David Gomez-Cabrero
| Snapshot Date: 2019-10-15 17:01:26 -0400 (Tue, 15 Oct 2019) |
| URL: https://git.bioconductor.org/packages/STATegRa |
| Branch: RELEASE_3_9 |
| Last Commit: 1457952 |
| Last Changed Date: 2019-05-02 11:53:50 -0400 (Thu, 02 May 2019) |
| Back to Multiple platform build/check report for BioC 3.9 |
|
This page was generated on 2019-10-16 12:31:36 -0400 (Wed, 16 Oct 2019).
| Package 1596/1741 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||
| STATegRa 1.20.0 David Gomez-Cabrero
| malbec2 | Linux (Ubuntu 18.04.2 LTS) / x86_64 | OK | OK | OK | | ||||||
| tokay2 | Windows Server 2012 R2 Standard / x64 | OK | OK | [ OK ] | OK | | ||||||
| celaya2 | OS X 10.11.6 El Capitan / x86_64 | OK | OK | OK | OK | |
| Package: STATegRa |
| Version: 1.20.0 |
| Command: C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=C:\Users\biocbuild\bbs-3.9-bioc\R\library --no-vignettes --timings STATegRa_1.20.0.tar.gz |
| StartedAt: 2019-10-16 07:23:37 -0400 (Wed, 16 Oct 2019) |
| EndedAt: 2019-10-16 07:30:43 -0400 (Wed, 16 Oct 2019) |
| EllapsedTime: 425.3 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: STATegRa.Rcheck |
| Warnings: 0 |
##############################################################################
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###
### Running command:
###
### C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:STATegRa.install-out.txt --library=C:\Users\biocbuild\bbs-3.9-bioc\R\library --no-vignettes --timings STATegRa_1.20.0.tar.gz
###
##############################################################################
##############################################################################
* using log directory 'C:/Users/biocbuild/bbs-3.9-bioc/meat/STATegRa.Rcheck'
* using R version 3.6.1 (2019-07-05)
* 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 '1.20.0'
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking 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 or elapsed time > 5s
user system elapsed
plotRes 7.11 0.14 7.25
omicsCompAnalysis 6.27 0.24 6.50
plotVAF 5.74 0.00 5.74
** running examples for arch 'x64' ... OK
Examples with CPU or elapsed time > 5s
user system elapsed
plotRes 5.39 0.05 5.53
* 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-3.9-bioc/meat/STATegRa.Rcheck/00check.log'
for details.
STATegRa.Rcheck/00install.out
##############################################################################
##############################################################################
###
### Running command:
###
### C:\cygwin\bin\curl.exe -O https://malbec2.bioconductor.org/BBS/3.9/bioc/src/contrib/STATegRa_1.20.0.tar.gz && rm -rf STATegRa.buildbin-libdir && mkdir STATegRa.buildbin-libdir && C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=STATegRa.buildbin-libdir STATegRa_1.20.0.tar.gz && C:\Users\biocbuild\bbs-3.9-bioc\R\bin\R.exe CMD INSTALL STATegRa_1.20.0.zip && rm STATegRa_1.20.0.tar.gz STATegRa_1.20.0.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 3177k 100 3177k 0 0 34.3M 0 --:--:-- --:--:-- --:--:-- 37.3M
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_1.20.0.zip
* DONE (STATegRa)
* installing to library 'C:/Users/biocbuild/bbs-3.9-bioc/R/library'
package 'STATegRa' successfully unpacked and MD5 sums checked
|
STATegRa.Rcheck/tests_i386/runTests.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 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 -- Wed Oct 16 07:28:14 2019
***********************************************
Number of test functions: 4
Number of errors: 0
Number of failures: 0
1 Test Suite :
STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures
Number of test functions: 4
Number of errors: 0
Number of failures: 0
Warning messages:
1: In rownames(pData) == colnames(exprs) :
longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
>
> proc.time()
user system elapsed
3.98 0.25 7.51
|
STATegRa.Rcheck/tests_x64/runTests.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-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 -- Wed Oct 16 07:30:27 2019
***********************************************
Number of test functions: 4
Number of errors: 0
Number of failures: 0
1 Test Suite :
STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures
Number of test functions: 4
Number of errors: 0
Number of failures: 0
Warning messages:
1: In rownames(pData) == colnames(exprs) :
longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num", :
Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
>
> proc.time()
user system elapsed
3.26 0.21 3.46
|
|
STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsCLUST.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: 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, 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
>
> #############################################
> ## 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
32.12 0.98 33.12
|
STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsCLUST.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-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, 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
22.90 0.73 23.62
|
|
STATegRa.Rcheck/tests_i386/STATegRa_Example.omicsNPC.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: 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
79.93 0.25 80.23
|
STATegRa.Rcheck/tests_x64/STATegRa_Example.omicsNPC.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-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
87.26 0.32 87.59
|
|
STATegRa.Rcheck/tests_i386/STATEgRa_Example.omicsPCA.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: 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 0.0781574338 -0.0431502524
sample2 -0.1192218429 0.0294087989
sample3 -0.0531412077 -0.0746839818
sample4 0.0292975121 -0.0005960583
sample5 0.0202091749 0.0110463662
sample6 0.1226089054 0.1053467305
sample7 0.1078928140 -0.0322475302
sample8 0.1782895267 0.1449363722
sample9 0.0468698109 -0.0455174280
sample10 -0.0036030532 0.0420110750
sample11 -0.0035566476 -0.0566292531
sample12 0.1006128920 0.0641380864
sample13 -0.1174408390 0.0907488282
sample14 0.0981203265 0.0617738338
sample15 0.0085334337 -0.0087013048
sample16 0.0783148644 0.1581294788
sample17 -0.1483609923 0.0638581911
sample18 -0.0963086232 0.0556640544
sample19 -0.0217244079 -0.0720086522
sample20 -0.0635636388 -0.0779652789
sample21 -0.0201840373 0.1566391284
sample22 0.0218268782 -0.0764104037
sample23 0.0852041994 -0.0032689403
sample24 -0.1287170735 0.1924542631
sample25 -0.0430574160 -0.0456566708
sample26 -0.1453896863 0.0541511716
sample27 -0.0197488765 -0.1185656308
sample28 -0.1025336333 0.0650685294
sample29 0.0706018525 -0.0682987685
sample30 -0.1295627490 -0.0066768587
sample31 0.1147449129 0.1232686939
sample32 -0.0374310852 0.0380178526
sample33 0.0599516042 0.0136867730
sample34 -0.0984200803 0.0375321400
sample35 -0.0543098361 -0.0378105365
sample36 0.1403625455 -0.0343755199
sample37 0.0228941921 -0.0732845051
sample38 -0.0222077215 -0.0962594617
sample39 -0.0941738496 0.0215199043
sample40 0.0643801198 -0.0687869812
sample41 -0.0327637998 -0.1232188155
sample42 -0.0500431832 -0.0292473667
sample43 -0.0184498807 0.0233011331
sample44 0.1487898701 0.1171353209
sample45 -0.1050774212 0.1123201079
sample46 -0.1151195683 -0.1094028517
sample47 -0.0962593712 -0.0288463393
sample48 0.0004837283 -0.0310278611
sample49 0.1135207757 0.1213972712
sample50 -0.0123553099 -0.1740743842
sample51 0.0550529843 0.1258887037
sample52 0.0499121195 0.0728544823
sample53 0.1119773648 0.1588014216
sample54 -0.0360055676 0.0228575673
sample55 0.0210418989 0.0006731873
sample56 -0.0434169254 0.0633126106
sample57 0.0197824570 0.1150713882
sample58 0.0030439899 0.0326098197
sample59 0.0500253147 0.0129419529
sample60 0.0184278658 0.0136086191
sample61 0.0150299401 0.0635026299
sample62 -0.0304763853 -0.0201318777
sample63 0.1102252462 0.1285976892
sample64 0.1552588080 0.0971168446
sample65 -0.0058503062 0.0207115380
sample66 -0.0025605364 0.0424319742
sample67 0.1546634841 -0.0661715612
sample68 0.0536369309 -0.0923683261
sample69 0.0640330395 0.0081983185
sample70 0.0163517781 -0.0663230104
sample71 -0.0102537619 -0.1345921626
sample72 -0.0654196013 -0.0196119084
sample73 -0.1048556117 0.0220938591
sample74 0.0123799485 0.0586115349
sample75 0.0392077949 -0.0209754884
sample76 0.0648953390 -0.0524764385
sample77 0.1172922136 -0.0201186793
sample78 -0.1463068119 0.0708472037
sample79 0.0265211175 -0.1603308525
sample80 0.0279737161 -0.0214204844
sample81 0.0079211500 -0.0738451078
sample82 -0.1544236492 -0.0361467848
sample83 -0.0494211358 -0.0050047996
