953e-02
sample88 0.0215934743 1.566050e-02
sample89 0.0751559502 2.811652e-02
sample90 -0.0057328000 -8.283795e-03
sample91 -0.1134005268 -8.603522e-02
sample92 -0.0101689918 -6.894992e-02
sample93 0.0725967502 -6.003176e-03
sample94 -0.0096878852 -4.693081e-03
sample95 -0.0223502239 -3.139636e-02
sample96 -0.0013232863 -1.963604e-02
sample97 -0.0476541710 1.183660e-02
sample98 0.0269546160 -5.978398e-03
sample99 0.0728179461 4.597884e-02
sample100 -0.0413398038 1.079347e-02
sample101 0.0087536994 -6.796076e-02
sample102 0.0032509529 3.932612e-03
sample103 0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105 0.0294940465 -7.140722e-03
sample106 0.0686472054 1.462895e-02
sample107 0.0748635927 8.401339e-03
sample108 0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110 0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112 0.0570870472 1.066018e-02
sample113 -0.0200110554 1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586 1.089843e-02
sample121 0.0542443861 3.861344e-02
sample122 0.0178575357 3.027138e-02
sample123 0.0775020581 -1.636852e-02
sample124 -0.0460701050 1.814758e-02
sample125 0.0543846585 2.075898e-03
sample126 -0.0729417144 3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128 0.0908136899 3.758801e-02
sample129 0.0552445878 -1.879062e-02
sample130 0.0007128089 -1.294308e-02
sample131 -0.0693311345 7.357082e-03
sample132 -0.0556565156 3.126995e-02
sample133 0.0375870104 -1.977240e-02
sample134 -0.1229130924 3.159495e-02
sample135 0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201 1.040683e-02
sample140 0.0452288969 -1.876279e-02
sample141 -0.0189142561 2.247042e-02
sample142 0.0297545566 1.280524e-02
sample143 0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491 5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147 0.0726083535 -1.239968e-02
sample148 -0.0284795794 3.389732e-02
sample149 0.0082261455 -6.399305e-02
sample150 -0.0765013197 2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152 0.0403422737 -2.714879e-02
sample153 0.0629117719 7.425085e-02
sample154 0.0551622927 -3.548984e-02
sample155 0.0654439133 -1.005306e-02
sample156 0.0209310714 -1.390213e-02
sample157 0.0851522597 6.577150e-03
sample158 0.0208354599 -4.663078e-03
sample159 -0.0498794349 1.913257e-02
sample160 0.0216074437 1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162 0.0963663017 5.705881e-02
sample163 -0.1009542191 7.174224e-02
sample164 0.0109881996 1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357 2.673084e-02
sample167 -0.0825048036 2.278863e-03
sample168 0.0486147429 1.793843e-02
sample169 0.0302506727 8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
[,1] [,2]
sample1 -0.0621842115 -1.364509e-02
sample2 0.0944623785 9.720892e-03
sample3 0.0406196267 -2.236338e-02
sample4 -0.0229316496 -3.932487e-04
sample5 -0.0157330047 3.231033e-03
sample6 -0.0945794025 3.120720e-02
sample7 -0.0854427118 -1.052880e-02
sample8 -0.1376625920 4.286608e-02
sample9 -0.0377115311 -1.415134e-02
sample10 0.0035244506 1.280825e-02
sample11 0.0016639987 -1.717895e-02
sample12 -0.0781403168 1.884368e-02
sample13 0.0938400516 2.838858e-02
sample14 -0.0759839772 1.810989e-02
sample15 -0.0068340837 -2.705361e-03
sample16 -0.0590150849 4.757848e-02
sample17 0.1178805097 2.040526e-02
sample18 0.0767858320 1.756604e-02
sample19 0.0157112113 -2.172867e-02
sample20 0.0485318300 -2.327033e-02
sample21 0.0185928176 4.777095e-02
sample22 -0.0191358702 -2.329775e-02
sample23 -0.0672994194 -1.535656e-03
sample24 0.1047476642 5.935707e-02
sample25 0.0329844953 -1.358036e-02
sample26 0.1154952052 1.741529e-02
sample27 0.0133849853 -3.590922e-02
sample28 0.0821554039 2.042376e-02
sample29 -0.0567643690 -2.123848e-02
sample30 0.1016073931 -1.134728e-03
