R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> BiocGenerics:::testPackage("STATegRa")
Common components
[1] 2

Distinctive components
[[1]]
[1] 0

[[2]]
[1] 0

Common components
[1] 2

Distinctive components
[[1]]
[1] 1

[[2]]
[1] 1

Common components
[1] 2

Distinctive components
[[1]]
[1] 2

[[2]]
[1] 2



RUNIT TEST PROTOCOL -- Fri Oct 15 06:18:52 2021 
*********************************************** 
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: In rownames(pData) == colnames(exprs) :
  longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
> 
> proc.time()
   user  system elapsed 
   3.39    0.21    3.59 

R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> BiocGenerics:::testPackage("STATegRa")
Common components
[1] 2

Distinctive components
[[1]]
[1] 0

[[2]]
[1] 0

Common components
[1] 2

Distinctive components
[[1]]
[1] 1

[[2]]
[1] 1

Common components
[1] 2

Distinctive components
[[1]]
[1] 2

[[2]]
[1] 2



RUNIT TEST PROTOCOL -- Fri Oct 15 06:20:41 2021 
*********************************************** 
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
STATegRa RUnit Tests - 4 test functions, 0 errors, 0 failures
Number of test functions: 4 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: In rownames(pData) == colnames(exprs) :
  longer object length is not a multiple of shorter object length
2: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "%accum",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 2
3: In modelSelection(Input = list(B1, B2), Rmax = 4, fac.sel = "fixed.num",  :
  Rmax cannot be higher than the minimum of components selected for each block. Rmax fixed to: 3
> 
> proc.time()
   user  system elapsed 
   3.42    0.25    3.65 

R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> ###########################################
> ########### EXAMPLE OF THE OMICSCLUSTERING
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
> 
> #############################################
> ## PART 1: CREATING a bioMap CLASS
> #############################################
> ####### This part creates or reads the map between features.
> ####### In the present example the map is downloaded from a resource.
> #######   then the class is created.
> 
> #load("../data/STATegRa_S2.rda")
> data(STATegRa_S2)
> 
> MAP.SYMBOL<-bioMap(name = "Symbol-miRNA",
+                 metadata =  list(type_v1="Gene",type_v2="miRNA",
+                                  source_database="targetscan.Hs.eg.db",
+                                  data_extraction="July2014"),
+                 map=mapdata)
> 
> 
> #############################################
> ## PART 2: CREATING a bioDist CLASS
> #############################################
> ##### In the second part given a set of main features and surrogate feautres,
> #####    the profile of the main features is computed through the surrogate features.
> 
> # Load Data
> data(STATegRa_S1)
>   #load("../data/STATegRa.S1.Rdata")
> 
> ## Create ExpressionSets
> #  source("../R/STATegRa_omicsPCA_classes_and_methods.R")
> # Block1 - Expression data
> mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname"))
> 
> # Create Gene-gene distance computed through miRNA data
> bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1),     
+              reference = "Var1",
+              mapping = MAP.SYMBOL,
+              surrogateData = miRNA.ds,  ### miRNA data
+              referenceData = mRNA.ds,  ### mRNA data
+              maxitems=2,
+              selectionRule="sd",
+              expfac=NULL,
+              aggregation = "sum",
+              distance = "spearman",
+              noMappingDist = 0,
+              filtering = NULL,
+              name = "mRNAbymiRNA")
> 
> require(Biobase)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
    tapply, union, unique, unsplit, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> 
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+                  distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+                  map.name = "id",
+                  map.metadata = list(),
+                  params = list())
> 
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
> 
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
> 
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+                        bioDistList = bioDistList,
+                        weights=weights)
> length(bioDistWList)
[1] 4
> 
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
> 
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+                listDistW = bioDistWList,
+                method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
4: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
5: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
6: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
7: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
> 
> #############################################
> ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
> #############################################
> 
> ## IDH1
> 
> IDH1.F<-bioDistFeature(Feature = "IDH1" ,
+                        listDistW = bioDistWList,
+                        threshold.cor=0.7)
> bioDistFeaturePlot(data=IDH1.F)
> 
> ## PDGFRA
> 
> #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
> #                       listDistW = bioDistWList,
> #                       threshold.cor=0.7)
> #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
> 
> ## EGFR
> #EGFR.F<-bioDistFeature(Feature = "EGFR" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.7)
> #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
> 
> ## MGMT
> #MGMT.F<-bioDistFeature(Feature = "MGMT" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.5)
> #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
  26.68    0.78   27.45 

R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> ###########################################
> ########### EXAMPLE OF THE OMICSCLUSTERING
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
> 
> #############################################
> ## PART 1: CREATING a bioMap CLASS
> #############################################
> ####### This part creates or reads the map between features.
> ####### In the present example the map is downloaded from a resource.
> #######   then the class is created.
> 
> #load("../data/STATegRa_S2.rda")
> data(STATegRa_S2)
> 
> MAP.SYMBOL<-bioMap(name = "Symbol-miRNA",
+                 metadata =  list(type_v1="Gene",type_v2="miRNA",
+                                  source_database="targetscan.Hs.eg.db",
+                                  data_extraction="July2014"),
+                 map=mapdata)
> 
> 
> #############################################
> ## PART 2: CREATING a bioDist CLASS
> #############################################
> ##### In the second part given a set of main features and surrogate feautres,
> #####    the profile of the main features is computed through the surrogate features.
> 
> # Load Data
> data(STATegRa_S1)
>   #load("../data/STATegRa.S1.Rdata")
> 
> ## Create ExpressionSets
> #  source("../R/STATegRa_omicsPCA_classes_and_methods.R")
> # Block1 - Expression data
> mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname"))
> 
> # Create Gene-gene distance computed through miRNA data
> bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1),     
+              reference = "Var1",
+              mapping = MAP.SYMBOL,
+              surrogateData = miRNA.ds,  ### miRNA data
+              referenceData = mRNA.ds,  ### mRNA data
+              maxitems=2,
+              selectionRule="sd",
+              expfac=NULL,
+              aggregation = "sum",
+              distance = "spearman",
+              noMappingDist = 0,
+              filtering = NULL,
+              name = "mRNAbymiRNA")
> 
> require(Biobase)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
    tapply, union, unique, unsplit, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> 
> # Create Gene-gene distance through mRNA data
> bioDistmRNA<-bioDistclass(name = "mRNAbymRNA",
+                  distance = cor(t(exprs(mRNA.ds)),method="spearman"),
+                  map.name = "id",
+                  map.metadata = list(),
+                  params = list())
> 
> #############################################
> ## PART 3: CREATING a LISTOF WEIGTHED DISTANCES MATRICES: bioDistWList
> #############################################
> 
> bioDistList<-list(bioDistmRNA,bioDistmiRNA)
> weights<-matrix(0,4,2)
> weights[,1]<-c(0,0.33,0.67,1)
> weights[,2]<-c(1,0.67,0.33,0)#
> 
> bioDistWList<-bioDistW(referenceFeatures = rownames(Block1),
+                        bioDistList = bioDistList,
+                        weights=weights)
> length(bioDistWList)
[1] 4
> 
> #############################################
> ## PART 4: DEFINING THE STRENGTH OF ASSOCIATIONS IN GENERAL
> #############################################
> 
> bioDistWPlot(referenceFeatures = rownames(Block1) ,
+                listDistW = bioDistWList,
+                method.cor="spearman")
Warning messages:
1: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
2: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
3: In cor.test.default(getDist(listDistW[[i]])[referenceFeatures, referenceFeatures],  :
  Cannot compute exact p-value with ties
4: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
5: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
6: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
7: In plot.window(...) :
  relative range of values (   0 * EPS) is small (axis 2)
> 
> #############################################
> ## PART 5: DEFINING THE ASSOCIATIONS FOR A GIVEN GENE
> #############################################
> 
> ## IDH1
> 
> IDH1.F<-bioDistFeature(Feature = "IDH1" ,
+                        listDistW = bioDistWList,
+                        threshold.cor=0.7)
> bioDistFeaturePlot(data=IDH1.F)
> 
> ## PDGFRA
> 
> #PDGFRA.F<-bioDistFeature(Feature = "PDGFRA" ,
> #                       listDistW = bioDistWList,
> #                       threshold.cor=0.7)
> #bioDistFeaturePlot(data=PDGFRA.F,name="../vignettes/PDGFRA.png")
> 
> ## EGFR
> #EGFR.F<-bioDistFeature(Feature = "EGFR" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.7)
> #bioDistFeaturePlot(data=EGFR.F,name="../vignettes/EGFR.png")
> 
> ## MGMT
> #MGMT.F<-bioDistFeature(Feature = "MGMT" ,
> #                         listDistW = bioDistWList,
> #                         threshold.cor=0.5)
> #bioDistFeaturePlot(data=MGMT.F,name="../vignettes/MGMT.png")
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
  25.04    0.82   25.86 