sample84 -0.0259038483 -0.0346549759
sample85 0.1116484371 -0.0031497298
sample86 -0.1306483007 -0.0377214940
sample87 -0.0554778202 -0.0459748880
sample88 -0.0301623867 0.0382197624
sample89 -0.1016866712 0.0694034016
sample90 0.0086819892 -0.0201320150
sample91 0.1578625358 -0.2097827870
sample92 0.0170936810 -0.1655807464
sample93 -0.0979806808 -0.0121512212
sample94 0.0131484112 -0.0114932096
sample95 0.0315682628 -0.0758859339
sample96 0.0024125619 -0.0470135762
sample97 0.0634545420 0.0270331777
sample98 -0.0359374626 -0.0135488370
sample99 -0.1009163341 0.1124779782
sample100 0.0551753140 0.0246489590
sample101 -0.0080118878 -0.1627368675
sample102 -0.0046444346 0.0095631449
sample103 -0.0472523169 -0.0940393267
sample104 0.0198159470 -0.0591091743
sample105 -0.0400237806 -0.0160912097
sample106 -0.0923808424 0.0369017681
sample107 -0.1019373937 0.0224954181
sample108 -0.0877091652 -0.0128834253
sample109 0.0864824369 -0.0900942104
sample110 -0.1223115543 -0.0096085780
sample111 0.0257354621 -0.0936169417
sample112 -0.0765286602 0.0270347648
sample113 0.0258803225 0.0377497098
sample114 0.0021138932 -0.0882014960
sample115 0.0303460183 -0.0723586032
sample116 0.0780508411 -0.0685066582
sample117 0.0536898088 -0.0911908663
sample118 0.0666651149 -0.0236231132
sample119 0.1021871635 -0.2324936947
sample120 0.0750216560 0.0243379040
sample121 -0.0756936405 0.0942950636
sample122 -0.0259628101 0.0731987058
sample123 -0.1037846251 -0.0369197173
sample124 0.0611207920 0.0421723478
sample125 -0.0738472718 0.0066950123
sample126 0.0972916457 0.0762640114
sample127 0.0824697645 -0.0096637343
sample128 -0.1249407658 0.0929312548
sample129 -0.0734067502 -0.0434362826
sample130 -0.0003501995 -0.0309852690
sample131 0.0930182819 0.0155937192
sample132 0.0736222809 0.0733029694
sample133 -0.0498397984 -0.0462437485
sample134 0.1644873488 0.0720005702
sample135 -0.0752297193 0.0003817855
sample136 0.0227145791 -0.0495505967
sample137 0.0564717430 -0.0288915657
sample138 0.0255988105 -0.0610857168
sample139 0.0621217807 0.0235807781
sample140 -0.0604152526 -0.0435593127
sample141 0.0246743966 0.0532648659
sample142 -0.0409560347 0.0316279792
sample143 -0.0077355233 -0.0476896281
sample144 0.0173240823 -0.0156777944
sample145 0.0485474487 0.1202770578
sample146 0.0419645651 -0.0811281232
sample147 -0.0977308337 -0.0274839926
sample148 0.0368256170 0.0803979674
sample149 -0.0072865792 -0.1532986064
sample150 0.1020825289 0.0624774726
sample151 0.0305399055 -0.0289278296
sample152 -0.0533594801 -0.0638309159
sample153 -0.0891627695 0.1799578115
sample154 -0.0727557461 -0.0834160848
sample155 -0.0880668558 -0.0220819477
sample156 -0.0276561045 -0.0326625252
sample157 -0.1155032185 0.0183616116
sample158 -0.0281507524 -0.0104938564
sample159 0.0663235704 0.0443837278
sample160 -0.0302643898 0.0404265579
sample161 0.0114715553 -0.0591025558
sample162 -0.1337087136 0.1398135472
sample163 0.1330124471 0.1688781242
sample164 -0.0150336107 0.0028416126
sample165 0.0076520260 -0.0164128391
sample166 0.0367794365 0.0630661943
sample167 0.1111988871 0.0030057865
sample168 -0.0672981605 0.0446279284
sample169 -0.0413004970 0.0224394382
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 -0.0420514627 0.0867863105
sample2 -0.0820828639 -0.0410977997
sample3 0.0155899966 -0.0195182384
sample4 -0.1001337137 -0.0410786644
sample5 -0.0153466030 -0.0253259676
sample6 0.0340325631 -0.0408223196
sample7 0.0722579822 0.0002332208
sample8 -0.0457500244 -0.0370016189
sample9 -0.0086249219 0.0820184900
sample10 -0.0423598703 -0.0083923263
sample11 0.0022548715 0.0787766035
sample12 0.0322105742 0.1479824684
sample13 -0.0293889982 -0.0306748601
sample14 0.0337482558 -0.0367506862
sample15 0.0815539176 0.1275622459
sample16 0.0508451927 0.0540604654
sample17 0.0062596619 0.0041023721
sample18 0.0705639748 -0.0351047709
sample19 -0.0476841588 -0.0509598077
sample20 0.0522962956 0.0715521833
sample21 -0.0119126965 -0.0376093069
sample22 0.0724393260 -0.0095625147
sample23 -0.0992532155 0.0134288826
sample24 -0.1595118420 0.0728662095
sample25 -0.0920693305 -0.0749757239
sample26 -0.0595540144 0.0848966069
sample27 0.0826485842 -0.0086735467
sample28 -0.0384788271 0.0440966895
sample29 0.0777671688 0.1735308482
sample30 0.1229471296 -0.0819005564
sample31 0.0579846192 -0.0238644760
sample32 0.0970393109 -0.0111426329
sample33 0.1017588003 -0.0630442598
sample34 0.0637922834 0.0377941693
sample35 0.0789984495 -0.0229723251
sample36 0.1224939267 -0.1274954985
sample37 0.1798820815 -0.1673427494
sample38 0.0466304669 0.0888160921
sample39 -0.0168687656 0.0421533762
sample40 0.1756392137 -0.1526642419
sample41 0.0042370952 0.0004928789
sample42 -0.0447849667 -0.0651504994
sample43 0.0482308392 -0.0253529286
sample44 -0.1986714946 -0.0545777794
sample45 -0.0741836565 0.0054703317
sample46 0.0478772272 -0.0007072043
sample47 0.0608188568 0.0481622615
sample48 -0.1381489388 0.0578287813
sample49 -0.0530520580 -0.1405532805
sample50 -0.0173799610 0.1602389658
sample51 0.0462560884 0.0303473833
sample52 0.0280064935 0.0280388390
sample53 0.0667621274 0.0237702037
sample54 0.0121833526 -0.0521354324
sample55 0.0182395898 0.0221328426
sample56 -0.0001255627 0.0030907362
sample57 0.0316675349 0.0530190280
sample58 0.0393918140 -0.0297798754
sample59 0.1278290889 -0.0546527989
sample60 0.1486985087 0.1069156513
sample61 0.0793122438 0.0569796505
sample62 0.1172801050 -0.0149198522
sample63 -0.0028726825 0.1300519847
sample64 0.0237364641 0.1073287722
sample65 -0.0126534915 0.0589808443
sample66 -0.0468194832 -0.0771072676
sample67 0.1494264576 -0.0769860344
sample68 0.0977961231 -0.0577351089
sample69 0.0403087314 0.0156042109
sample70 0.0221531335 0.0315440948
sample71 -0.0546434535 -0.0272396433
sample72 0.1107487798 -0.0537319409
sample73 0.0906761258 0.0579966582
sample74 0.0586555371 0.0121421661
sample75 0.0390493277 0.0349282800
sample76 -0.0022960726 -0.1676558796
sample77 -0.0232096245 -0.2067302789
sample78 -0.0929754968 -0.0434939454
sample79 -0.1619496762 -0.0378114200
sample80 0.0680365541 0.1424663471
sample81 -0.0530784048 -0.0358350836
sample82 0.0266821975 -0.0577445117
sample83 0.1517235195 -0.0448554384
sample84 -0.0570967124 -0.0273813227
sample85 0.1086289677 -0.1228119363
sample86 0.0833859953 -0.0442915012
sample87 0.0022018387 -0.0943906861
sample88 -0.0078224947 -0.1140506529
sample89 0.0611057236 -0.0094585166
sample90 0.0022928097 -0.0936253993
sample91 0.0433590584 0.3205982756
sample92 -0.1815335502 -0.0334680248
sample93 0.0267630776 0.0614429017
sample94 0.0181877791 0.0605090403
sample95 -0.0720375705 -0.0013045636
sample96 -0.0559714773 -0.0118791401
sample97 -0.0217411012 0.0195414157
sample98 0.0379177408 0.0588357093
sample99 -0.0792427294 -0.0151273766
sample100 0.0222116432 -0.0023321436
sample101 -0.0387228555 0.1224226220
sample102 -0.2094614182 -0.0516442549
sample103 0.0138481228 0.0301051942
sample104 -0.0807987029 -0.0162718897
sample105 -0.0520493376 -0.1229665150
sample106 -0.0192613299 -0.0185238178
sample107 0.0319017171 0.0405123281
sample108 -0.0140690987 0.0163421385
sample109 -0.1831930096 0.0613007617
sample110 -0.0292790595 -0.0199849073
sample111 -0.1423252026 0.0327340371
sample112 0.0426332848 -0.0029083454
sample113 -0.0771904541 0.0268733676
sample114 -0.0241641419 -0.0184080412
sample115 -0.1959015580 0.0460130742
sample116 -0.1394475993 -0.0530805764
sample117 -0.1672361824 -0.1386536351
sample118 -0.0448344274 -0.0117621919
sample119 -0.0910386305 0.2217433397
sample120 -0.0331392210 -0.0057274479
sample121 0.0307574887 0.1392506541
sample122 -0.0839781187 -0.0291994386
sample123 0.0239650408 -0.0642163738
sample124 -0.0909150622 0.0130419539
sample125 -0.0065350878 -0.1092631812
sample126 0.0935311863 0.1368284024
sample127 0.0035387915 0.0292755638
sample128 -0.0660295473 0.1018566356
sample129 0.0693638595 -0.0695421767
sample130 0.0008493442 -0.0669704329
sample131 0.0431024017 0.0174064855
sample132 -0.0637040126 0.0029374755
sample133 -0.0289494783 -0.0390818826
sample134 0.0446203010 0.0456334497
sample135 0.0712336983 0.0521634928
sample136 0.0596271016 0.0197299299
sample137 0.0793151977 -0.0380628335
sample138 -0.0973548338 -0.0454218231
sample139 0.0539904700 -0.1534327381
sample140 0.0850827024 0.0955814496
sample141 -0.0192681839 -0.0554450055
sample142 -0.0672262036 -0.0461320888
sample143 -0.0303730446 -0.0519260233
sample144 -0.0089364588 0.0145814917
sample145 -0.0638769985 0.0122258441
sample146 0.0585856333 0.0063083322
sample147 0.0894133332 -0.1124615738
sample148 -0.0216366990 -0.0615967111
sample149 -0.0515420701 -0.0839903488