sample31 -0.0880396372 3.670548e-02
sample32 0.0300363338 1.182406e-02
sample33 -0.0467252272 3.739254e-03
sample34 0.0783666394 1.203777e-02
sample35 0.0424227097 -1.118559e-02
sample36 -0.1107646166 -1.143464e-02
sample37 -0.0191667664 -2.246060e-02
sample38 0.0155968095 -2.909621e-02
sample39 0.0746847148 7.148218e-03
sample40 -0.0517028178 -2.137267e-02
sample41 0.0234979494 -3.723018e-02
sample42 0.0388797356 -8.557228e-03
sample43 0.0149555568 7.210002e-03
sample44 -0.1150305613 3.461805e-02
sample45 0.0846146236 3.486020e-02
sample46 0.0884426404 -3.246853e-02
sample47 0.0748644971 -8.083045e-03
sample48 -0.0012033198 -9.403647e-03
sample49 -0.0872662737 3.616245e-02
sample50 0.0066941314 -5.284863e-02
sample51 -0.0411777630 3.791830e-02
sample52 -0.0379355780 2.180834e-02
sample53 -0.0851639886 4.751761e-02
sample54 0.0288006248 7.184424e-03
sample55 -0.0164920835 5.919925e-05
sample56 0.0355115616 1.951043e-02
sample57 -0.0141146068 3.492409e-02
sample58 -0.0015636132 9.862883e-03
sample59 -0.0390656483 3.590929e-03
sample60 -0.0139454780 3.963030e-03
sample61 -0.0106410274 1.919705e-02
sample62 0.0236748439 -5.922677e-03
sample63 -0.0846790877 3.839102e-02
sample64 -0.1202581015 2.846469e-02
sample65 0.0050548584 6.328644e-03
sample66 0.0028013072 1.291807e-02
sample67 -0.1231623009 -2.112565e-02
sample68 -0.0437782161 -2.845072e-02
sample69 -0.0501199692 2.053469e-03
sample70 -0.0140278645 -2.027157e-02
sample71 0.0057489505 -4.085977e-02
sample72 0.0511212704 -5.522408e-03
sample73 0.0828141409 7.431582e-03
sample74 -0.0085959456 1.772951e-02
sample75 -0.0312180394 -6.636869e-03
sample76 -0.0519051781 -1.640191e-02
sample77 -0.0925924762 -6.907800e-03
sample78 0.1163971046 2.251122e-02
sample79 -0.0240906926 -4.887766e-02
sample80 -0.0221327065 -6.730703e-03
sample81 -0.0072114968 -2.254399e-02
sample82 0.1204416674 -9.907422e-03
sample83 0.0386739485 -1.171663e-03
sample84 0.0195988488 -1.033806e-02
sample85 -0.0877680171 -1.725057e-03
sample86 0.1023541048 -1.062501e-02
sample87 0.0425213089 -1.356865e-02
sample88 0.0244788514 1.180820e-02
sample89 0.0804276691 2.188588e-02
sample90 -0.0074639871 -6.140721e-03
sample91 -0.1278832404 -6.485140e-02
sample92 -0.0162199697 -5.048358e-02
sample93 0.0769344893 -3.045135e-03
sample94 -0.0104345587 -3.593172e-03
sample95 -0.0260058453 -2.330475e-02
sample96 -0.0025018700 -1.433516e-02
sample97 -0.0492358305 7.774183e-03
sample98 0.0279220220 -3.862141e-03
sample99 0.0813921923 3.487339e-02
sample100 -0.0428797405 7.112807e-03
sample101 0.0032855240 -4.940743e-02
sample102 0.0038439317 2.938008e-03
sample103 0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105 0.0314853405 -4.656633e-03
sample106 0.0726456731 1.192390e-02
sample107 0.0807342975 7.508627e-03
sample108 0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110 0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112 0.0599954082 8.820317e-03
sample113 -0.0195006577 1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131 6.937326e-03
sample121 0.0613899126 2.915307e-02
sample122 0.0218424338 2.241775e-02
sample123 0.0809008460 -1.051759e-02
sample124 -0.0472109313 1.239887e-02
sample125 0.0583180947 2.521167e-03
sample126 -0.0753941872 2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128 0.1000212216 2.908091e-02
sample129 0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877 4.091672e-03
sample132 -0.0566219024 2.179861e-02
sample133 0.0384172955 -1.372840e-02
sample134 -0.1280862736 2.077912e-02
sample135 0.0592633273 6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861 6.729304e-03
sample140 0.0468926306 -1.285498e-02
sample141 -0.0186248693 1.605439e-02
sample142 0.0328031246 9.887746e-03