R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> rm(list = ls())
> require("STATegRa")
Loading required package: STATegRa
> # Load the data
> data("TCGA_BRCA_Batch_93")
> # Setting dataTypes
> dataTypes <- c("count", "count", "continuous")
> # Setting methods to combine pvalues
> combMethods = c("Fisher", "Liptak", "Tippett")
> # Setting number of permutations
> numPerms = 1000
> # Setting number of cores
> numCores = 1
> # Setting holistOmics to print out the steps that it performs.
> verbose = TRUE
> # Run holistOmics analysis.
> output <- omicsNPC(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes, combMethods = combMethods, numPerms = numPerms, numCores = numCores, verbose = verbose)
Compute initial statistics on data
Building NULL distributions by permuting data
Compute pseudo p-values based on NULL distributions...
NPC p-values calculation...
> 
> proc.time()
   user  system elapsed 
  66.67    0.23   66.89 

R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> rm(list = ls())
> require("STATegRa")
Loading required package: STATegRa
> # Load the data
> data("TCGA_BRCA_Batch_93")
> # Setting dataTypes
> dataTypes <- c("count", "count", "continuous")
> # Setting methods to combine pvalues
> combMethods = c("Fisher", "Liptak", "Tippett")
> # Setting number of permutations
> numPerms = 1000
> # Setting number of cores
> numCores = 1
> # Setting holistOmics to print out the steps that it performs.
> verbose = TRUE
> # Run holistOmics analysis.
> output <- omicsNPC(dataInput = TCGA_BRCA_Data, dataTypes = dataTypes, combMethods = combMethods, numPerms = numPerms, numCores = numCores, verbose = verbose)
Compute initial statistics on data
Building NULL distributions by permuting data
Compute pseudo p-values based on NULL distributions...
NPC p-values calculation...
> 
> proc.time()
   user  system elapsed 
  64.98    0.29   65.26 

R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> ###########################################
> ########### EXAMPLE OF THE OMICSPCA
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
> 
> # g_legend (not exported by STATegRa any more)
> ## code from https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs
> g_legend<-function(a.gplot){
+     tmp <- ggplot_gtable(ggplot_build(a.gplot))
+     leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
+     legend <- tmp$grobs[[leg]]
+     return(legend)}
> 
> #########################
> ## PART 1. Load data
> 
> ## Load data
> data(STATegRa_S3)
> 
> ls()
[1] "Block1.PCA" "Block2.PCA" "ed.PCA"     "g_legend"  
> 
> ## Create ExpressionSets
> # Block1 - Expression data
> B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname"))
> 
> #########################
> ## PART 2.  Model Selection
> 
> require(grid)
Loading required package: grid
> require(gridExtra)
Loading required package: gridExtra
> require(ggplot2)
Loading required package: ggplot2
> 
> ## Select the optimal components
> ms <- modelSelection(Input=list(B1,B2),Rmax=4,fac.sel="single%",varthreshold=0.03,center=TRUE,scale=TRUE,weight=TRUE)
Common components
[1] 2