sample150 0.0568283331 -0.0124468937
sample151 -0.0789532403 -0.0261831158
sample152 -0.0330753454 0.1306443592
sample153 -0.1751931277 0.1497732183
sample154 0.0421424349 -0.0037010219
sample155 0.0680177498 0.0095711201
sample156 0.0388911156 0.1057562942
sample157 0.0314769420 0.0561367413
sample158 0.0329620536 0.0353947308
sample159 -0.0398416587 -0.1007373753
sample160 0.0424938722 0.0108496162
sample161 -0.0888371333 -0.0679700144
sample162 -0.0027475917 0.1237843875
sample163 -0.0126105267 0.0725434367
sample164 -0.0566779686 -0.0458324160
sample165 -0.0315336417 -0.0236362340
sample166 -0.0612058148 -0.0425233007
sample167 0.0142729876 0.0179308272
sample168 -0.0169503508 -0.0769617888
sample169 0.0675080344 0.0131505305
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 -0.0012329690 -1.635717e-01
sample2 -0.0724350136 -6.021270e-03
sample3 -0.0188460433 -1.080036e-01
sample4 0.0390145243 3.114030e-04
sample5 0.1774811616 -2.996385e-02
sample6 -0.0451444473 -3.455859e-02
sample7 -0.0226466190 -7.020148e-03
sample8 -0.1033680330 -9.856797e-03
sample9 0.1350011798 8.979098e-02
sample10 0.1259887188 -5.097854e-02
sample11 0.0979788428 7.086535e-02
sample12 -0.0863019145 -8.620317e-02
sample13 -0.1381401134 1.828007e-01
sample14 -0.0615073883 -2.642803e-02
sample15 0.0381598983 -3.101663e-02
sample16 -0.0048776797 1.271830e-03
sample17 -0.0788481007 -1.547554e-02
sample18 -0.0884188783 -3.795486e-02
sample19 0.0703044405 -1.084004e-01
sample20 -0.0025585434 7.975876e-02
sample21 0.0941601544 -4.126743e-02
sample22 -0.0550273361 -7.806742e-02
sample23 0.0679495263 -4.102006e-02
sample24 -0.1310962937 1.649309e-01
sample25 0.0113585249 -4.426863e-02
sample26 -0.1402945982 2.016542e-02
sample27 0.0261561232 1.588464e-03
sample28 -0.0724198765 5.850591e-02
sample29 -0.0330058489 2.060843e-03
sample30 -0.0228752525 -2.015429e-02
sample31 -0.0635068005 -6.670334e-02
sample32 0.0685099647 -4.955272e-02
sample33 -0.0777765220 -1.272078e-01
sample34 0.0157842396 -3.024314e-02
sample35 -0.0529632662 1.500972e-01
sample36 0.0070900912 2.025307e-01
sample37 -0.0442420418 1.802089e-01
sample38 -0.0781511236 -3.676419e-02
sample39 0.0120331817 -3.388842e-02
sample40 -0.0473291903 1.471562e-01
sample41 0.0228189473 -2.673553e-02
sample42 -0.0245360277 -7.960867e-02
sample43 0.1036362784 -8.229577e-02
sample44 -0.1012228912 7.049449e-02
sample45 0.0013731925 -2.450913e-02
sample46 -0.0558509945 2.947398e-03
sample47 -0.0380481132 4.554174e-02
sample48 0.0784342062 4.888979e-02
sample49 -0.0605164049 -1.162357e-02
sample50 0.0530079355 -2.737931e-02
sample51 0.1514646499 5.678344e-02
sample52 0.1860935249 1.246717e-01
sample53 -0.0064177160 -2.700994e-02
sample54 0.0697038323 -2.308389e-02
sample55 0.1633577046 1.366442e-02
sample56 0.1011485074 4.682204e-02
sample57 0.1730374211 1.609603e-01
sample58 -0.0071384715 -1.666955e-02
sample59 -0.0030461625 3.005286e-02
sample60 0.0215835278 2.665878e-01
sample61 0.1510583667 1.002385e-01
sample62 -0.0925533911 -4.845840e-02
sample63 -0.0596311869 -4.137023e-02
sample64 -0.0449225830 -2.600585e-03
sample65 0.0939383728 -4.406909e-02
sample66 0.1063400690 -5.709994e-02
sample67 -0.0201589823 2.361728e-01
sample68 0.0037203311 2.418391e-02
sample69 -0.0645161225 -1.155622e-01
sample70 -0.1013440007 -1.351789e-01
sample71 -0.0016467831 -2.976841e-02
sample72 0.0328893061 -2.835856e-02
sample73 0.0275080041 -5.148185e-02
sample74 0.1341719652 -7.895280e-02
sample75 0.0951575683 -3.943184e-02
sample76 -0.0864721919 3.034993e-02
sample77 -0.1035749561 -2.545353e-02
sample78 -0.1575644213 4.939593e-02
sample79 0.0189137124 4.874679e-02
sample80 0.1384140630 4.265745e-05
sample81 -0.0118846456 -6.357931e-02
sample82 -0.1675308144 3.533912e-02
sample83 -0.0065673375 -7.812608e-02
sample84 0.1486891596 -3.109057e-02
sample85 -0.0532724341 7.417886e-02
sample86 -0.1138477295 -1.914192e-05
sample87 0.0432864024 6.080473e-02
sample88 0.0433450384 1.402491e-01
sample89 0.0331205748 -1.395401e-02
sample90 -0.0607412828 -8.610414e-02
sample91 -0.0566272442 1.303747e-01
sample92 -0.0359582448 1.061604e-01
sample93 -0.0433646368 -4.443634e-02
sample94 -0.0477291319 -1.059574e-01
sample95 -0.0249595766 -3.980525e-02
sample96 0.0035218985 -9.293928e-02
sample97 -0.0066048819 -1.527231e-01
sample98 0.0020366818 -5.579549e-02
sample99 -0.0886616209 -3.728227e-02
sample100 -0.1091259146 -3.560420e-02
sample101 -0.0739726421 -4.317997e-02
sample102 0.0574461058 -2.783916e-02
sample103 0.0142731078 9.705567e-03
sample104 0.0710395227 4.068351e-02
sample105 0.0980831323 -3.452953e-02
sample106 -0.0254259338 3.628983e-02
sample107 -0.0160653487 -9.173395e-02
sample108 -0.0200987671 -2.379692e-02
sample109 -0.0389780662 1.692358e-02
sample110 -0.0326304855 2.988109e-02
sample111 0.0676937545 -6.038213e-02
sample112 0.0167883429 5.336937e-03
sample113 0.0969216960 -2.757605e-02
sample114 -0.0026398341 -9.209156e-02
sample115 -0.0308047346 1.603822e-02
sample116 -0.1240307144 1.273000e-01
sample117 0.0334729089 5.392710e-02
sample118 -0.1037152897 6.252431e-02
sample119 -0.1064176516 1.196203e-01
sample120 -0.0771355142 -1.004933e-01
sample121 -0.0129350790 3.181975e-02
sample122 0.0847492198 -5.568328e-02
sample123 -0.0041336756 7.693184e-03
sample124 -0.0583458079 -8.396390e-02
sample125 0.0634844571 -5.232540e-02
sample126 -0.0662580979 -1.091732e-01
sample127 -0.0865024624 -1.094176e-01
sample128 -0.0627817527 -1.470965e-02
sample129 -0.0336276419 -4.007858e-02
sample130 -0.0293517755 -8.046117e-02
sample131 -0.0469197649 -2.209745e-03
sample132 -0.0241740775 -1.248598e-01
sample133 0.0907303226 1.466700e-02
sample134 -0.0350842064 7.539662e-02
sample135 0.0001333429 9.185383e-03
sample136 -0.0335876038 -9.860273e-02
sample137 -0.0640148881 -7.554469e-02
sample138 0.0060964837 -1.742762e-02
sample139 -0.0592084430 5.614969e-02
sample140 0.0427985974 -1.099550e-02
sample141 0.0618796322 -9.301039e-02
sample142 0.0898554433 3.573416e-02
sample143 0.0817389256 8.880524e-02
sample144 0.0787754777 -3.821392e-02
sample145 0.1085821542 1.569476e-01
sample146 -0.0589557883 -4.373358e-02
sample147 -0.0495330400 7.277212e-03
sample148 0.1161592746 9.079071e-03
sample149 -0.0121579369 7.788375e-02
sample150 -0.0314512532 3.520213e-02
sample151 0.0575382155 -1.945353e-02
sample152 -0.0494542060 7.025538e-02
sample153 -0.0941332848 2.153297e-01
sample154 -0.0335931948 2.078730e-02
sample155 0.0690457670 -2.780409e-02
sample156 0.1039901632 -6.292525e-02
sample157 -0.0408645790 8.065516e-03
sample158 0.1018105325 7.816876e-03
sample159 -0.0281730575 -1.207207e-02
sample160 0.1643053001 2.978099e-03
sample161 0.0374329278 8.524610e-02
sample162 -0.0804535388 8.349753e-02
sample163 -0.0743228063 -1.406226e-02
sample164 0.1208805981 -2.139461e-02
sample165 0.1608115919 2.025191e-02
sample166 -0.0425944701 -2.660715e-02
sample167 -0.0226849480 -4.464281e-02
sample168 -0.0180735620 -7.466258e-04
sample169 0.0190779023 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
15.45 0.39 15.81
|
STATegRa.Rcheck/tests_x64/STATEgRa_Example.omicsPCA.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-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 0.0781574706 -0.0431501151
sample2 -0.1192218428 0.0294087200
sample3 -0.0531411918 -0.0746840261
sample4 0.0292975182 -0.0005961228
sample5 0.0202091721 0.0110463104
sample6 0.1226088802 0.1053466907
sample7 0.1078928114 -0.0322475084
sample8 0.1782895038 0.1449363538
sample9 0.0468698241 -0.0455173001
sample10 -0.0036030537 0.0420110448
sample11 -0.0035566327 -0.0566291362
sample12 0.1006129053 0.0641383375
sample13 -0.1174408675 0.0907487854
sample14 0.0981203089 0.0617737984
sample15 0.0085334455 -0.0087011092
sample16 0.0783148389 0.1581295777
sample17 -0.1483610017 0.0638581791
sample18 -0.0963086430 0.0556639909
sample19 -0.0217243898 -0.0720087500
sample20 -0.0635636273 -0.0779651721
sample21 -0.0201840657 0.1566390542
sample22 0.0218268870 -0.0764104132
sample23 0.0852042165 -0.0032689160
sample24 -0.1287170872 0.1924543754
sample25 -0.0430574046 -0.0456568004
sample26 -0.1453896764 0.0541512931
sample27 -0.0197488680 -0.1185656487
sample28 -0.1025336370 0.0650685908
sample29 0.0706018798 -0.0682984816
sample30 -0.1295627731 -0.0066770052
sample31 0.1147448866 0.1232686794
sample32 -0.0374311022 0.0380178205
sample33 0.0599515901 0.0136866864
sample34 -0.0984200870 0.0375321796
sample35 -0.0543098530 -0.0378105652
sample36 0.1403625033 -0.0343756799
sample37 0.0228941459 -0.0732847425
sample38 -0.0222076948 -0.0962593211
sample39 -0.0941738428 0.0215199502
sample40 0.0643800777 -0.0687871906
sample41 -0.0327637775 -0.1232188243