sample143 0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310 3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147 0.0765025670 -7.714769e-03
sample148 -0.0276016641 2.420589e-02
sample149 0.0027545308 -4.653007e-02
sample150 -0.0792296010 1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152 0.0409796547 -1.907063e-02
sample153 0.0734301757 5.528780e-02
sample154 0.0557740684 -2.487723e-02
sample155 0.0689436560 -6.127635e-03
sample156 0.0212272938 -9.747423e-03
sample157 0.0911931194 6.355708e-03
sample158 0.0220840645 -3.016357e-03
sample159 -0.0513244242 1.304175e-02
sample160 0.0246213576 1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162 0.1078802043 4.337260e-02
sample163 -0.1017965082 5.047171e-02
sample164 0.0119430799 9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180 1.899222e-02
sample167 -0.0872832229 1.516582e-04
sample168 0.0540714512 1.397701e-02
sample169 0.0328432652 7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
[,1] [,2]
sample1 0.0133684846 2.195848e-02
sample2 0.0254157197 -1.058416e-02
sample3 -0.0049551479 -4.840017e-03
sample4 0.0310390570 -1.063929e-02
sample5 0.0046941318 -6.488426e-03
sample6 -0.0107406753 -1.026702e-02
sample7 -0.0225157631 2.624712e-04
sample8 0.0141320952 -9.505821e-03
sample9 0.0029681280 2.078210e-02
sample10 0.0131729174 -2.275042e-03
sample11 -0.0004164298 1.994019e-02
sample12 -0.0095211620 3.759883e-02
sample13 0.0091018604 -7.953956e-03
sample14 -0.0106557524 -9.181659e-03
sample15 -0.0249924121 3.262724e-02
sample16 -0.0156216400 1.375700e-02
sample17 -0.0019382446 1.073994e-03
sample18 -0.0221072481 -8.703592e-03
sample19 0.0146917619 -1.311712e-02
sample20 -0.0160353760 1.826290e-02
sample21 0.0035947899 -9.616341e-03
sample22 -0.0225060762 -2.532589e-03
sample23 0.0310000683 3.033060e-03
sample24 0.0499544372 1.809450e-02
sample25 0.0284442301 -1.932558e-02
sample26 0.0188220043 2.146985e-02
sample27 -0.0257763219 -1.999228e-03
sample28 0.0120888648 1.125834e-02
sample29 -0.0236482520 4.426726e-02
sample30 -0.0385486305 -2.055935e-02
sample31 -0.0181539336 -5.877838e-03
sample32 -0.0302630460 -2.607192e-03
sample33 -0.0319565715 -1.562628e-02
sample34 -0.0197970124 9.906813e-03
sample35 -0.0247412713 -5.434440e-03
sample36 -0.0386259060 -3.190394e-02
sample37 -0.0566199273 -4.192574e-02
sample38 -0.0142060273 2.259644e-02
sample39 0.0053589035 1.076485e-02
sample40 -0.0552546493 -3.819896e-02
sample41 -0.0013089975 9.278818e-05
sample42 0.0137252142 -1.664652e-02
sample43 -0.0151259626 -6.290953e-03
sample44 0.0617391754 -1.442883e-02
sample45 0.0231410886 1.163143e-03
sample46 -0.0148898209 -1.384176e-04
sample47 -0.0187252536 1.221690e-02
sample48 0.0432839432 1.416671e-02
sample49 0.0160818605 -3.588745e-02
sample50 0.0059333545 4.067003e-02
sample51 -0.0142914866 7.776270e-03
sample52 -0.0086339952 7.208917e-03
sample53 -0.0207386980 6.272432e-03
sample54 -0.0039856719 -1.316934e-02
sample55 -0.0056217017 5.692315e-03
sample56 0.0000123292 8.978290e-04
sample57 -0.0095805555 1.324253e-02
sample58 -0.0124160295 -7.326376e-03
sample59 -0.0400195442 -1.349736e-02
sample60 -0.0460063358 2.770091e-02
sample61 -0.0245266456 1.470710e-02
sample62 -0.0366022783 -3.437352e-03
sample63 0.0013742171 3.288796e-02
sample64 -0.0070599859 2.739588e-02
sample65 0.0041201911 1.498268e-02
sample66 0.0143173351 -1.968812e-02
sample67 -0.0467477531 -1.929938e-02
sample68 -0.0306751978 -1.436184e-02
sample69 -0.0125317217 4.130407e-03
sample70 -0.0068071487 8.080857e-03
sample71 0.0169170264 -7.027348e-03
sample72 -0.0346909749 -1.333770e-02
sample73 -0.0280506153 1.493843e-02
sample74 -0.0182611498 3.294697e-03
sample75 -0.0120563964 8.974612e-03
sample76 0.0001437236 -4.253184e-02
sample77 0.0065330299 -5.252886e-02