Distinctive components
[[1]]
[1] 2

[[2]]
[1] 2

> 
> 
> #########################
> ## PART 3. Component Analysis
> 
> ## 3.1 Component analysis of the three methods
> discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                               scale=TRUE,weight=TRUE)
> jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                              scale=TRUE,weight=TRUE)
> o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                               scale=TRUE,weight=TRUE)
> 
> ## 3.2 Exploring scores structures
> 
> # Exploring DISCO-SCA scores structure
> discoRes@scores$common ## Common scores
                      1             2
sample1    0.0781575594 -0.0431548606
sample2   -0.1192221351  0.0294021895
sample3   -0.0531408719 -0.0746837586
sample4    0.0292971804 -0.0006033889
sample5    0.0202090752  0.0110455323
sample6    0.1226088466  0.1053493181
sample7    0.1078931318 -0.0322419135
sample8    0.1782891187  0.1449330434
sample9    0.0468697284 -0.0455171759
sample10  -0.0036032676  0.0420077830
sample11  -0.0035566376 -0.0566285162
sample12   0.1006129669  0.0641393908
sample13  -0.1174412754  0.0907475403
sample14   0.0981203566  0.0617763581
sample15   0.0085337231 -0.0086956217
sample16   0.0783146845  0.1581333476
sample17  -0.1483610653  0.0638580125
sample18  -0.0963084395  0.0556687986
sample19  -0.0217243093 -0.0720128570
sample20  -0.0635633956 -0.0779610247
sample21  -0.0201843956  0.1566382042
sample22   0.0218273803 -0.0764055767
sample23   0.0852039056 -0.0032765078
sample24  -0.1287181413  0.1924426036
sample25  -0.0430575638 -0.0456638644
sample26  -0.1453899763  0.0541458694
sample27  -0.0197483656 -0.1185592761
sample28  -0.1025339397  0.0650655274
sample29   0.0706022417 -0.0682932991
sample30  -0.1295622999 -0.0066677456
sample31   0.1147449325  0.1232727323
sample32  -0.0374308225  0.0380249717
sample33   0.0599520743  0.0136936275
sample34  -0.0984199314  0.0375364671
sample35  -0.0543096420 -0.0378035066
sample36   0.1403628055 -0.0343638695
sample37   0.0228947667 -0.0732689245
sample38  -0.0222073068 -0.0962567254
sample39  -0.0941739218  0.0215180491
sample40   0.0643807064 -0.0687719514
sample41  -0.0327635010 -0.1232187196
sample42  -0.0500431638 -0.0292513799
sample43  -0.0184497193  0.0233044499
sample44   0.1487889421  0.1171209902
sample45  -0.1050778759  0.1123140272
sample46  -0.1151191631 -0.1093995705
sample47  -0.0962591540 -0.0288417980
sample48   0.0004832630 -0.0310379630
sample49   0.1135203919  0.1213936615
sample50  -0.0123549977 -0.1740763092
sample51   0.0550527468  0.1258930875
sample52   0.0499118514  0.0728581032
sample53   0.1119772690  0.1588064377
sample54  -0.0360055712  0.0228585712
sample55   0.0210418827  0.0006750777
sample56  -0.0434171475  0.0633131295
sample57   0.0197820730  0.1150754026
sample58   0.0030440707  0.0326127648
sample59   0.0500256735  0.0129521897
sample60   0.0184280096  0.0136218297
sample61   0.0150298934  0.0635097080
sample62  -0.0304758776 -0.0201235929
sample63   0.1102250137  0.1285968244
sample64   0.1552586841  0.0971185184
sample65  -0.0058503822  0.0207102686
sample66  -0.0025607457  0.0424284574
sample67   0.1546638671 -0.0661577864
sample68   0.0536374227 -0.0923603873
sample69   0.0640332980  0.0082003894
sample70   0.0163521701 -0.0663227228
sample71  -0.0102536142 -0.1345965012
sample72  -0.0654191772 -0.0196035962
sample73  -0.1048553261  0.0221000170
sample74   0.0123800499  0.0586157003
sample75   0.0392079721 -0.0209726118
sample76   0.0648954580 -0.0524759365
sample77   0.1172922660 -0.0201200255
sample78  -0.1463072584  0.0708399398
sample79   0.0265208829 -0.1603425684
sample80   0.0279739193 -0.0214153311
sample81   0.0079212135 -0.0738495580
sample82  -0.1544234601 -0.0361450520
sample83  -0.0494205440 -0.0049938594
sample84  -0.0259039734 -0.0346591622
sample85   0.1116487449 -0.0031403885
sample86  -0.1306479068 -0.0377155810
sample87  -0.0554777860 -0.0459739535
sample88  -0.0301626494  0.0382206824
sample89  -0.1016866182  0.0694078366
sample90   0.0086821634 -0.0201323756
sample91   0.1578629717 -0.2097792379
sample92   0.0170933508 -0.1655936681
sample93  -0.0979805114 -0.0121500481
sample94   0.0131486197 -0.0114929501
sample95   0.0315682449 -0.0758917263
sample96   0.0024125826 -0.0470185271
sample97   0.0634545796  0.0270303634
sample98  -0.0359372573 -0.0135466489
sample99  -0.1009167583  0.1124712534
sample100  0.0551754094  0.0246502129
sample101 -0.0080116038 -0.1627407409
sample102 -0.0046451156  0.0095472750
sample103 -0.0472520891 -0.0940383525
sample104  0.0198157459 -0.0591147260
sample105 -0.0400238984 -0.0160949046
sample106 -0.0923810123  0.0369003857
sample107 -0.1019372381  0.0224967303
sample108 -0.0877091519 -0.0128849754
sample109  0.0864820278 -0.0901081211
sample110 -0.1223116472 -0.0096108519
sample111  0.0257352421 -0.0936281292
sample112 -0.0765285919  0.0270379400
sample113  0.0258799861  0.0377438341
sample114  0.0021141068 -0.0882040153
sample115  0.0303455298 -0.0723735408
sample116  0.0780504483 -0.0685162416
sample117  0.0536894084 -0.0912025380
sample118  0.0666649902 -0.0236261119
sample119  0.1021872531 -0.2325004030
sample120  0.0750216330  0.0243345617
sample121 -0.0756937872  0.0942970348
sample122 -0.0259632042  0.0731921195
sample123 -0.1037844691 -0.0369178672
sample124  0.0611205180  0.0421647001
sample125 -0.0738472616  0.0066944368
sample126  0.0972919116  0.0762698781
sample127  0.0824699422 -0.0096644716
sample128 -0.1249411505  0.0929253705
sample129 -0.0734063712 -0.0434313518
sample130 -0.0003500264 -0.0309857158
sample131  0.0930184045  0.0155970085
sample132  0.0736220615  0.0732972114
sample133 -0.0498398362 -0.0462455902
sample134  0.1644872644  0.0720046906
sample135 -0.0752295055  0.0003869594
sample136  0.0227149942 -0.0495469424
sample137  0.0564721687 -0.0288860564
sample138  0.0255986485 -0.0610930556
sample139  0.0621218795  0.0235857897
sample140 -0.0604148972 -0.0435532387
sample141  0.0246743050  0.0532630381
sample142 -0.0409563846  0.0316234304
sample143 -0.0077356384 -0.0476908799
sample144  0.0173240997 -0.0156785865
sample145  0.0485467759  0.1202737908
sample146  0.0419649952 -0.0811240923
sample147 -0.0977304657 -0.0274771186
sample148  0.0368253309  0.0803969570
sample149 -0.0072864887 -0.1533016959
sample150  0.1020825525  0.0624822556
sample151  0.0305397165 -0.0289337016
sample152 -0.0533595220 -0.0638335148
sample153 -0.0891639361  0.1799453029
sample154 -0.0727554418 -0.0834129412
sample155 -0.0880665838 -0.0220770347
sample156 -0.0276558874 -0.0326601866
sample157 -0.1155031565  0.0183635475
sample158 -0.0281506708 -0.0104911950
sample159  0.0663233797  0.0443809790
sample160 -0.0302644005  0.0404301362
sample161  0.0114712968 -0.0591083082
sample162 -0.1337090971  0.1398131317
sample163  0.1330120782  0.1688769243
sample164 -0.0150338138  0.0028375445
sample165  0.0076518841 -0.0164145789
sample166  0.0367791516  0.0630614243
sample167  0.1111989812  0.0030066421
sample168 -0.0672982961  0.0446266786
sample169 -0.0413003638  0.0224447071
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
                      1             2
sample1    0.0420463577  0.0867866059
sample2    0.0820849288 -0.0410968888
sample3   -0.0155964811 -0.0195186366
sample4    0.1001342695 -0.0410776453
sample5    0.0153479460 -0.0253257740
sample6   -0.0340240864 -0.0408223312
sample7   -0.0722601924  0.0002323977
sample8    0.0457617041 -0.0370006926
sample9    0.0086217301  0.0820184483
sample10   0.0423630532 -0.0083917670
sample11  -0.0022592593  0.0787764107
sample12  -0.0322076905  0.1479823435
sample13   0.0293968565 -0.0306742889
sample14  -0.0337431936 -0.0367508314
sample15  -0.0815559879  0.1275613909
sample16  -0.0508334549  0.0540604431
sample17  -0.0062556109  0.0041024881
sample18  -0.0705601657 -0.0351053292
sample19   0.0476784953 -0.0509595503
sample20  -0.0523025880  0.0715514091
sample21   0.0119248468 -0.0376087062
sample22  -0.0724456644 -0.0095634877
sample23   0.0992529592  0.0134298888
sample24   0.1595262389  0.0728684138
sample25   0.0920661817 -0.0749749350
sample26   0.0595566365  0.0848973624
sample27  -0.0826575140 -0.0086747526
sample28   0.0384832636  0.0440972715
sample29  -0.0777739611  0.1735298547
sample30  -0.1229474212 -0.0819018402
sample31  -0.0579752507 -0.0238646782
sample32  -0.0970366311 -0.0111435064
sample33  -0.1017580226 -0.0630452521
sample34  -0.0637903125  0.0377936259
sample35  -0.0790002972 -0.0229732440
sample36  -0.1224933073 -0.1274968261
sample37  -0.1798846748 -0.1673447932
sample38  -0.0466392096  0.0888153172
sample39   0.0168694521  0.0421536031
sample40  -0.1756417270 -0.1526662260
sample41  -0.0042467276  0.0004924528
sample42   0.0447826075 -0.0651501431
sample43  -0.0482292333 -0.0253533504
sample44   0.1986817297 -0.0545753846
sample45   0.0741916356  0.0054714193
sample46  -0.0478860221 -0.0007080396
sample47  -0.0608216066  0.0481615465
sample48   0.1381465216  0.0578300927
sample49   0.0530628721 -0.1405523582
sample50   0.0173649636  0.1602386053
sample51  -0.0462458853  0.0303473111
sample52  -0.0279997495  0.0280387919
sample53  -0.0667501310  0.0237700239
sample54  -0.0121812109 -0.0521354893
sample55  -0.0182392303  0.0221326648
sample56   0.0001307768  0.0030909316
sample57  -0.0316576942  0.0530190719
sample58  -0.0393891565 -0.0297801758
sample59  -0.1278271753 -0.0546540515
sample60  -0.1486965141  0.1069141959
sample61  -0.0793068740  0.0569790484