sample42 -0.0500431762 -0.0292474801
sample43 -0.0184498891 0.0233010757
sample44 0.1487898623 0.1171352725
sample45 -0.1050774288 0.1123200955
sample46 -0.1151195554 -0.1094028659
sample47 -0.0962593697 -0.0288462730
sample48 0.0004837547 -0.0310277780
sample49 0.1135207425 0.1213970761
sample50 -0.0123552534 -0.1740741442
sample51 0.0550529563 0.1258887497
sample52 0.0499120973 0.0728545245
sample53 0.1119773350 0.1588014778
sample54 -0.0360055794 0.0228574719
sample55 0.0210418975 0.0006732103
sample56 -0.0434169398 0.0633126010
sample57 0.0197824283 0.1150714678
sample58 0.0030439765 0.0326097742
sample59 0.0500252868 0.0129418808
sample60 0.0184278413 0.0136088062
sample61 0.0150299194 0.0635027140
sample62 -0.0304763942 -0.0201318981
sample63 0.1102252467 0.1285979137
sample64 0.1552588045 0.0971170428
sample65 -0.0058502972 0.0207116164
sample66 -0.0025605457 0.0424318387
sample67 0.1546634491 -0.0661716349
sample68 0.0536369245 -0.0923684040
sample69 0.0640330442 0.0081983534
sample70 0.0163518018 -0.0663229568
sample71 -0.0102537340 -0.1345922094
sample72 -0.0654196175 -0.0196120064
sample73 -0.1048556148 0.0220939270
sample74 0.0123799377 0.0586115386
sample75 0.0392078008 -0.0209754387
sample76 0.0648953226 -0.0524766778
sample77 0.1172921927 -0.0201189727
sample78 -0.1463068213 0.0708471283
sample79 0.0265211556 -0.1603309084
sample80 0.0279737310 -0.0214202705
sample81 0.0079211686 -0.0738451661
sample82 -0.1544236559 -0.0361468817
sample83 -0.0494211543 -0.0050048796
sample84 -0.0259038382 -0.0346550407
sample85 0.1116484017 -0.0031498899
sample86 -0.1306483100 -0.0377215725
sample87 -0.0554778309 -0.0459750448
sample88 -0.0301624192 0.0382195843
sample89 -0.1016866912 0.0694033664
sample90 0.0086819855 -0.0201321587
sample91 0.1578626036 -0.2097822463
sample92 0.0170937193 -0.1655807883
sample93 -0.0979806696 -0.0121511404
sample94 0.0131484276 -0.0114931149
sample95 0.0315682875 -0.0758859319
sample96 0.0024125817 -0.0470136015
sample97 0.0634545539 0.0270332094
sample98 -0.0359374524 -0.0135487547
sample99 -0.1009163424 0.1124779425
sample100 0.0551753101 0.0246489737
sample101 -0.0080118339 -0.1627366744
sample102 -0.0046444164 0.0095630526
sample103 -0.0472522990 -0.0940392885
sample104 0.0198159612 -0.0591092021
sample105 -0.0400237875 -0.0160914208
sample106 -0.0923808515 0.0369017280
sample107 -0.1019373889 0.0224954600
sample108 -0.0877091570 -0.0128834146
sample109 0.0864824826 -0.0900940984
sample110 -0.1223115538 -0.0096086253
sample111 0.0257355041 -0.0936168994
sample112 -0.0765286710 0.0270347466
sample113 0.0258803307 0.0377497433
sample114 0.0021139152 -0.0882015304
sample115 0.0303460604 -0.0723585257
sample116 0.0780508540 -0.0685067094
sample117 0.0536898207 -0.0911910757
sample118 0.0666651190 -0.0236231066
sample119 0.1021872385 -0.2324933155
sample120 0.0750216629 0.0243379085
sample121 -0.0756936425 0.0942952713
sample122 -0.0259628132 0.0731986425
sample123 -0.1037846315 -0.0369198336
sample124 0.0611208043 0.0421723773
sample125 -0.0738472846 0.0066948198
sample126 0.0972916493 0.0762642422
sample127 0.0824697785 -0.0096636731
sample128 -0.1249407573 0.0929313961
sample129 -0.0734067582 -0.0434364014
sample130 -0.0003501980 -0.0309853756
sample131 0.0930182770 0.0155937675
sample132 0.0736222858 0.0733029798
sample133 -0.0498397944 -0.0462438264
sample134 0.1644873324 0.0720006785
sample135 -0.0752297213 0.0003818557
sample136 0.0227145904 -0.0495505641
sample137 0.0564717386 -0.0288916124
sample138 0.0255988275 -0.0610857865
sample139 0.0621217443 0.0235805604
sample140 -0.0604152414 -0.0435591777
sample141 0.0246743880 0.0532647714
sample142 -0.0409560417 0.0316278926
sample143 -0.0077355259 -0.0476897131
sample144 0.0173240903 -0.0156777792
sample145 0.0485474256 0.1202770834
sample146 0.0419645763 -0.0811281021
sample147 -0.0977308564 -0.0274841774
sample148 0.0368255955 0.0803978665
sample149 -0.0072865643 -0.1532987330
sample150 0.1020825072 0.0624774770
sample151 0.0305399173 -0.0289278735
sample152 -0.0533594510 -0.0638307128
sample153 -0.0891627719 0.1799580495
sample154 -0.0727557388 -0.0834160976
sample155 -0.0880668575 -0.0220819553
sample156 -0.0276560847 -0.0326623790
sample157 -0.1155032177 0.0183616844
sample158 -0.0281507509 -0.0104938152
sample159 0.0663235543 0.0443835836
sample160 -0.0302644018 0.0404265542
sample161 0.0114715600 -0.0591026594
sample162 -0.1337087247 0.1398137304
sample163 0.1330124318 0.1688782661
sample164 -0.0150336102 0.0028415243
sample165 0.0076520266 -0.0164128898
sample166 0.0367794293 0.0630661358
sample167 0.1111988908 0.0030058331
sample168 -0.0672981770 0.0446277986
sample169 -0.0413005092 0.0224394525
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
1 2
sample1 0.0420515983 0.0867863178
sample2 0.0820828099 -0.0410979674
sample3 -0.0155900289 -0.0195180696
sample4 0.1001336363 -0.0410788454
sample5 0.0153465473 -0.0253260196
sample6 -0.0340326472 -0.0408224339
sample7 -0.0722579949 0.0002333845
sample8 0.0457499444 -0.0370019434
sample9 0.0086250566 0.0820185179
sample10 0.0423598492 -0.0083924644
sample11 -0.0022547356 0.0787766800
sample12 -0.0322103254 0.1479824284
sample13 0.0293889735 -0.0306750566
sample14 -0.0337483295 -0.0367507300
sample15 -0.0815536980 0.1275624039
sample16 -0.0508451045 0.0540602972
sample17 -0.0062596325 0.0041023117
sample18 -0.0705640211 -0.0351047093
sample19 0.0476840636 -0.0509597633
sample20 -0.0522961605 0.0715523934
sample21 0.0119126300 -0.0376095675
sample22 -0.0724393473 -0.0095622570
sample23 0.0992532240 0.0134287040
sample24 0.1595120010 0.0728656307
sample25 0.0920692014 -0.0749758018
sample26 0.0595541890 0.0848964437
sample27 -0.0826486002 -0.0086732161
sample28 0.0384789236 0.0440965305
sample29 -0.0777668729 0.1735310822
sample30 -0.1229472572 -0.0819003074
sample31 -0.0579846727 -0.0238645691
sample32 -0.0970393302 -0.0111425126
sample33 -0.1017589193 -0.0630440877
sample34 -0.0637922063 0.0377942407
sample35 -0.0789984757 -0.0229721400
sample36 -0.1224941606 -0.1274952819
sample37 -0.1798823701 -0.1673423485
sample38 -0.0466303081 0.0888163368
sample39 0.0168688496 0.0421533313
sample40 -0.1756394827 -0.1526638562
sample41 -0.0042370944 0.0004930857
sample42 0.0447848561 -0.0651505116
sample43 -0.0482308887 -0.0253528706
sample44 0.1986713899 -0.0545783368
sample45 0.0741836799 0.0054700457
sample46 -0.0478772136 -0.0007069309
sample47 -0.0608187577 0.0481624220
sample48 0.1381490379 0.0578285756
sample49 0.0530518020 -0.1405535738
sample50 0.0173802371 0.1602392094
sample51 -0.0462560443 0.0303472436
sample52 -0.0280064527 0.0280387383
sample53 -0.0667620975 0.0237700619
sample54 -0.0121834432 -0.0521354417
sample55 -0.0182395603 0.0221328596
sample56 0.0001255718 0.0030906311
sample57 -0.0316674439 0.0530188675
sample58 -0.0393918663 -0.0297798546
sample59 -0.1278291894 -0.0546526066
sample60 -0.1486983140 0.1069158462
sample61 -0.0793121475 0.0569796650
sample62 -0.1172801256 -0.0149195985
sample63 0.0028729003 0.1300517732
sample64 -0.0237362914 0.1073286431
sample65 0.0126535901 0.0589807924
sample66 0.0468193421 -0.0771074124
sample67 -0.1494266027 -0.0769857269
sample68 -0.0977962318 -0.0577348053
sample69 -0.0403087133 0.0156042807
sample70 -0.0221530820 0.0315442607
sample71 0.0546434036 -0.0272395234
sample72 -0.1107488689 -0.0537317042
sample73 -0.0906760140 0.0579968043
sample74 -0.0586555252 0.0121421808
sample75 -0.0390492774 0.0349283763
sample76 0.0022957748 -0.1676558113
sample77 0.0232092510 -0.2067302957
sample78 0.0929754486 -0.0434941967
sample79 0.1619496049 -0.0378114667
sample80 -0.0680363143 0.1424664883
sample81 0.0530783378 -0.0358350525
sample82 -0.0266822716 -0.0577443827
sample83 -0.1517235949 -0.0448551452
sample84 0.0570966591 -0.0273813684
sample85 -0.1086291915 -0.1228117656
sample86 -0.0833860526 -0.0442912713
sample87 -0.0022019978 -0.0943906143
sample88 0.0078223025 -0.1140507456
sample89 -0.0611057286 -0.0094585025
sample90 -0.0022929759 -0.0936253487
sample91 -0.0433585118 0.3205986384
sample92 0.1815334922 -0.0334681022
sample93 -0.0267629579 0.0614429909
sample94 -0.0181876768 0.0605091070
sample95 0.0720375626 -0.0013045689
sample96 0.0559714517 -0.0118791513
sample97 0.0217411227 0.0195413501
sample98 -0.0379176357 0.0588358105
sample99 0.0792427197 -0.0151276655
sample100 -0.0222116502 -0.0023321397
sample101 0.0387230700 0.1224228191
sample102 0.2094613248 -0.0516446352
sample103 -0.0138480661 0.0301053694
sample104 0.0807986693 -0.0162719522
sample105 0.0520491210 -0.1229665754
sample106 0.0192613119 -0.0185238998
sample107 -0.0319016360 0.0405123786