sample78 0.0288278141 -1.127782e-02
sample79 0.0503961481 -1.023318e-02
sample80 -0.0207693429 3.648391e-02
sample81 0.0163562768 -9.074596e-03
sample82 -0.0084317129 -1.478976e-02
sample83 -0.0474097918 -1.103126e-02
sample84 0.0177181395 -7.191197e-03
sample85 -0.0342718548 -3.082360e-02
sample86 -0.0261671791 -1.089491e-02
sample87 -0.0009486358 -2.411514e-02
sample88 0.0020528931 -2.894615e-02
sample89 -0.0189361111 -2.638639e-03
sample90 -0.0009863658 -2.390075e-02
sample91 -0.0124352695 8.153234e-02
sample92 0.0564264106 -8.909537e-03
sample93 -0.0081461774 1.570851e-02
sample94 -0.0054896581 1.547251e-02
sample95 0.0224073150 -4.374348e-04
sample96 0.0173528924 -3.050441e-03
sample97 0.0067948115 5.008237e-03
sample98 -0.0116030825 1.498764e-02
sample99 0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101 0.0124923691 3.091503e-02
sample102 0.0650835386 -1.367400e-02
sample103 -0.0042741828 7.855985e-03
sample104 0.0250591040 -4.171938e-03
sample105 0.0157516368 -3.121990e-02
sample106 0.0060593853 -5.101693e-03
sample107 -0.0098329626 1.044506e-02
sample108 0.0044269853 4.142036e-03
sample109 0.0572473486 1.517542e-02
sample110 0.0090474827 -5.119868e-03
sample111 0.0444263015 7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113 0.0241047399 6.706740e-03
sample114 0.0074558775 -4.728652e-03
sample115 0.0611851433 1.117210e-02
sample116 0.0432646951 -1.380556e-02
sample117 0.0516750066 -3.575617e-02
sample118 0.0139942100 -3.279138e-03
sample119 0.0291722987 5.587946e-02
sample120 0.0103515853 -1.690016e-03
sample121 -0.0091396331 3.552116e-02
sample122 0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124 0.0283466326 3.127845e-03
sample125 0.0016472378 -2.770692e-02
sample126 -0.0286529417 3.489336e-02
sample127 -0.0010224500 7.483214e-03
sample128 0.0209049296 2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968 4.637390e-03
sample132 0.0198526786 5.723983e-04
sample133 0.0088812957 -9.988115e-03
sample134 -0.0137484514 1.172591e-02
sample135 -0.0220314568 1.347465e-02
sample136 -0.0185173353 5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138 0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762 2.456863e-02
sample141 0.0058369763 -1.420854e-02
sample142 0.0207886071 -1.188764e-02
sample143 0.0092832598 -1.324238e-02
sample144 0.0028442140 3.627979e-03
sample145 0.0199749569 2.862202e-03
sample146 -0.0182236697 1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148 0.0065435868 -1.572917e-02
sample149 0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151 0.0245166984 -6.888241e-03
sample152 0.0107259913 3.314630e-02
sample153 0.0550963965 3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574 2.642246e-03
sample156 -0.0117803505 2.698265e-02
sample157 -0.0096167165 1.433840e-02
sample158 -0.0101754772 9.137620e-03
sample159 0.0120662931 -2.565236e-02
sample160 -0.0132238202 2.916023e-03
sample161 0.0274491966 -1.748284e-02
sample162 0.0012482909 3.152261e-02
sample163 0.0042031315 1.830701e-02
sample164 0.0174896157 -1.175915e-02
sample165 0.0097517662 -6.119019e-03
sample166 0.0190134679 -1.121582e-02
sample167 -0.0044140836 4.665585e-03
sample168 0.0049689168 -1.941822e-02
sample169 -0.0209802098 3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
[,1] [,2]
sample1 -0.0515543627 -0.0305856787
sample2 -0.0144993256 0.0236342950
sample3 -0.0371833108 -0.0140263348
sample4 0.0068945388 -0.0132539692
sample5 0.0215035333 -0.0663338101
sample6 -0.0187055152 0.0088773016
sample7 -0.0061521552 0.0064029054
sample8 -0.0210874459 0.0334652901
sample9 0.0516865043 -0.0291142799
sample10 0.0059440366 -0.0527217447
sample11 0.0393010793 -0.0200624712
sample12 -0.0420837100 0.0131331362
sample13 0.0333252565 0.0818552509