sample62  -0.1172821431 -0.0149211123
sample63   0.0028810710  0.1300524178
sample64  -0.0237297809  0.1073288445
sample65   0.0126543400  0.0589810357
sample66   0.0468233111 -0.0771066623
sample67  -0.1494285418 -0.0769877310
sample68  -0.0978022096 -0.0577363801
sample69  -0.0403090432  0.0156038271
sample70  -0.0221596583  0.0315436600
sample71   0.0546332327 -0.0272395045
sample72  -0.1107500699 -0.0537331335
sample73  -0.0906756839  0.0579957928
sample74  -0.0586514322  0.0121417508
sample75  -0.0390512258  0.0349278226
sample76   0.0022940179 -0.1676560114
sample77   0.0232101550 -0.2067300961
sample78   0.0929808646 -0.0434927982
sample79   0.1619383386 -0.0378102630
sample80  -0.0680392310  0.1424655952
sample81   0.0530726852 -0.0358347736
sample82  -0.0266849789 -0.0577449108
sample83  -0.1517241861 -0.0448570035
sample84   0.0570943762 -0.0273808507
sample85  -0.1086271795 -0.1228130362
sample86  -0.0833890930 -0.0442924794
sample87  -0.0022040195 -0.0943908522
sample88   0.0078275810 -0.1140504523
sample89  -0.0611007002 -0.0094589341
sample90  -0.0022941244 -0.0936254883
sample91  -0.0433769528  0.3205972101
sample92   0.1815220326 -0.0334666844
sample93  -0.0267653628  0.0614425801
sample94  -0.0181901028  0.0605088163
sample95   0.0720315643 -0.0013040641
sample96   0.0559673599 -0.0118787196
sample97   0.0217420407  0.0195417196
sample98  -0.0379198864  0.0588352766
sample99   0.0792506488 -0.0151262387
sample100 -0.0222100683 -0.0023322912
sample101  0.0387087200  0.1224225109
sample102  0.2094625816 -0.0516420982
sample103 -0.0138556363  0.0301047601
sample104  0.0807948876 -0.0162712458
sample105  0.0520492016 -0.1229660395
sample106  0.0192642353 -0.0185235167
sample107 -0.0319014478  0.0405120589
sample108  0.0140674510  0.0163422317
sample109  0.1831858436  0.0613023543
sample110  0.0292782803 -0.0199846510
sample111  0.1423175401  0.0327351962
sample112 -0.0426313680 -0.0029087025
sample113  0.0771931543  0.0268742710
sample114  0.0241569474 -0.0184080708
sample115  0.1958957279  0.0460148449
sample116  0.1394438228 -0.0530793595
sample117  0.1672312897 -0.1386522085
sample118  0.0448331858 -0.0117618016
sample119  0.0910196421  0.2217435627
sample120  0.0331404708 -0.0057270344
sample121 -0.0307517683  0.1392506252
sample122  0.0839836880 -0.0291983638
sample123 -0.0239674868 -0.0642167410
sample124  0.0909175960  0.0130430089
sample125  0.0065362311 -0.1092631027
sample126 -0.0935273988  0.1368276909
sample127 -0.0035405469  0.0292755003
sample128  0.0660349350  0.1018575780
sample129 -0.0693670623 -0.0695430246
sample130 -0.0008516736 -0.0669705407
sample131 -0.0431012124  0.0174061040
sample132  0.0637088383  0.0029383499
sample133  0.0289465044 -0.0390817336
sample134 -0.0446142842  0.0456332369
sample135 -0.0712343748  0.0521627632
sample136 -0.0596317916  0.0197291693
sample137 -0.0793175197 -0.0380637267
sample138  0.0973506152 -0.0454210200
sample139 -0.0539866405 -0.1534332065
sample140 -0.0850871676  0.0955804455
sample141  0.0192723439 -0.0554446463
sample142  0.0672294080 -0.0461313097
sample143  0.0303707455 -0.0519258571
sample144  0.0089350720  0.0145815357
sample145  0.0638875689  0.0122268763
sample146 -0.0585921865  0.0063074889
sample147 -0.0894146558 -0.1124625770
sample148  0.0216438891 -0.0615962380
sample149  0.0515317702 -0.0839902945
sample150 -0.0568229364 -0.0124472677
sample151  0.0789513903 -0.0261823995
sample152  0.0330693871  0.1306444955
sample153  0.1752063686  0.1497755501
sample154 -0.0421489053 -0.0037017136
sample155 -0.0680198592  0.0095703524
sample156 -0.0388949894  0.1057557950
sample157 -0.0314765204  0.0561364661
sample158 -0.0329630178  0.0353943629
sample159  0.0398461235 -0.1007368283
sample160 -0.0424906134  0.0108493088
sample161  0.0888340652 -0.0679692884
sample162  0.0027569708  0.1237848342
sample163  0.0126228043  0.0725440958
sample164  0.0566787171 -0.0458318318
sample165  0.0315331645 -0.0236359598
sample166  0.0612108768 -0.0425224830
sample167 -0.0142729572  0.0179307004
sample168  0.0169542663 -0.0769614864
sample169 -0.0675063582  0.0131499110
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
                      1             2
sample1   -0.0012331670 -1.635716e-01
sample2   -0.0724353217 -6.022123e-03
sample3   -0.0188459942 -1.080029e-01
sample4    0.0390143129  3.106568e-04
sample5    0.1774810626 -2.996429e-02
sample6   -0.0451446469 -3.455898e-02
sample7   -0.0226463479 -7.019216e-03
sample8   -0.1033684567 -9.857940e-03
sample9    0.1350014227  8.979113e-02
sample10   0.1259884412 -5.097937e-02
sample11   0.0979790932  7.086566e-02
sample12  -0.0863020996 -8.620322e-02
sample13  -0.1381401831  1.827998e-01
sample14  -0.0615074742 -2.642808e-02
sample15   0.0381600585 -3.101602e-02
sample16  -0.0048779406  1.271028e-03
sample17  -0.0788483235 -1.547605e-02
sample18  -0.0884189504 -3.795477e-02
sample19   0.0703043519 -1.084003e-01
sample20  -0.0025581342  7.975969e-02
sample21   0.0941596620 -4.126892e-02
sample22  -0.0550270857 -7.806618e-02
sample23   0.0679492816 -4.102077e-02
sample24  -0.1310969457  1.649282e-01
sample25   0.0113583566 -4.426900e-02
sample26  -0.1402948918  2.016460e-02
sample27   0.0261566059  1.589951e-03
sample28  -0.0724200791  5.850511e-02
sample29  -0.0330054762  2.062070e-03
sample30  -0.0228750331 -2.015345e-02
sample31  -0.0635070421 -6.670369e-02
sample32   0.0685099998 -4.955246e-02
sample33  -0.0777764932 -1.272070e-01
sample34   0.0157842079 -3.024311e-02
sample35  -0.0529627918  1.500981e-01
sample36   0.0070908002  2.025321e-01
sample37  -0.0442411778  1.802109e-01
sample38  -0.0781508370 -3.676299e-02
sample39   0.0120330062 -3.388883e-02
sample40  -0.0473283815  1.471581e-01
sample41   0.0228192202 -2.673459e-02
sample42  -0.0245361862 -7.960877e-02
sample43   0.1036362007 -8.229578e-02
sample44  -0.1012234789  7.049242e-02
sample45   0.0013726678 -2.451068e-02
sample46  -0.0558506441  2.948576e-03
sample47  -0.0380478711  4.554236e-02
sample48   0.0784340466  4.888892e-02
sample49  -0.0605168082 -1.162470e-02
sample50   0.0530082929 -2.737814e-02
sample51   0.1514645344  5.678261e-02
sample52   0.1860936012  1.246711e-01
sample53  -0.0064179721 -2.701060e-02
sample54   0.0697037560 -2.308412e-02
sample55   0.1633577707  1.366433e-02
sample56   0.1011483993  4.682134e-02
sample57   0.1730374403  1.609594e-01
sample58  -0.0071384892 -1.666951e-02
sample59  -0.0030458470  3.005375e-02
sample60   0.0215842207  2.665887e-01
sample61   0.1510585334  1.002384e-01
sample62  -0.0925531584 -4.845729e-02
sample63  -0.0596315455 -4.137108e-02
sample64  -0.0449227291 -2.600958e-03
sample65   0.0939382242 -4.406950e-02
sample66   0.1063397729 -5.710077e-02
sample67  -0.0201580787  2.361746e-01
sample68   0.0037208403  2.418541e-02
sample69  -0.0645162003 -1.155618e-01
sample70  -0.1013439748 -1.351780e-01
sample71  -0.0016466093 -2.976774e-02
sample72   0.0328895409 -2.835772e-02
sample73   0.0275080383 -5.148153e-02
sample74   0.1341718359 -7.895303e-02
sample75   0.0951576645 -3.943148e-02
sample76  -0.0864719936  3.035053e-02
sample77  -0.1035749499 -2.545325e-02
sample78  -0.1575647877  4.939476e-02
sample79   0.0189138393  4.874690e-02
sample80   0.1384142772  4.314447e-05
sample81  -0.0118846660 -6.357908e-02
sample82  -0.1675306634  3.533969e-02
sample83  -0.0065671156 -7.812498e-02
sample84   0.1486890661 -3.109096e-02
sample85  -0.0532720335  7.417987e-02
sample86  -0.1138474912 -1.820827e-05
sample87   0.0432865958  6.080499e-02
sample88   0.0433451201  1.402486e-01
sample89   0.0331204794 -1.395428e-02
sample90  -0.0607413478 -8.610386e-02
sample91  -0.0566263727  1.303770e-01
sample92  -0.0359580677  1.061605e-01
sample93  -0.0433646459 -4.443610e-02
sample94  -0.0477292133 -1.059571e-01
sample95  -0.0249595919 -3.980510e-02
sample96   0.0035217580 -9.293931e-02
sample97  -0.0066052012 -1.527234e-01
sample98   0.0020367062 -5.579516e-02
sample99  -0.0886621741 -3.728373e-02
sample100 -0.1091259606 -3.560401e-02
sample101 -0.0739723823 -4.317886e-02
sample102  0.0574455709 -2.784084e-02
sample103  0.0142733757  9.706345e-03
sample104  0.0710395590  4.068331e-02
sample105  0.0980829944 -3.452996e-02
sample106 -0.0254260496  3.628934e-02
sample107 -0.0160655047 -9.173398e-02
sample108 -0.0200988312 -2.379699e-02
sample109 -0.0389781932  1.692312e-02
sample110 -0.0326305249  2.988087e-02
sample111  0.0676935943 -6.038249e-02
sample112  0.0167883508  5.336924e-03
sample113  0.0969213952 -2.757702e-02
sample114 -0.0026397972 -9.209102e-02
sample115 -0.0308049558  1.603745e-02
sample116 -0.1240306380  1.272998e-01
sample117  0.0334728665  5.392663e-02
sample118 -0.1037152152  6.252440e-02
sample119 -0.1064170565  1.196217e-01
sample120 -0.0771357713 -1.004935e-01
sample121 -0.0129352324  3.181915e-02
sample122  0.0847487553 -5.568462e-02
sample123 -0.0041335515  7.693549e-03
sample124 -0.0583462208 -8.396475e-02
sample125  0.0634843251 -5.232567e-02
sample126 -0.0662582110 -1.091730e-01
sample127 -0.0865025619 -1.094172e-01
sample128 -0.0627822039 -1.471092e-02
sample129 -0.0336274588 -4.007775e-02
sample130 -0.0293518113 -8.046087e-02
sample131 -0.0469196794 -2.209388e-03
sample132 -0.0241745641 -1.248608e-01
sample133  0.0907303804  1.466698e-02
sample134 -0.0350841234  7.539660e-02
sample135  0.0001334879  9.185814e-03
sample136 -0.0335874807 -9.860182e-02
sample137 -0.0640147272 -7.554372e-02
sample138  0.0060964055 -1.742782e-02
sample139 -0.0592082759  5.615006e-02