sample108 0.0140691378 0.0163421513
sample109 0.1831931073 0.0613005678
sample110 0.0292790419 -0.0199849289
sample111 0.1423252499 0.0327339351
sample112 -0.0426332806 -0.0029083034
sample113 0.0771904933 0.0268731688
sample114 0.0241641045 -0.0184079324
sample115 0.1959016357 0.0460128381
sample116 0.1394475058 -0.0530807384
sample117 0.1672359317 -0.1386538042
sample118 0.0448344051 -0.0117622473
sample119 0.0910390120 0.2217435146
sample120 0.0331392017 -0.0057275352
sample121 -0.0307572319 0.1392505680
sample122 0.0839780661 -0.0291996921
sample123 -0.0239651409 -0.0642162603
sample124 0.0909150777 0.0130417361
sample125 0.0065349006 -0.1092631878
sample126 -0.0935309582 0.1368284539
sample127 -0.0035387508 0.0292755946
sample128 0.0660297451 0.1018563988
sample129 -0.0693639733 -0.0695419690
sample130 -0.0008494639 -0.0669703692
sample131 -0.0431023803 0.0174065262
sample132 0.0637040063 0.0029372595
sample133 0.0289494116 -0.0390818614
sample134 -0.0446202354 0.0456333825
sample135 -0.0712335973 0.0521636266
sample136 -0.0596270730 0.0197301252
sample137 -0.0793152719 -0.0380626426
sample138 0.0973547490 -0.0454219001
sample139 -0.0539907415 -0.1534326945
sample140 -0.0850825305 0.0955816747
sample141 0.0192680783 -0.0554451148
sample142 0.0672261256 -0.0461322603
sample143 0.0303729535 -0.0519260192
sample144 0.0089364770 0.0145814995
sample145 0.0638770173 0.0122255080
sample146 -0.0585856278 0.0063085650
sample147 -0.0894135164 -0.1124613586
sample148 0.0216365831 -0.0615968872
sample149 0.0515419246 -0.0839902108
sample150 -0.0568283637 -0.0124469098
sample151 0.0789531868 -0.0261832137
sample152 0.0330755848 0.1306444001
sample153 0.1751934168 0.1497726160
sample154 -0.0421424316 -0.0037008090
sample155 -0.0680177267 0.0095712870
sample156 -0.0388909344 0.1057564212
sample157 -0.0314768270 0.0561367855
sample158 -0.0329619925 0.0353948023
sample159 0.0398414756 -0.1007375204
sample160 -0.0424938559 0.0108496229
sample161 0.0888370133 -0.0679700949
sample162 0.0027478343 0.1237841759
sample163 0.0126106432 0.0725431388
sample164 0.0566778840 -0.0458325209
sample165 0.0315335927 -0.0236362766
sample166 0.0612057377 -0.0425235050
sample167 -0.0142729704 0.0179308396
sample168 0.0169502256 -0.0769618771
sample169 -0.0675080057 0.0131506149
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
1 2
sample1 -0.0012329519 1.635717e-01
sample2 -0.0724350096 6.021297e-03
sample3 -0.0188460435 1.080036e-01
sample4 0.0390145266 -3.113985e-04
sample5 0.1774811647 2.996384e-02
sample6 -0.0451444366 3.455859e-02
sample7 -0.0226466240 7.020137e-03
sample8 -0.1033680164 9.856810e-03
sample9 0.1350011726 -8.979101e-02
sample10 0.1259887288 5.097853e-02
sample11 0.0979788351 -7.086537e-02
sample12 -0.0863018933 8.620315e-02
sample13 -0.1381401215 -1.828007e-01
sample14 -0.0615073826 2.642803e-02
sample15 0.0381599035 3.101660e-02
sample16 -0.0048776629 -1.271836e-03
sample17 -0.0788480942 1.547557e-02
sample18 -0.0884188752 3.795488e-02
sample19 0.0703044420 1.084004e-01
sample20 -0.0025585571 -7.975877e-02
sample21 0.0941601719 4.126744e-02
sample22 -0.0550273405 7.806741e-02
sample23 0.0679495353 4.102005e-02
sample24 -0.1310962789 -1.649309e-01
sample25 0.0113585239 4.426865e-02
sample26 -0.1402945885 -2.016540e-02
sample27 0.0261561070 -1.588474e-03
sample28 -0.0724198718 -5.850590e-02
sample29 -0.0330058494 -2.060879e-03
sample30 -0.0228752622 2.015431e-02
sample31 -0.0635067855 6.670335e-02
sample32 0.0685099676 4.955272e-02
sample33 -0.0777765170 1.272078e-01
sample34 0.0157842445 3.024314e-02
sample35 -0.0529632882 -1.500972e-01
sample36 0.0070900589 -2.025307e-01
sample37 -0.0442420816 -1.802089e-01
sample38 -0.0781511274 3.676417e-02
sample39 0.0120331891 3.388842e-02
sample40 -0.0473292255 -1.471562e-01
sample41 0.0228189369 2.673552e-02
sample42 -0.0245360256 7.960868e-02
sample43 0.1036362843 8.229577e-02
sample44 -0.1012228787 -7.049447e-02
sample45 0.0013732085 2.450915e-02
sample46 -0.0558510086 -2.947394e-03
sample47 -0.0380481209 -4.554174e-02
sample48 0.0784342081 -4.888980e-02
sample49 -0.0605163962 1.162359e-02
sample50 0.0530079299 2.737927e-02
sample51 0.1514646574 -5.678345e-02
sample52 0.1860935219 -1.246717e-01
sample53 -0.0064176987 2.700994e-02
sample54 0.0697038329 2.308390e-02
sample55 0.1633577040 -1.366444e-02
sample56 0.1011485095 -4.682204e-02
sample57 0.1730374201 -1.609603e-01
sample58 -0.0071384702 1.666955e-02
sample59 -0.0030461721 -3.005286e-02
sample60 0.0215835052 -2.665878e-01
sample61 0.1510583639 -1.002385e-01
sample62 -0.0925533951 4.845841e-02
sample63 -0.0596311625 4.137022e-02
sample64 -0.0449225673 2.600566e-03
sample65 0.0939383824 4.406908e-02
sample66 0.1063400761 5.709995e-02
sample67 -0.0201590191 -2.361728e-01
sample68 0.0037203127 -2.418392e-02
sample69 -0.0645161121 1.155622e-01
sample70 -0.1013439947 1.351789e-01
sample71 -0.0016467930 2.976841e-02
sample72 0.0328892984 2.835857e-02
sample73 0.0275080091 5.148185e-02
sample74 0.1341719761 7.895279e-02
sample75 0.0951575701 3.943182e-02
sample76 -0.0864722082 -3.034990e-02
sample77 -0.1035749651 2.545356e-02
sample78 -0.1575644179 -4.939589e-02
sample79 0.0189136976 -4.874679e-02
sample80 0.1384140657 -4.269735e-05
sample81 -0.0118846469 6.357932e-02
sample82 -0.1675308265 -3.533909e-02
sample83 -0.0065673407 7.812609e-02
sample84 0.1486891598 3.109057e-02
sample85 -0.0532724515 -7.417884e-02
sample86 -0.1138477403 1.916242e-05
sample87 0.0432863869 -6.080472e-02
sample88 0.0433450239 -1.402490e-01
sample89 0.0331205790 1.395402e-02
sample90 -0.0607412824 8.610415e-02
sample91 -0.0566272598 -1.303748e-01
sample92 -0.0359582641 -1.061604e-01
sample93 -0.0433646328 4.443634e-02
sample94 -0.0477291212 1.059574e-01
sample95 -0.0249595773 3.980525e-02
sample96 0.0035219038 9.293928e-02
sample97 -0.0066048634 1.527231e-01
sample98 0.0020366866 5.579549e-02
sample99 -0.0886616045 3.728230e-02
sample100 -0.1091259097 3.560420e-02
sample101 -0.0739726463 4.317995e-02
sample102 0.0574461156 2.783917e-02
sample103 0.0142730983 -9.705576e-03
sample104 0.0710395161 -4.068351e-02
sample105 0.0980831290 3.452955e-02
sample106 -0.0254259342 -3.628982e-02
sample107 -0.0160653384 9.173395e-02
sample108 -0.0200987654 2.379693e-02
sample109 -0.0389780645 -1.692359e-02
sample110 -0.0326304898 -2.988108e-02
sample111 0.0676937588 6.038212e-02
sample112 0.0167883425 -5.336933e-03
sample113 0.0969217072 2.757604e-02
sample114 -0.0026398347 9.209156e-02
sample115 -0.0308047318 -1.603822e-02
sample116 -0.1240307284 -1.273000e-01
sample117 0.0334728954 -5.392708e-02
sample118 -0.1037152957 -6.252430e-02
sample119 -0.1064176683 -1.196203e-01
sample120 -0.0771355013 1.004933e-01
sample121 -0.0129350669 -3.181976e-02
sample122 0.0847492339 5.568329e-02
sample123 -0.0041336852 -7.693170e-03
sample124 -0.0583457912 8.396391e-02
sample125 0.0634844562 5.232542e-02
sample126 -0.0662580768 1.091732e-01
sample127 -0.0865024518 1.094176e-01
sample128 -0.0627817347 1.470966e-02
sample129 -0.0336276501 4.007859e-02
sample130 -0.0293517752 8.046118e-02
sample131 -0.0469197635 2.209739e-03
sample132 -0.0241740558 1.248598e-01
sample133 0.0907303155 -1.466700e-02
sample134 -0.0350842041 -7.539663e-02
sample135 0.0001333412 -9.185389e-03
sample136 -0.0335876015 9.860272e-02
sample137 -0.0640148895 7.554469e-02
sample138 0.0060964812 1.742763e-02
sample139 -0.0592084558 -5.614967e-02
sample140 0.0427985950 1.099548e-02
sample141 0.0618796437 9.301039e-02
sample142 0.0898554435 -3.573416e-02
sample143 0.0817389116 -8.880524e-02
sample144 0.0787754808 3.821390e-02
sample145 0.1085821564 -1.569476e-01
sample146 -0.0589557945 4.373358e-02
sample147 -0.0495330541 -7.277188e-03
sample148 0.1161592797 -9.079068e-03
sample149 -0.0121579612 -7.788374e-02
sample150 -0.0314512525 -3.520213e-02
sample151 0.0575382167 1.945353e-02
sample152 -0.0494542104 -7.025539e-02
sample153 -0.0941332705 -2.153297e-01
sample154 -0.0335932075 -2.078730e-02
sample155 0.0690457641 2.780409e-02
sample156 0.1039901692 6.292522e-02
sample157 -0.0408645771 -8.065512e-03
sample158 0.1018105310 -7.816890e-03
sample159 -0.0281730552 1.207208e-02
sample160 0.1643053024 -2.978112e-03
sample161 0.0374329148 -8.524610e-02
sample162 -0.0804535264 -8.349753e-02
sample163 -0.0743227828 1.406226e-02
sample164 0.1208806002 2.139461e-02
sample165 0.1608115887 -2.025192e-02
sample166 -0.0425944609 2.660717e-02
sample167 -0.0226849427 4.464280e-02
sample168 -0.0180735614 7.466475e-04
sample169 0.0190778996 -2.645403e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
[,1] [,2]
sample1 -0.0572060227 -1.729087e-02
sample2 0.0875245208 1.112588e-02
sample3 0.0403482602 -3.168994e-02