sample14 -0.0190062644 0.0160202175
sample15 -0.0030968049 -0.0189230681
sample16 -0.0004452158 0.0018880102
sample17 -0.0185848615 0.0240170131
sample18 -0.0273093598 0.0230213640
sample19 -0.0217761111 -0.0445894441
sample20 0.0245820821 0.0159812738
sample21 0.0034527644 -0.0400016054
sample22 -0.0340789054 0.0039289109
sample23 -0.0010344929 -0.0310161212
sample24 0.0289468503 0.0760962436
sample25 -0.0119098496 -0.0122798760
sample26 -0.0181001057 0.0517892852
sample27 0.0050465417 -0.0086515844
sample28 0.0057491502 0.0358830107
sample29 -0.0051104246 0.0116605117
sample30 -0.0103085904 0.0039678538
sample31 -0.0319929858 0.0090606113
sample32 -0.0036232521 -0.0328202010
sample33 -0.0534742153 0.0024751837
sample34 -0.0067495749 -0.0111000311
sample35 0.0378745721 0.0465929296
sample36 0.0647886800 0.0359987924
sample37 0.0488441236 0.0492906912
sample38 -0.0251514062 0.0197110110
sample39 -0.0085428066 -0.0105117852
sample40 0.0379324087 0.0440810741
sample41 -0.0044199152 -0.0128820644
sample42 -0.0292553573 -0.0067045265
sample43 -0.0077829155 -0.0510178219
sample44 0.0045122248 0.0479660309
sample45 -0.0074444298 -0.0051116726
sample46 -0.0088025512 0.0196186661
sample47 0.0076696301 0.0215947965
sample48 0.0290108585 -0.0175568376
sample49 -0.0141754858 0.0184717099
sample50 0.0006282201 -0.0233054373
sample51 0.0441995177 -0.0410022921
sample52 0.0715329391 -0.0399499475
sample53 -0.0095954087 -0.0029140909
sample54 0.0048933768 -0.0281884386
sample55 0.0327325487 -0.0532290012
sample56 0.0323068984 -0.0256595538
sample57 0.0806603122 -0.0286748097
sample58 -0.0064792049 -0.0006945349
sample59 0.0088958941 0.0067389649
sample60 0.0874124612 0.0431964341
sample61 0.0577604571 -0.0326112099
sample62 -0.0313318464 0.0224391756
sample63 -0.0233625220 0.0125110562
sample64 -0.0086426068 0.0148770341
sample65 0.0025256193 -0.0404466327
sample66 0.0006014071 -0.0471576264
sample67 0.0706087042 0.0516228406
sample68 0.0082301011 0.0033109509
sample69 -0.0475076743 0.0001452708
sample70 -0.0600773716 0.0089986962
sample71 -0.0096321627 -0.0050761187
sample72 -0.0031773546 -0.0166221542
sample73 -0.0113700517 -0.0191726684
sample74 -0.0014179662 -0.0608101325
sample75 0.0041911740 -0.0399981269
sample76 -0.0055326449 0.0353114263
sample77 -0.0260214459 0.0305731380
sample78 -0.0119267436 0.0632236007
sample79 0.0186017239 0.0027402910
sample80 0.0241047889 -0.0472697181
sample81 -0.0220288317 -0.0079577210
sample82 -0.0180751258 0.0639051029
sample83 -0.0256671713 -0.0125898269
sample84 0.0161392598 -0.0567222449
sample85 0.0139988188 0.0322763454
sample86 -0.0198382995 0.0389225776
sample87 0.0266270281 -0.0032979996
sample88 0.0515677078 0.0117902495
sample89 0.0014022125 -0.0140510488
sample90 -0.0375949749 0.0044004551
sample91 0.0310397965 0.0440610926
sample92 0.0270570567 0.0324380452
sample93 -0.0215009202 0.0063993941
sample94 -0.0415702912 -0.0037692077
sample95 -0.0168416047 0.0010019120
sample96 -0.0285582661 -0.0187991000
sample97 -0.0490843868 -0.0266760748
sample98 -0.0171579033 -0.0112897471
sample99 -0.0271316525 0.0232395583
sample100 -0.0301789816 0.0305498693
sample101 -0.0264371151 0.0170723968
sample102 0.0012767734 -0.0248949597
sample103 0.0055214687 -0.0030040587
sample104 0.0251346074 -0.0165212671
sample105 0.0062424215 -0.0400309901
sample106 0.0069768684 0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679 0.0023637162
sample109 -0.0014762845 0.0165583675
sample110 0.0036971063 0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112 0.0046098120 -0.0048009350
sample113 0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453 0.0135805368
sample116 0.0183575759 0.0665377581
sample117 0.0227640036 -0.0012287760
sample118 0.0015695248 0.0472617382