sample140  0.0427988603 -1.099467e-02
sample141  0.0618793268 -9.301101e-02
sample142  0.0898552510  3.573325e-02
sample143  0.0817391072  8.880528e-02
sample144  0.0787754475 -3.821395e-02
sample145  0.1085819510  1.569461e-01
sample146 -0.0589555000 -4.373238e-02
sample147 -0.0495327911  7.278052e-03
sample148  0.1161590484  9.078145e-03
sample149 -0.0121575490  7.788461e-02
sample150 -0.0314511981  3.520220e-02
sample151  0.0575380950 -1.945392e-02
sample152 -0.0494540354  7.025565e-02
sample153 -0.0941338540  2.153270e-01
sample154 -0.0335928826  2.078824e-02
sample155  0.0690459043 -2.780362e-02
sample156  0.1039902300 -6.292489e-02
sample157 -0.0408645845  8.065530e-03
sample158  0.1018106341  7.817019e-03
sample159 -0.0281732528 -1.207259e-02
sample160  0.1643052860  2.977812e-03
sample161  0.0374330092  8.524589e-02
sample162 -0.0804538274  8.349637e-02
sample163 -0.0743232404 -1.406345e-02
sample164  0.1208804282 -2.139523e-02
sample165  0.1608115957  2.025159e-02
sample166 -0.0425947933 -2.660800e-02
sample167 -0.0226849508 -4.464258e-02
sample168 -0.0180737368 -7.471482e-04
sample169  0.0190780205  2.645426e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
                   [,1]          [,2]
sample1   -0.0572060227 -1.729087e-02
sample2    0.0875245208  1.112588e-02
sample3    0.0403482602 -3.168994e-02
sample4   -0.0218345996  4.052760e-06
sample5   -0.0150905011  4.795041e-03
sample6   -0.0924362933  4.511003e-02
sample7   -0.0793066751 -1.243823e-02
sample8   -0.1342997187  6.215220e-02
sample9   -0.0338886944 -1.854401e-02
sample10   0.0020547173  1.749421e-02
sample11   0.0037275602 -2.364116e-02
sample12  -0.0753094533  2.772698e-02
sample13   0.0856160091  3.679963e-02
sample14  -0.0737457307  2.668452e-02
sample15  -0.0062111746 -3.554864e-03
sample16  -0.0602355268  6.675115e-02
sample17   0.1086768843  2.524534e-02
sample18   0.0702999472  2.231671e-02
sample19   0.0173785882 -3.024846e-02
sample20   0.0484173812 -3.310904e-02
sample21   0.0124657042  6.517144e-02
sample22  -0.0140989936 -3.159137e-02
sample23  -0.0627028403 -5.393710e-04
sample24   0.0919972100  7.909297e-02
sample25   0.0326998483 -1.945206e-02
sample26   0.1064741246  2.120849e-02
sample27   0.0166058995 -4.964993e-02
sample28   0.0743504770  2.614211e-02
sample29  -0.0511008491 -2.782647e-02
sample30   0.0962250842 -3.974893e-03
sample31  -0.0869563008  5.250819e-02
sample32   0.0271858919  1.552005e-02
sample33  -0.0448364581  6.243160e-03
sample34   0.0718415218  1.469396e-02
sample35   0.0403086451 -1.632629e-02
sample36  -0.1036402827 -1.304320e-02
sample37  -0.0159385744 -3.036525e-02
sample38   0.0182198369 -4.034805e-02
sample39   0.0690363619  8.058350e-03
sample40  -0.0467312750 -2.810325e-02
sample41   0.0263674438 -5.171216e-02
sample42   0.0374578960 -1.268634e-02
sample43   0.0132336869  9.536642e-03
sample44  -0.1119154428  5.028683e-02
sample45   0.0759639367  4.587903e-02
sample46   0.0871885519 -4.670385e-02
sample47   0.0721490571 -1.288540e-02
sample48   0.0005086144 -1.290565e-02
sample49  -0.0858177028  5.173760e-02
sample50   0.0118992665 -7.276215e-02
sample51  -0.0426446855  5.306205e-02
sample52  -0.0381605826  3.086785e-02
sample53  -0.0855757630  6.730043e-02
sample54   0.0261723092  9.184260e-03
sample55  -0.0156418304  4.682404e-04
sample56   0.0307831193  2.597550e-02
sample57  -0.0157242103  4.829381e-02
sample58  -0.0031174404  1.359898e-02
sample59  -0.0373001859  5.868397e-03
sample60  -0.0142609099  5.831654e-03
sample61  -0.0122255144  2.663579e-02
sample62   0.0228002942 -8.692265e-03
sample63  -0.0833127581  5.473229e-02
sample64  -0.1166548159  4.196500e-02
sample65   0.0038808902  8.568590e-03
sample66   0.0011561811  1.766612e-02
sample67  -0.1129311062 -2.608702e-02
sample68  -0.0382526429 -3.804045e-02
sample69  -0.0476502440  4.003241e-03
sample70  -0.0110329882 -2.752719e-02
sample71   0.0096850282 -5.627056e-02
sample72   0.0487124704 -8.800131e-03
sample73   0.0773058132  8.239864e-03
sample74  -0.0102488176  2.454957e-02
sample75  -0.0286613976 -8.387293e-03
sample76  -0.0472655595 -2.129315e-02
sample77  -0.0865043074 -7.296820e-03
sample78   0.1070293698  2.818346e-02
sample79  -0.0165060681 -6.659721e-02
sample80  -0.0206765949 -8.712112e-03
sample81  -0.0050943615 -3.079175e-02
sample82   0.1153622361 -1.647054e-02
sample83   0.0367979217 -2.538114e-03
sample84   0.0199463070 -1.468961e-02
sample85  -0.0827122185 -2.709824e-04
sample86   0.0969487314 -1.699897e-02
sample87   0.0421957457 -1.965953e-02
sample88   0.0215934743  1.566050e-02
sample89   0.0751559502  2.811652e-02
sample90  -0.0057328000 -8.283795e-03
sample91  -0.1134005268 -8.603522e-02
sample92  -0.0101689918 -6.894992e-02
sample93   0.0725967502 -6.003176e-03
sample94  -0.0096878852 -4.693081e-03
sample95  -0.0223502239 -3.139636e-02
sample96  -0.0013232863 -1.963604e-02
sample97  -0.0476541710  1.183660e-02
sample98   0.0269546160 -5.978398e-03
sample99   0.0728179461  4.597884e-02
sample100 -0.0413398038  1.079347e-02
sample101  0.0087536994 -6.796076e-02
sample102  0.0032509529  3.932612e-03
sample103  0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105  0.0294940465 -7.140722e-03
sample106  0.0686472054  1.462895e-02
sample107  0.0748635927  8.401339e-03
sample108  0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110  0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112  0.0570870472  1.066018e-02
sample113 -0.0200110554  1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586  1.089843e-02
sample121  0.0542443861  3.861344e-02
sample122  0.0178575357  3.027138e-02
sample123  0.0775020581 -1.636852e-02
sample124 -0.0460701050  1.814758e-02
sample125  0.0543846585  2.075898e-03
sample126 -0.0729417144  3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128  0.0908136899  3.758801e-02
sample129  0.0552445878 -1.879062e-02
sample130  0.0007128089 -1.294308e-02
sample131 -0.0693311345  7.357082e-03
sample132 -0.0556565156  3.126995e-02
sample133  0.0375870104 -1.977240e-02
sample134 -0.1229130924  3.159495e-02
sample135  0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201  1.040683e-02
sample140  0.0452288969 -1.876279e-02
sample141 -0.0189142561  2.247042e-02
sample142  0.0297545566  1.280524e-02
sample143  0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491  5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147  0.0726083535 -1.239968e-02
sample148 -0.0284795794  3.389732e-02
sample149  0.0082261455 -6.399305e-02
sample150 -0.0765013197  2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152  0.0403422737 -2.714879e-02
sample153  0.0629117719  7.425085e-02
sample154  0.0551622927 -3.548984e-02
sample155  0.0654439133 -1.005306e-02
sample156  0.0209310714 -1.390213e-02
sample157  0.0851522597  6.577150e-03
sample158  0.0208354599 -4.663078e-03
sample159 -0.0498794349  1.913257e-02
sample160  0.0216074437  1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162  0.0963663017  5.705881e-02
sample163 -0.1009542191  7.174224e-02
sample164  0.0109881996  1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357  2.673084e-02
sample167 -0.0825048036  2.278863e-03
sample168  0.0486147429  1.793843e-02
sample169  0.0302506727  8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
                   [,1]          [,2]
sample1   -0.0621842115 -1.364509e-02
sample2    0.0944623785  9.720892e-03
sample3    0.0406196267 -2.236338e-02
sample4   -0.0229316496 -3.932487e-04
sample5   -0.0157330047  3.231033e-03
sample6   -0.0945794025  3.120720e-02
sample7   -0.0854427118 -1.052880e-02
sample8   -0.1376625920  4.286608e-02
sample9   -0.0377115311 -1.415134e-02
sample10   0.0035244506  1.280825e-02
sample11   0.0016639987 -1.717895e-02
sample12  -0.0781403168  1.884368e-02
sample13   0.0938400516  2.838858e-02
sample14  -0.0759839772  1.810989e-02
sample15  -0.0068340837 -2.705361e-03
sample16  -0.0590150849  4.757848e-02
sample17   0.1178805097  2.040526e-02
sample18   0.0767858320  1.756604e-02
sample19   0.0157112113 -2.172867e-02
sample20   0.0485318300 -2.327033e-02
sample21   0.0185928176  4.777095e-02
sample22  -0.0191358702 -2.329775e-02
sample23  -0.0672994194 -1.535656e-03
sample24   0.1047476642  5.935707e-02
sample25   0.0329844953 -1.358036e-02
sample26   0.1154952052  1.741529e-02
sample27   0.0133849853 -3.590922e-02
sample28   0.0821554039  2.042376e-02
sample29  -0.0567643690 -2.123848e-02
sample30   0.1016073931 -1.134728e-03
sample31  -0.0880396372  3.670548e-02
sample32   0.0300363338  1.182406e-02
sample33  -0.0467252272  3.739254e-03
sample34   0.0783666394  1.203777e-02
sample35   0.0424227097 -1.118559e-02
sample36  -0.1107646166 -1.143464e-02
sample37  -0.0191667664 -2.246060e-02
sample38   0.0155968095 -2.909621e-02
sample39   0.0746847148  7.148218e-03
sample40  -0.0517028178 -2.137267e-02
sample41   0.0234979494 -3.723018e-02
sample42   0.0388797356 -8.557228e-03
sample43   0.0149555568  7.210002e-03
sample44  -0.1150305613  3.461805e-02
sample45   0.0846146236  3.486020e-02
sample46   0.0884426404 -3.246853e-02
sample47   0.0748644971 -8.083045e-03
sample48  -0.0012033198 -9.403647e-03
sample49  -0.0872662737  3.616245e-02
sample50   0.0066941314 -5.284863e-02
sample51  -0.0411777630  3.791830e-02
sample52  -0.0379355780  2.180834e-02
sample53  -0.0851639886  4.751761e-02
sample54   0.0288006248  7.184424e-03
sample55  -0.0164920835  5.919925e-05
sample56   0.0355115616  1.951043e-02
sample57  -0.0141146068  3.492409e-02
sample58  -0.0015636132  9.862883e-03