sample4 -0.0218345996 4.052760e-06
sample5 -0.0150905011 4.795041e-03
sample6 -0.0924362933 4.511003e-02
sample7 -0.0793066751 -1.243823e-02
sample8 -0.1342997187 6.215220e-02
sample9 -0.0338886944 -1.854401e-02
sample10 0.0020547173 1.749421e-02
sample11 0.0037275602 -2.364116e-02
sample12 -0.0753094533 2.772698e-02
sample13 0.0856160091 3.679963e-02
sample14 -0.0737457307 2.668452e-02
sample15 -0.0062111746 -3.554864e-03
sample16 -0.0602355268 6.675115e-02
sample17 0.1086768843 2.524534e-02
sample18 0.0702999472 2.231671e-02
sample19 0.0173785882 -3.024846e-02
sample20 0.0484173812 -3.310904e-02
sample21 0.0124657042 6.517144e-02
sample22 -0.0140989936 -3.159137e-02
sample23 -0.0627028403 -5.393710e-04
sample24 0.0919972100 7.909297e-02
sample25 0.0326998483 -1.945206e-02
sample26 0.1064741246 2.120849e-02
sample27 0.0166058995 -4.964993e-02
sample28 0.0743504770 2.614211e-02
sample29 -0.0511008491 -2.782647e-02
sample30 0.0962250842 -3.974893e-03
sample31 -0.0869563008 5.250819e-02
sample32 0.0271858919 1.552005e-02
sample33 -0.0448364581 6.243160e-03
sample34 0.0718415218 1.469396e-02
sample35 0.0403086451 -1.632629e-02
sample36 -0.1036402827 -1.304320e-02
sample37 -0.0159385744 -3.036525e-02
sample38 0.0182198369 -4.034805e-02
sample39 0.0690363619 8.058350e-03
sample40 -0.0467312750 -2.810325e-02
sample41 0.0263674438 -5.171216e-02
sample42 0.0374578960 -1.268634e-02
sample43 0.0132336869 9.536642e-03
sample44 -0.1119154428 5.028683e-02
sample45 0.0759639367 4.587903e-02
sample46 0.0871885519 -4.670385e-02
sample47 0.0721490571 -1.288540e-02
sample48 0.0005086144 -1.290565e-02
sample49 -0.0858177028 5.173760e-02
sample50 0.0118992665 -7.276215e-02
sample51 -0.0426446855 5.306205e-02
sample52 -0.0381605826 3.086785e-02
sample53 -0.0855757630 6.730043e-02
sample54 0.0261723092 9.184260e-03
sample55 -0.0156418304 4.682404e-04
sample56 0.0307831193 2.597550e-02
sample57 -0.0157242103 4.829381e-02
sample58 -0.0031174404 1.359898e-02
sample59 -0.0373001859 5.868397e-03
sample60 -0.0142609099 5.831654e-03
sample61 -0.0122255144 2.663579e-02
sample62 0.0228002942 -8.692265e-03
sample63 -0.0833127581 5.473229e-02
sample64 -0.1166548159 4.196500e-02
sample65 0.0038808902 8.568590e-03
sample66 0.0011561811 1.766612e-02
sample67 -0.1129311062 -2.608702e-02
sample68 -0.0382526429 -3.804045e-02
sample69 -0.0476502440 4.003241e-03
sample70 -0.0110329882 -2.752719e-02
sample71 0.0096850282 -5.627056e-02
sample72 0.0487124704 -8.800131e-03
sample73 0.0773058132 8.239864e-03
sample74 -0.0102488176 2.454957e-02
sample75 -0.0286613976 -8.387293e-03
sample76 -0.0472655595 -2.129315e-02
sample77 -0.0865043074 -7.296820e-03
sample78 0.1070293698 2.818346e-02
sample79 -0.0165060681 -6.659721e-02
sample80 -0.0206765949 -8.712112e-03
sample81 -0.0050943615 -3.079175e-02
sample82 0.1153622361 -1.647054e-02
sample83 0.0367979217 -2.538114e-03
sample84 0.0199463070 -1.468961e-02
sample85 -0.0827122185 -2.709824e-04
sample86 0.0969487314 -1.699897e-02
sample87 0.0421957457 -1.965953e-02
sample88 0.0215934743 1.566050e-02
sample89 0.0751559502 2.811652e-02
sample90 -0.0057328000 -8.283795e-03
sample91 -0.1134005268 -8.603522e-02
sample92 -0.0101689918 -6.894992e-02
sample93 0.0725967502 -6.003176e-03
sample94 -0.0096878852 -4.693081e-03
sample95 -0.0223502239 -3.139636e-02
sample96 -0.0013232863 -1.963604e-02
sample97 -0.0476541710 1.183660e-02
sample98 0.0269546160 -5.978398e-03
sample99 0.0728179461 4.597884e-02
sample100 -0.0413398038 1.079347e-02
sample101 0.0087536994 -6.796076e-02
sample102 0.0032509529 3.932612e-03
sample103 0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105 0.0294940465 -7.140722e-03
sample106 0.0686472054 1.462895e-02
sample107 0.0748635927 8.401339e-03
sample108 0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110 0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112 0.0570870472 1.066018e-02
sample113 -0.0200110554 1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586 1.089843e-02
sample121 0.0542443861 3.861344e-02
sample122 0.0178575357 3.027138e-02
sample123 0.0775020581 -1.636852e-02
sample124 -0.0460701050 1.814758e-02
sample125 0.0543846585 2.075898e-03
sample126 -0.0729417144 3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128 0.0908136899 3.758801e-02
sample129 0.0552445878 -1.879062e-02
sample130 0.0007128089 -1.294308e-02
sample131 -0.0693311345 7.357082e-03
sample132 -0.0556565156 3.126995e-02
sample133 0.0375870104 -1.977240e-02
sample134 -0.1229130924 3.159495e-02
sample135 0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201 1.040683e-02
sample140 0.0452288969 -1.876279e-02
sample141 -0.0189142561 2.247042e-02
sample142 0.0297545566 1.280524e-02
sample143 0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491 5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147 0.0726083535 -1.239968e-02
sample148 -0.0284795794 3.389732e-02
sample149 0.0082261455 -6.399305e-02
sample150 -0.0765013197 2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152 0.0403422737 -2.714879e-02
sample153 0.0629117719 7.425085e-02
sample154 0.0551622927 -3.548984e-02
sample155 0.0654439133 -1.005306e-02
sample156 0.0209310714 -1.390213e-02
sample157 0.0851522597 6.577150e-03
sample158 0.0208354599 -4.663078e-03
sample159 -0.0498794349 1.913257e-02
sample160 0.0216074437 1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162 0.0963663017 5.705881e-02
sample163 -0.1009542191 7.174224e-02
sample164 0.0109881996 1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357 2.673084e-02
sample167 -0.0825048036 2.278863e-03
sample168 0.0486147429 1.793843e-02
sample169 0.0302506727 8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
[,1] [,2]
sample1 -0.0621842115 -1.364509e-02
sample2 0.0944623785 9.720892e-03
sample3 0.0406196267 -2.236338e-02
sample4 -0.0229316496 -3.932487e-04
sample5 -0.0157330047 3.231033e-03
sample6 -0.0945794025 3.120720e-02
sample7 -0.0854427118 -1.052880e-02
sample8 -0.1376625920 4.286608e-02
sample9 -0.0377115311 -1.415134e-02
sample10 0.0035244506 1.280825e-02
sample11 0.0016639987 -1.717895e-02
sample12 -0.0781403168 1.884368e-02
sample13 0.0938400516 2.838858e-02
sample14 -0.0759839772 1.810989e-02
sample15 -0.0068340837 -2.705361e-03
sample16 -0.0590150849 4.757848e-02
sample17 0.1178805097 2.040526e-02
sample18 0.0767858320 1.756604e-02
sample19 0.0157112113 -2.172867e-02
sample20 0.0485318300 -2.327033e-02
sample21 0.0185928176 4.777095e-02
sample22 -0.0191358702 -2.329775e-02
sample23 -0.0672994194 -1.535656e-03
sample24 0.1047476642 5.935707e-02
sample25 0.0329844953 -1.358036e-02
sample26 0.1154952052 1.741529e-02
sample27 0.0133849853 -3.590922e-02
sample28 0.0821554039 2.042376e-02
sample29 -0.0567643690 -2.123848e-02
sample30 0.1016073931 -1.134728e-03
sample31 -0.0880396372 3.670548e-02
sample32 0.0300363338 1.182406e-02
sample33 -0.0467252272 3.739254e-03
sample34 0.0783666394 1.203777e-02
sample35 0.0424227097 -1.118559e-02
sample36 -0.1107646166 -1.143464e-02
sample37 -0.0191667664 -2.246060e-02
sample38 0.0155968095 -2.909621e-02
sample39 0.0746847148 7.148218e-03
sample40 -0.0517028178 -2.137267e-02
sample41 0.0234979494 -3.723018e-02
sample42 0.0388797356 -8.557228e-03
sample43 0.0149555568 7.210002e-03
sample44 -0.1150305613 3.461805e-02
sample45 0.0846146236 3.486020e-02
sample46 0.0884426404 -3.246853e-02
sample47 0.0748644971 -8.083045e-03
sample48 -0.0012033198 -9.403647e-03
sample49 -0.0872662737 3.616245e-02
sample50 0.0066941314 -5.284863e-02
sample51 -0.0411777630 3.791830e-02
sample52 -0.0379355780 2.180834e-02
sample53 -0.0851639886 4.751761e-02
sample54 0.0288006248 7.184424e-03
sample55 -0.0164920835 5.919925e-05
sample56 0.0355115616 1.951043e-02
sample57 -0.0141146068 3.492409e-02
sample58 -0.0015636132 9.862883e-03
sample59 -0.0390656483 3.590929e-03
sample60 -0.0139454780 3.963030e-03
sample61 -0.0106410274 1.919705e-02
sample62 0.0236748439 -5.922677e-03
sample63 -0.0846790877 3.839102e-02
sample64 -0.1202581015 2.846469e-02
sample65 0.0050548584 6.328644e-03
sample66 0.0028013072 1.291807e-02
sample67 -0.1231623009 -2.112565e-02
sample68 -0.0437782161 -2.845072e-02
sample69 -0.0501199692 2.053469e-03
sample70 -0.0140278645 -2.027157e-02
sample71 0.0057489505 -4.085977e-02
sample72 0.0511212704 -5.522408e-03
sample73 0.0828141409 7.431582e-03
sample74 -0.0085959456 1.772951e-02
sample75 -0.0312180394 -6.636869e-03
sample76 -0.0519051781 -1.640191e-02
sample77 -0.0925924762 -6.907800e-03
sample78 0.1163971046 2.251122e-02
sample79 -0.0240906926 -4.887766e-02
sample80 -0.0221327065 -6.730703e-03
sample81 -0.0072114968 -2.254399e-02
sample82 0.1204416674 -9.907422e-03