sample119 0.0190084932 0.0590034062
sample120 -0.0449645755 0.0072755697
sample121 0.0077307184 0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123 0.0016959300 0.0028593594
sample124 -0.0365091615 0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408 0.0019165544
sample127 -0.0494064872 0.0088209044
sample128 -0.0155454766 0.0186819802
sample129 -0.0184340400 0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422 0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133 0.0204029276 -0.0282209049
sample134 0.0175513332 0.0262883962
sample135 0.0029009925 0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323 0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139 0.0073103935 0.0308956174
sample140 0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142 0.0268670676 -0.0239229634
sample143 0.0421049126 -0.0110888235
sample144 0.0017253664 -0.0341766012
sample145 0.0681741320 -0.0073526377
sample146 -0.0239965222 0.0118396767
sample147 -0.0063453522 0.0183130585
sample148 0.0230825251 -0.0379753037
sample149 0.0223298673 0.0188909118
sample150 0.0055709108 0.0174179009
sample151 0.0039177786 -0.0233533275
sample152 0.0134325667 0.0302344591
sample153 0.0511990309 0.0730230140
sample154 0.0006698324 0.0154177486
sample155 0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599 0.0154934573
sample158 0.0201775524 -0.0332982124
sample159 -0.0086909001 0.0073496711
sample160 0.0295437331 -0.0555734536
sample161 0.0332754288 0.0033779619
sample162 0.0121954537 0.0433540412
sample163 -0.0173490933 0.0227219128
sample164 0.0143374783 -0.0453542590
sample165 0.0343612593 -0.0511194536
sample166 -0.0157536004 0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919 0.0060747155
sample169 0.0116231468 -0.0015112800
>
> ## 3.3 Plotting VAF
>
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
>
> # JIVE plotVAF
> plotVAF(jiveRes)
>
>
> #########################
> ## PART 4. Plot Results
>
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
>
> # Scores for common part. JIVE
> plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
>
> # Scores for common part. O2PLS.
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for common part. O2PLS.
> plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common",
+ combined=TRUE,block=NULL,color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
>
> # Scores for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for distinctive part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual",
+ combined=TRUE,block=NULL,color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
> # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block)
> p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for common and distinctive part. DISCO (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Loadings for common part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> # Loadings for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> # Combined plot for loadings from common and distinctive part (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
>
> ## Plot scores and loadings togheter: Common components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> ## Plot scores and loadings togheter: Common components O2PLS
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> ## Plot scores and loadings togheter: Distintive components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
>
>
> proc.time()
user system elapsed
14.01 0.46 14.62
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STATegRa.Rcheck/examples_i386/STATegRa-Ex.timings
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STATegRa.Rcheck/examples_x64/STATegRa-Ex.timings
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