sample59  -0.0390656483  3.590929e-03
sample60  -0.0139454780  3.963030e-03
sample61  -0.0106410274  1.919705e-02
sample62   0.0236748439 -5.922677e-03
sample63  -0.0846790877  3.839102e-02
sample64  -0.1202581015  2.846469e-02
sample65   0.0050548584  6.328644e-03
sample66   0.0028013072  1.291807e-02
sample67  -0.1231623009 -2.112565e-02
sample68  -0.0437782161 -2.845072e-02
sample69  -0.0501199692  2.053469e-03
sample70  -0.0140278645 -2.027157e-02
sample71   0.0057489505 -4.085977e-02
sample72   0.0511212704 -5.522408e-03
sample73   0.0828141409  7.431582e-03
sample74  -0.0085959456  1.772951e-02
sample75  -0.0312180394 -6.636869e-03
sample76  -0.0519051781 -1.640191e-02
sample77  -0.0925924762 -6.907800e-03
sample78   0.1163971046  2.251122e-02
sample79  -0.0240906926 -4.887766e-02
sample80  -0.0221327065 -6.730703e-03
sample81  -0.0072114968 -2.254399e-02
sample82   0.1204416674 -9.907422e-03
sample83   0.0386739485 -1.171663e-03
sample84   0.0195988488 -1.033806e-02
sample85  -0.0877680171 -1.725057e-03
sample86   0.1023541048 -1.062501e-02
sample87   0.0425213089 -1.356865e-02
sample88   0.0244788514  1.180820e-02
sample89   0.0804276691  2.188588e-02
sample90  -0.0074639871 -6.140721e-03
sample91  -0.1278832404 -6.485140e-02
sample92  -0.0162199697 -5.048358e-02
sample93   0.0769344893 -3.045135e-03
sample94  -0.0104345587 -3.593172e-03
sample95  -0.0260058453 -2.330475e-02
sample96  -0.0025018700 -1.433516e-02
sample97  -0.0492358305  7.774183e-03
sample98   0.0279220220 -3.862141e-03
sample99   0.0813921923  3.487339e-02
sample100 -0.0428797405  7.112807e-03
sample101  0.0032855240 -4.940743e-02
sample102  0.0038439317  2.938008e-03
sample103  0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105  0.0314853405 -4.656633e-03
sample106  0.0726456731  1.192390e-02
sample107  0.0807342975  7.508627e-03
sample108  0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110  0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112  0.0599954082  8.820317e-03
sample113 -0.0195006577  1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131  6.937326e-03
sample121  0.0613899126  2.915307e-02
sample122  0.0218424338  2.241775e-02
sample123  0.0809008460 -1.051759e-02
sample124 -0.0472109313  1.239887e-02
sample125  0.0583180947  2.521167e-03
sample126 -0.0753941872  2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128  0.1000212216  2.908091e-02
sample129  0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877  4.091672e-03
sample132 -0.0566219024  2.179861e-02
sample133  0.0384172955 -1.372840e-02
sample134 -0.1280862736  2.077912e-02
sample135  0.0592633273  6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861  6.729304e-03
sample140  0.0468926306 -1.285498e-02
sample141 -0.0186248693  1.605439e-02
sample142  0.0328031246  9.887746e-03
sample143  0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310  3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147  0.0765025670 -7.714769e-03
sample148 -0.0276016641  2.420589e-02
sample149  0.0027545308 -4.653007e-02
sample150 -0.0792296010  1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152  0.0409796547 -1.907063e-02
sample153  0.0734301757  5.528780e-02
sample154  0.0557740684 -2.487723e-02
sample155  0.0689436560 -6.127635e-03
sample156  0.0212272938 -9.747423e-03
sample157  0.0911931194  6.355708e-03
sample158  0.0220840645 -3.016357e-03
sample159 -0.0513244242  1.304175e-02
sample160  0.0246213576  1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162  0.1078802043  4.337260e-02
sample163 -0.1017965082  5.047171e-02
sample164  0.0119430799  9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180  1.899222e-02
sample167 -0.0872832229  1.516582e-04
sample168  0.0540714512  1.397701e-02
sample169  0.0328432652  7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
                   [,1]          [,2]
sample1    0.0133684846  2.195848e-02
sample2    0.0254157197 -1.058416e-02
sample3   -0.0049551479 -4.840017e-03
sample4    0.0310390570 -1.063929e-02
sample5    0.0046941318 -6.488426e-03
sample6   -0.0107406753 -1.026702e-02
sample7   -0.0225157631  2.624712e-04
sample8    0.0141320952 -9.505821e-03
sample9    0.0029681280  2.078210e-02
sample10   0.0131729174 -2.275042e-03
sample11  -0.0004164298  1.994019e-02
sample12  -0.0095211620  3.759883e-02
sample13   0.0091018604 -7.953956e-03
sample14  -0.0106557524 -9.181659e-03
sample15  -0.0249924121  3.262724e-02
sample16  -0.0156216400  1.375700e-02
sample17  -0.0019382446  1.073994e-03
sample18  -0.0221072481 -8.703592e-03
sample19   0.0146917619 -1.311712e-02
sample20  -0.0160353760  1.826290e-02
sample21   0.0035947899 -9.616341e-03
sample22  -0.0225060762 -2.532589e-03
sample23   0.0310000683  3.033060e-03
sample24   0.0499544372  1.809450e-02
sample25   0.0284442301 -1.932558e-02
sample26   0.0188220043  2.146985e-02
sample27  -0.0257763219 -1.999228e-03
sample28   0.0120888648  1.125834e-02
sample29  -0.0236482520  4.426726e-02
sample30  -0.0385486305 -2.055935e-02
sample31  -0.0181539336 -5.877838e-03
sample32  -0.0302630460 -2.607192e-03
sample33  -0.0319565715 -1.562628e-02
sample34  -0.0197970124  9.906813e-03
sample35  -0.0247412713 -5.434440e-03
sample36  -0.0386259060 -3.190394e-02
sample37  -0.0566199273 -4.192574e-02
sample38  -0.0142060273  2.259644e-02
sample39   0.0053589035  1.076485e-02
sample40  -0.0552546493 -3.819896e-02
sample41  -0.0013089975  9.278818e-05
sample42   0.0137252142 -1.664652e-02
sample43  -0.0151259626 -6.290953e-03
sample44   0.0617391754 -1.442883e-02
sample45   0.0231410886  1.163143e-03
sample46  -0.0148898209 -1.384176e-04
sample47  -0.0187252536  1.221690e-02
sample48   0.0432839432  1.416671e-02
sample49   0.0160818605 -3.588745e-02
sample50   0.0059333545  4.067003e-02
sample51  -0.0142914866  7.776270e-03
sample52  -0.0086339952  7.208917e-03
sample53  -0.0207386980  6.272432e-03
sample54  -0.0039856719 -1.316934e-02
sample55  -0.0056217017  5.692315e-03
sample56   0.0000123292  8.978290e-04
sample57  -0.0095805555  1.324253e-02
sample58  -0.0124160295 -7.326376e-03
sample59  -0.0400195442 -1.349736e-02
sample60  -0.0460063358  2.770091e-02
sample61  -0.0245266456  1.470710e-02
sample62  -0.0366022783 -3.437352e-03
sample63   0.0013742171  3.288796e-02
sample64  -0.0070599859  2.739588e-02
sample65   0.0041201911  1.498268e-02
sample66   0.0143173351 -1.968812e-02
sample67  -0.0467477531 -1.929938e-02
sample68  -0.0306751978 -1.436184e-02
sample69  -0.0125317217  4.130407e-03
sample70  -0.0068071487  8.080857e-03
sample71   0.0169170264 -7.027348e-03
sample72  -0.0346909749 -1.333770e-02
sample73  -0.0280506153  1.493843e-02
sample74  -0.0182611498  3.294697e-03
sample75  -0.0120563964  8.974612e-03
sample76   0.0001437236 -4.253184e-02
sample77   0.0065330299 -5.252886e-02
sample78   0.0288278141 -1.127782e-02
sample79   0.0503961481 -1.023318e-02
sample80  -0.0207693429  3.648391e-02
sample81   0.0163562768 -9.074596e-03
sample82  -0.0084317129 -1.478976e-02
sample83  -0.0474097918 -1.103126e-02
sample84   0.0177181395 -7.191197e-03
sample85  -0.0342718548 -3.082360e-02
sample86  -0.0261671791 -1.089491e-02
sample87  -0.0009486358 -2.411514e-02
sample88   0.0020528931 -2.894615e-02
sample89  -0.0189361111 -2.638639e-03
sample90  -0.0009863658 -2.390075e-02
sample91  -0.0124352695  8.153234e-02
sample92   0.0564264106 -8.909537e-03
sample93  -0.0081461774  1.570851e-02
sample94  -0.0054896581  1.547251e-02
sample95   0.0224073150 -4.374348e-04
sample96   0.0173528924 -3.050441e-03
sample97   0.0067948115  5.008237e-03
sample98  -0.0116030825  1.498764e-02
sample99   0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101  0.0124923691  3.091503e-02
sample102  0.0650835386 -1.367400e-02
sample103 -0.0042741828  7.855985e-03
sample104  0.0250591040 -4.171938e-03
sample105  0.0157516368 -3.121990e-02
sample106  0.0060593853 -5.101693e-03
sample107 -0.0098329626  1.044506e-02
sample108  0.0044269853  4.142036e-03
sample109  0.0572473486  1.517542e-02
sample110  0.0090474827 -5.119868e-03
sample111  0.0444263015  7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113  0.0241047399  6.706740e-03
sample114  0.0074558775 -4.728652e-03
sample115  0.0611851433  1.117210e-02
sample116  0.0432646951 -1.380556e-02
sample117  0.0516750066 -3.575617e-02
sample118  0.0139942100 -3.279138e-03
sample119  0.0291722987  5.587946e-02
sample120  0.0103515853 -1.690016e-03
sample121 -0.0091396331  3.552116e-02
sample122  0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124  0.0283466326  3.127845e-03
sample125  0.0016472378 -2.770692e-02
sample126 -0.0286529417  3.489336e-02
sample127 -0.0010224500  7.483214e-03
sample128  0.0209049296  2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968  4.637390e-03
sample132  0.0198526786  5.723983e-04
sample133  0.0088812957 -9.988115e-03
sample134 -0.0137484514  1.172591e-02
sample135 -0.0220314568  1.347465e-02
sample136 -0.0185173353  5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138  0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762  2.456863e-02
sample141  0.0058369763 -1.420854e-02
sample142  0.0207886071 -1.188764e-02
sample143  0.0092832598 -1.324238e-02
sample144  0.0028442140  3.627979e-03
sample145  0.0199749569  2.862202e-03
sample146 -0.0182236697  1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148  0.0065435868 -1.572917e-02
sample149  0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151  0.0245166984 -6.888241e-03
sample152  0.0107259913  3.314630e-02