sample83 0.0386739485 -1.171663e-03
sample84 0.0195988488 -1.033806e-02
sample85 -0.0877680171 -1.725057e-03
sample86 0.1023541048 -1.062501e-02
sample87 0.0425213089 -1.356865e-02
sample88 0.0244788514 1.180820e-02
sample89 0.0804276691 2.188588e-02
sample90 -0.0074639871 -6.140721e-03
sample91 -0.1278832404 -6.485140e-02
sample92 -0.0162199697 -5.048358e-02
sample93 0.0769344893 -3.045135e-03
sample94 -0.0104345587 -3.593172e-03
sample95 -0.0260058453 -2.330475e-02
sample96 -0.0025018700 -1.433516e-02
sample97 -0.0492358305 7.774183e-03
sample98 0.0279220220 -3.862141e-03
sample99 0.0813921923 3.487339e-02
sample100 -0.0428797405 7.112807e-03
sample101 0.0032855240 -4.940743e-02
sample102 0.0038439317 2.938008e-03
sample103 0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105 0.0314853405 -4.656633e-03
sample106 0.0726456731 1.192390e-02
sample107 0.0807342975 7.508627e-03
sample108 0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110 0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112 0.0599954082 8.820317e-03
sample113 -0.0195006577 1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131 6.937326e-03
sample121 0.0613899126 2.915307e-02
sample122 0.0218424338 2.241775e-02
sample123 0.0809008460 -1.051759e-02
sample124 -0.0472109313 1.239887e-02
sample125 0.0583180947 2.521167e-03
sample126 -0.0753941872 2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128 0.1000212216 2.908091e-02
sample129 0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877 4.091672e-03
sample132 -0.0566219024 2.179861e-02
sample133 0.0384172955 -1.372840e-02
sample134 -0.1280862736 2.077912e-02
sample135 0.0592633273 6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861 6.729304e-03
sample140 0.0468926306 -1.285498e-02
sample141 -0.0186248693 1.605439e-02
sample142 0.0328031246 9.887746e-03
sample143 0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310 3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147 0.0765025670 -7.714769e-03
sample148 -0.0276016641 2.420589e-02
sample149 0.0027545308 -4.653007e-02
sample150 -0.0792296010 1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152 0.0409796547 -1.907063e-02
sample153 0.0734301757 5.528780e-02
sample154 0.0557740684 -2.487723e-02
sample155 0.0689436560 -6.127635e-03
sample156 0.0212272938 -9.747423e-03
sample157 0.0911931194 6.355708e-03
sample158 0.0220840645 -3.016357e-03
sample159 -0.0513244242 1.304175e-02
sample160 0.0246213576 1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162 0.1078802043 4.337260e-02
sample163 -0.1017965082 5.047171e-02
sample164 0.0119430799 9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180 1.899222e-02
sample167 -0.0872832229 1.516582e-04
sample168 0.0540714512 1.397701e-02
sample169 0.0328432652 7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
[,1] [,2]
sample1 0.0133684846 2.195848e-02
sample2 0.0254157197 -1.058416e-02
sample3 -0.0049551479 -4.840017e-03
sample4 0.0310390570 -1.063929e-02
sample5 0.0046941318 -6.488426e-03
sample6 -0.0107406753 -1.026702e-02
sample7 -0.0225157631 2.624712e-04
sample8 0.0141320952 -9.505821e-03
sample9 0.0029681280 2.078210e-02
sample10 0.0131729174 -2.275042e-03
sample11 -0.0004164298 1.994019e-02
sample12 -0.0095211620 3.759883e-02
sample13 0.0091018604 -7.953956e-03
sample14 -0.0106557524 -9.181659e-03
sample15 -0.0249924121 3.262724e-02
sample16 -0.0156216400 1.375700e-02
sample17 -0.0019382446 1.073994e-03
sample18 -0.0221072481 -8.703592e-03
sample19 0.0146917619 -1.311712e-02
sample20 -0.0160353760 1.826290e-02
sample21 0.0035947899 -9.616341e-03
sample22 -0.0225060762 -2.532589e-03
sample23 0.0310000683 3.033060e-03
sample24 0.0499544372 1.809450e-02
sample25 0.0284442301 -1.932558e-02
sample26 0.0188220043 2.146985e-02
sample27 -0.0257763219 -1.999228e-03
sample28 0.0120888648 1.125834e-02
sample29 -0.0236482520 4.426726e-02
sample30 -0.0385486305 -2.055935e-02
sample31 -0.0181539336 -5.877838e-03
sample32 -0.0302630460 -2.607192e-03
sample33 -0.0319565715 -1.562628e-02
sample34 -0.0197970124 9.906813e-03
sample35 -0.0247412713 -5.434440e-03
sample36 -0.0386259060 -3.190394e-02
sample37 -0.0566199273 -4.192574e-02
sample38 -0.0142060273 2.259644e-02
sample39 0.0053589035 1.076485e-02
sample40 -0.0552546493 -3.819896e-02
sample41 -0.0013089975 9.278818e-05
sample42 0.0137252142 -1.664652e-02
sample43 -0.0151259626 -6.290953e-03
sample44 0.0617391754 -1.442883e-02
sample45 0.0231410886 1.163143e-03
sample46 -0.0148898209 -1.384176e-04
sample47 -0.0187252536 1.221690e-02
sample48 0.0432839432 1.416671e-02
sample49 0.0160818605 -3.588745e-02
sample50 0.0059333545 4.067003e-02
sample51 -0.0142914866 7.776270e-03
sample52 -0.0086339952 7.208917e-03
sample53 -0.0207386980 6.272432e-03
sample54 -0.0039856719 -1.316934e-02
sample55 -0.0056217017 5.692315e-03
sample56 0.0000123292 8.978290e-04
sample57 -0.0095805555 1.324253e-02
sample58 -0.0124160295 -7.326376e-03
sample59 -0.0400195442 -1.349736e-02
sample60 -0.0460063358 2.770091e-02
sample61 -0.0245266456 1.470710e-02
sample62 -0.0366022783 -3.437352e-03
sample63 0.0013742171 3.288796e-02
sample64 -0.0070599859 2.739588e-02
sample65 0.0041201911 1.498268e-02
sample66 0.0143173351 -1.968812e-02
sample67 -0.0467477531 -1.929938e-02
sample68 -0.0306751978 -1.436184e-02
sample69 -0.0125317217 4.130407e-03
sample70 -0.0068071487 8.080857e-03
sample71 0.0169170264 -7.027348e-03
sample72 -0.0346909749 -1.333770e-02
sample73 -0.0280506153 1.493843e-02
sample74 -0.0182611498 3.294697e-03
sample75 -0.0120563964 8.974612e-03
sample76 0.0001437236 -4.253184e-02
sample77 0.0065330299 -5.252886e-02
sample78 0.0288278141 -1.127782e-02
sample79 0.0503961481 -1.023318e-02
sample80 -0.0207693429 3.648391e-02
sample81 0.0163562768 -9.074596e-03
sample82 -0.0084317129 -1.478976e-02
sample83 -0.0474097918 -1.103126e-02
sample84 0.0177181395 -7.191197e-03
sample85 -0.0342718548 -3.082360e-02
sample86 -0.0261671791 -1.089491e-02
sample87 -0.0009486358 -2.411514e-02
sample88 0.0020528931 -2.894615e-02
sample89 -0.0189361111 -2.638639e-03
sample90 -0.0009863658 -2.390075e-02
sample91 -0.0124352695 8.153234e-02
sample92 0.0564264106 -8.909537e-03
sample93 -0.0081461774 1.570851e-02
sample94 -0.0054896581 1.547251e-02
sample95 0.0224073150 -4.374348e-04
sample96 0.0173528924 -3.050441e-03
sample97 0.0067948115 5.008237e-03
sample98 -0.0116030825 1.498764e-02
sample99 0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101 0.0124923691 3.091503e-02
sample102 0.0650835386 -1.367400e-02
sample103 -0.0042741828 7.855985e-03
sample104 0.0250591040 -4.171938e-03
sample105 0.0157516368 -3.121990e-02
sample106 0.0060593853 -5.101693e-03
sample107 -0.0098329626 1.044506e-02
sample108 0.0044269853 4.142036e-03
sample109 0.0572473486 1.517542e-02
sample110 0.0090474827 -5.119868e-03
sample111 0.0444263015 7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113 0.0241047399 6.706740e-03
sample114 0.0074558775 -4.728652e-03
sample115 0.0611851433 1.117210e-02
sample116 0.0432646951 -1.380556e-02
sample117 0.0516750066 -3.575617e-02
sample118 0.0139942100 -3.279138e-03
sample119 0.0291722987 5.587946e-02
sample120 0.0103515853 -1.690016e-03
sample121 -0.0091396331 3.552116e-02
sample122 0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124 0.0283466326 3.127845e-03
sample125 0.0016472378 -2.770692e-02
sample126 -0.0286529417 3.489336e-02
sample127 -0.0010224500 7.483214e-03
sample128 0.0209049296 2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968 4.637390e-03
sample132 0.0198526786 5.723983e-04
sample133 0.0088812957 -9.988115e-03
sample134 -0.0137484514 1.172591e-02
sample135 -0.0220314568 1.347465e-02
sample136 -0.0185173353 5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138 0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762 2.456863e-02
sample141 0.0058369763 -1.420854e-02
sample142 0.0207886071 -1.188764e-02
sample143 0.0092832598 -1.324238e-02
sample144 0.0028442140 3.627979e-03
sample145 0.0199749569 2.862202e-03
sample146 -0.0182236697 1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148 0.0065435868 -1.572917e-02
sample149 0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151 0.0245166984 -6.888241e-03
sample152 0.0107259913 3.314630e-02
sample153 0.0550963965 3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574 2.642246e-03
sample156 -0.0117803505 2.698265e-02
sample157 -0.0096167165 1.433840e-02
sample158 -0.0101754772 9.137620e-03
sample159 0.0120662931 -2.565236e-02
sample160 -0.0132238202 2.916023e-03
sample161 0.0274491966 -1.748284e-02
sample162 0.0012482909 3.152261e-02
sample163 0.0042031315 1.830701e-02
sample164 0.0174896157 -1.175915e-02