sample153  0.0550963965  3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574  2.642246e-03
sample156 -0.0117803505  2.698265e-02
sample157 -0.0096167165  1.433840e-02
sample158 -0.0101754772  9.137620e-03
sample159  0.0120662931 -2.565236e-02
sample160 -0.0132238202  2.916023e-03
sample161  0.0274491966 -1.748284e-02
sample162  0.0012482909  3.152261e-02
sample163  0.0042031315  1.830701e-02
sample164  0.0174896157 -1.175915e-02
sample165  0.0097517662 -6.119019e-03
sample166  0.0190134679 -1.121582e-02
sample167 -0.0044140836  4.665585e-03
sample168  0.0049689168 -1.941822e-02
sample169 -0.0209802098  3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
                   [,1]          [,2]
sample1   -0.0515543627 -0.0305856787
sample2   -0.0144993256  0.0236342950
sample3   -0.0371833108 -0.0140263348
sample4    0.0068945388 -0.0132539692
sample5    0.0215035333 -0.0663338101
sample6   -0.0187055152  0.0088773016
sample7   -0.0061521552  0.0064029054
sample8   -0.0210874459  0.0334652901
sample9    0.0516865043 -0.0291142799
sample10   0.0059440366 -0.0527217447
sample11   0.0393010793 -0.0200624712
sample12  -0.0420837100  0.0131331362
sample13   0.0333252565  0.0818552509
sample14  -0.0190062644  0.0160202175
sample15  -0.0030968049 -0.0189230681
sample16  -0.0004452158  0.0018880102
sample17  -0.0185848615  0.0240170131
sample18  -0.0273093598  0.0230213640
sample19  -0.0217761111 -0.0445894441
sample20   0.0245820821  0.0159812738
sample21   0.0034527644 -0.0400016054
sample22  -0.0340789054  0.0039289109
sample23  -0.0010344929 -0.0310161212
sample24   0.0289468503  0.0760962436
sample25  -0.0119098496 -0.0122798760
sample26  -0.0181001057  0.0517892852
sample27   0.0050465417 -0.0086515844
sample28   0.0057491502  0.0358830107
sample29  -0.0051104246  0.0116605117
sample30  -0.0103085904  0.0039678538
sample31  -0.0319929858  0.0090606113
sample32  -0.0036232521 -0.0328202010
sample33  -0.0534742153  0.0024751837
sample34  -0.0067495749 -0.0111000311
sample35   0.0378745721  0.0465929296
sample36   0.0647886800  0.0359987924
sample37   0.0488441236  0.0492906912
sample38  -0.0251514062  0.0197110110
sample39  -0.0085428066 -0.0105117852
sample40   0.0379324087  0.0440810741
sample41  -0.0044199152 -0.0128820644
sample42  -0.0292553573 -0.0067045265
sample43  -0.0077829155 -0.0510178219
sample44   0.0045122248  0.0479660309
sample45  -0.0074444298 -0.0051116726
sample46  -0.0088025512  0.0196186661
sample47   0.0076696301  0.0215947965
sample48   0.0290108585 -0.0175568376
sample49  -0.0141754858  0.0184717099
sample50   0.0006282201 -0.0233054373
sample51   0.0441995177 -0.0410022921
sample52   0.0715329391 -0.0399499475
sample53  -0.0095954087 -0.0029140909
sample54   0.0048933768 -0.0281884386
sample55   0.0327325487 -0.0532290012
sample56   0.0323068984 -0.0256595538
sample57   0.0806603122 -0.0286748097
sample58  -0.0064792049 -0.0006945349
sample59   0.0088958941  0.0067389649
sample60   0.0874124612  0.0431964341
sample61   0.0577604571 -0.0326112099
sample62  -0.0313318464  0.0224391756
sample63  -0.0233625220  0.0125110562
sample64  -0.0086426068  0.0148770341
sample65   0.0025256193 -0.0404466327
sample66   0.0006014071 -0.0471576264
sample67   0.0706087042  0.0516228406
sample68   0.0082301011  0.0033109509
sample69  -0.0475076743  0.0001452708
sample70  -0.0600773716  0.0089986962
sample71  -0.0096321627 -0.0050761187
sample72  -0.0031773546 -0.0166221542
sample73  -0.0113700517 -0.0191726684
sample74  -0.0014179662 -0.0608101325
sample75   0.0041911740 -0.0399981269
sample76  -0.0055326449  0.0353114263
sample77  -0.0260214459  0.0305731380
sample78  -0.0119267436  0.0632236007
sample79   0.0186017239  0.0027402910
sample80   0.0241047889 -0.0472697181
sample81  -0.0220288317 -0.0079577210
sample82  -0.0180751258  0.0639051029
sample83  -0.0256671713 -0.0125898269
sample84   0.0161392598 -0.0567222449
sample85   0.0139988188  0.0322763454
sample86  -0.0198382995  0.0389225776
sample87   0.0266270281 -0.0032979996
sample88   0.0515677078  0.0117902495
sample89   0.0014022125 -0.0140510488
sample90  -0.0375949749  0.0044004551
sample91   0.0310397965  0.0440610926
sample92   0.0270570567  0.0324380452
sample93  -0.0215009202  0.0063993941
sample94  -0.0415702912 -0.0037692077
sample95  -0.0168416047  0.0010019120
sample96  -0.0285582661 -0.0187991000
sample97  -0.0490843868 -0.0266760748
sample98  -0.0171579033 -0.0112897471
sample99  -0.0271316525  0.0232395583
sample100 -0.0301789816  0.0305498693
sample101 -0.0264371151  0.0170723968
sample102  0.0012767734 -0.0248949597
sample103  0.0055214687 -0.0030040587
sample104  0.0251346074 -0.0165212671
sample105  0.0062424215 -0.0400309901
sample106  0.0069768684  0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679  0.0023637162
sample109 -0.0014762845  0.0165583675
sample110  0.0036971063  0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112  0.0046098120 -0.0048009350
sample113  0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453  0.0135805368
sample116  0.0183575759  0.0665377581
sample117  0.0227640036 -0.0012287760
sample118  0.0015695248  0.0472617382
sample119  0.0190084932  0.0590034062
sample120 -0.0449645755  0.0072755697
sample121  0.0077307184  0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123  0.0016959300  0.0028593594
sample124 -0.0365091615  0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408  0.0019165544
sample127 -0.0494064872  0.0088209044
sample128 -0.0155454766  0.0186819802
sample129 -0.0184340400  0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422  0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133  0.0204029276 -0.0282209049
sample134  0.0175513332  0.0262883962
sample135  0.0029009925  0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323  0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139  0.0073103935  0.0308956174
sample140  0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142  0.0268670676 -0.0239229634
sample143  0.0421049126 -0.0110888235
sample144  0.0017253664 -0.0341766012
sample145  0.0681741320 -0.0073526377
sample146 -0.0239965222  0.0118396767
sample147 -0.0063453522  0.0183130585
sample148  0.0230825251 -0.0379753037
sample149  0.0223298673  0.0188909118
sample150  0.0055709108  0.0174179009
sample151  0.0039177786 -0.0233533275
sample152  0.0134325667  0.0302344591
sample153  0.0511990309  0.0730230140
sample154  0.0006698324  0.0154177486
sample155  0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599  0.0154934573
sample158  0.0201775524 -0.0332982124
sample159 -0.0086909001  0.0073496711
sample160  0.0295437331 -0.0555734536
sample161  0.0332754288  0.0033779619
sample162  0.0121954537  0.0433540412
sample163 -0.0173490933  0.0227219128
sample164  0.0143374783 -0.0453542590
sample165  0.0343612593 -0.0511194536
sample166 -0.0157536004  0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919  0.0060747155
sample169  0.0116231468 -0.0015112800
> 
> ## 3.3 Plotting VAF
> 
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
> 
> # JIVE plotVAF
> plotVAF(jiveRes)
> 
> 
> #########################
> ## PART 4. Plot Results
> 
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+              combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+              background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+              axisSize=NULL,titleSize=NULL)
> 
> # Scores for common part. JIVE
> plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common",
+              combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+              background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+              axisSize=NULL,titleSize=NULL)
> 
> # Scores for common part. O2PLS.
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for common part. O2PLS.
> plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common",
+              combined=TRUE,block=NULL,color="classname",shape=NULL,
+              labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+              labelSize=NULL,axisSize=NULL,titleSize=NULL)
> 
> 
> # Scores for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for distinctive part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual",
+              combined=TRUE,block=NULL,color="classname",shape=NULL,
+              labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+              labelSize=NULL,axisSize=NULL,titleSize=NULL)
> 
> # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block)
> p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for common and distinctive part. DISCO  (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Loadings for common part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> # Loadings for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> # Combined plot for loadings from common and distinctive part  (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> 
> ## Plot scores and loadings togheter: Common components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+         combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+         background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+         axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> ## Plot scores and loadings togheter:  Common components O2PLS
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> ## Plot scores and loadings togheter: Distintive components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
  10.10    0.32   10.42 