sample165 0.0097517662 -6.119019e-03
sample166 0.0190134679 -1.121582e-02
sample167 -0.0044140836 4.665585e-03
sample168 0.0049689168 -1.941822e-02
sample169 -0.0209802098 3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
[,1] [,2]
sample1 -0.0515543627 -0.0305856787
sample2 -0.0144993256 0.0236342950
sample3 -0.0371833108 -0.0140263348
sample4 0.0068945388 -0.0132539692
sample5 0.0215035333 -0.0663338101
sample6 -0.0187055152 0.0088773016
sample7 -0.0061521552 0.0064029054
sample8 -0.0210874459 0.0334652901
sample9 0.0516865043 -0.0291142799
sample10 0.0059440366 -0.0527217447
sample11 0.0393010793 -0.0200624712
sample12 -0.0420837100 0.0131331362
sample13 0.0333252565 0.0818552509
sample14 -0.0190062644 0.0160202175
sample15 -0.0030968049 -0.0189230681
sample16 -0.0004452158 0.0018880102
sample17 -0.0185848615 0.0240170131
sample18 -0.0273093598 0.0230213640
sample19 -0.0217761111 -0.0445894441
sample20 0.0245820821 0.0159812738
sample21 0.0034527644 -0.0400016054
sample22 -0.0340789054 0.0039289109
sample23 -0.0010344929 -0.0310161212
sample24 0.0289468503 0.0760962436
sample25 -0.0119098496 -0.0122798760
sample26 -0.0181001057 0.0517892852
sample27 0.0050465417 -0.0086515844
sample28 0.0057491502 0.0358830107
sample29 -0.0051104246 0.0116605117
sample30 -0.0103085904 0.0039678538
sample31 -0.0319929858 0.0090606113
sample32 -0.0036232521 -0.0328202010
sample33 -0.0534742153 0.0024751837
sample34 -0.0067495749 -0.0111000311
sample35 0.0378745721 0.0465929296
sample36 0.0647886800 0.0359987924
sample37 0.0488441236 0.0492906912
sample38 -0.0251514062 0.0197110110
sample39 -0.0085428066 -0.0105117852
sample40 0.0379324087 0.0440810741
sample41 -0.0044199152 -0.0128820644
sample42 -0.0292553573 -0.0067045265
sample43 -0.0077829155 -0.0510178219
sample44 0.0045122248 0.0479660309
sample45 -0.0074444298 -0.0051116726
sample46 -0.0088025512 0.0196186661
sample47 0.0076696301 0.0215947965
sample48 0.0290108585 -0.0175568376
sample49 -0.0141754858 0.0184717099
sample50 0.0006282201 -0.0233054373
sample51 0.0441995177 -0.0410022921
sample52 0.0715329391 -0.0399499475
sample53 -0.0095954087 -0.0029140909
sample54 0.0048933768 -0.0281884386
sample55 0.0327325487 -0.0532290012
sample56 0.0323068984 -0.0256595538
sample57 0.0806603122 -0.0286748097
sample58 -0.0064792049 -0.0006945349
sample59 0.0088958941 0.0067389649
sample60 0.0874124612 0.0431964341
sample61 0.0577604571 -0.0326112099
sample62 -0.0313318464 0.0224391756
sample63 -0.0233625220 0.0125110562
sample64 -0.0086426068 0.0148770341
sample65 0.0025256193 -0.0404466327
sample66 0.0006014071 -0.0471576264
sample67 0.0706087042 0.0516228406
sample68 0.0082301011 0.0033109509
sample69 -0.0475076743 0.0001452708
sample70 -0.0600773716 0.0089986962
sample71 -0.0096321627 -0.0050761187
sample72 -0.0031773546 -0.0166221542
sample73 -0.0113700517 -0.0191726684
sample74 -0.0014179662 -0.0608101325
sample75 0.0041911740 -0.0399981269
sample76 -0.0055326449 0.0353114263
sample77 -0.0260214459 0.0305731380
sample78 -0.0119267436 0.0632236007
sample79 0.0186017239 0.0027402910
sample80 0.0241047889 -0.0472697181
sample81 -0.0220288317 -0.0079577210
sample82 -0.0180751258 0.0639051029
sample83 -0.0256671713 -0.0125898269
sample84 0.0161392598 -0.0567222449
sample85 0.0139988188 0.0322763454
sample86 -0.0198382995 0.0389225776
sample87 0.0266270281 -0.0032979996
sample88 0.0515677078 0.0117902495
sample89 0.0014022125 -0.0140510488
sample90 -0.0375949749 0.0044004551
sample91 0.0310397965 0.0440610926
sample92 0.0270570567 0.0324380452
sample93 -0.0215009202 0.0063993941
sample94 -0.0415702912 -0.0037692077
sample95 -0.0168416047 0.0010019120
sample96 -0.0285582661 -0.0187991000
sample97 -0.0490843868 -0.0266760748
sample98 -0.0171579033 -0.0112897471
sample99 -0.0271316525 0.0232395583
sample100 -0.0301789816 0.0305498693
sample101 -0.0264371151 0.0170723968
sample102 0.0012767734 -0.0248949597
sample103 0.0055214687 -0.0030040587
sample104 0.0251346074 -0.0165212671
sample105 0.0062424215 -0.0400309901
sample106 0.0069768684 0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679 0.0023637162
sample109 -0.0014762845 0.0165583675
sample110 0.0036971063 0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112 0.0046098120 -0.0048009350
sample113 0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453 0.0135805368
sample116 0.0183575759 0.0665377581
sample117 0.0227640036 -0.0012287760
sample118 0.0015695248 0.0472617382
sample119 0.0190084932 0.0590034062
sample120 -0.0449645755 0.0072755697
sample121 0.0077307184 0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123 0.0016959300 0.0028593594
sample124 -0.0365091615 0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408 0.0019165544
sample127 -0.0494064872 0.0088209044
sample128 -0.0155454766 0.0186819802
sample129 -0.0184340400 0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422 0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133 0.0204029276 -0.0282209049
sample134 0.0175513332 0.0262883962
sample135 0.0029009925 0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323 0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139 0.0073103935 0.0308956174
sample140 0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142 0.0268670676 -0.0239229634
sample143 0.0421049126 -0.0110888235
sample144 0.0017253664 -0.0341766012
sample145 0.0681741320 -0.0073526377
sample146 -0.0239965222 0.0118396767
sample147 -0.0063453522 0.0183130585
sample148 0.0230825251 -0.0379753037
sample149 0.0223298673 0.0188909118
sample150 0.0055709108 0.0174179009
sample151 0.0039177786 -0.0233533275
sample152 0.0134325667 0.0302344591
sample153 0.0511990309 0.0730230140
sample154 0.0006698324 0.0154177486
sample155 0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599 0.0154934573
sample158 0.0201775524 -0.0332982124
sample159 -0.0086909001 0.0073496711
sample160 0.0295437331 -0.0555734536
sample161 0.0332754288 0.0033779619
sample162 0.0121954537 0.0433540412
sample163 -0.0173490933 0.0227219128
sample164 0.0143374783 -0.0453542590
sample165 0.0343612593 -0.0511194536
sample166 -0.0157536004 0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919 0.0060747155
sample169 0.0116231468 -0.0015112800
>
> ## 3.3 Plotting VAF
>
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
>
> # JIVE plotVAF
> plotVAF(jiveRes)
>
>
> #########################
> ## PART 4. Plot Results
>
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
>
> # Scores for common part. JIVE
> plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
>
> # Scores for common part. O2PLS.
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for common part. O2PLS.
> plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common",
+ combined=TRUE,block=NULL,color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
>
> # Scores for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for distinctive part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual",
+ combined=TRUE,block=NULL,color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
>
> # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block)
> p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Combined plot of scores for common and distinctive part. DISCO (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ legend,heights=c(6/7,1/7))
>
> # Loadings for common part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> # Loadings for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> # Combined plot for loadings from common and distinctive part (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+ combined=FALSE,block="expr",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,
+ labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+ labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
>
> ## Plot scores and loadings togheter: Common components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> ## Plot scores and loadings togheter: Common components O2PLS
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
> ## Plot scores and loadings togheter: Distintive components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+ combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+ combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+ background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+ axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+ p2+theme(legend.position="none"),nrow=1),
+ heights=c(6/7,1/7))
>
>
>
>
> proc.time()
user system elapsed
15.95 0.45 16.39
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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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