R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> ###########################################
> ########### EXAMPLE OF THE OMICSPCA
> ###########################################
> require(STATegRa)
Loading required package: STATegRa
> 
> # g_legend (not exported by STATegRa any more)
> ## code from https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs
> g_legend<-function(a.gplot){
+     tmp <- ggplot_gtable(ggplot_build(a.gplot))
+     leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
+     legend <- tmp$grobs[[leg]]
+     return(legend)}
> 
> #########################
> ## PART 1. Load data
> 
> ## Load data
> data(STATegRa_S3)
> 
> ls()
[1] "Block1.PCA" "Block2.PCA" "ed.PCA"     "g_legend"  
> 
> ## Create ExpressionSets
> # Block1 - Expression data
> B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname"))
> # Block2 - miRNA expression data
> B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname"))
> 
> #########################
> ## PART 2.  Model Selection
> 
> require(grid)
Loading required package: grid
> require(gridExtra)
Loading required package: gridExtra
> require(ggplot2)
Loading required package: ggplot2
> 
> ## Select the optimal components
> ms <- modelSelection(Input=list(B1,B2),Rmax=4,fac.sel="single%",varthreshold=0.03,center=TRUE,scale=TRUE,weight=TRUE)
Common components
[1] 2

Distinctive components
[[1]]
[1] 2

[[2]]
[1] 2

> 
> 
> #########################
> ## PART 3. Component Analysis
> 
> ## 3.1 Component analysis of the three methods
> discoRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="DISCOSCA",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                               scale=TRUE,weight=TRUE)
> jiveRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="JIVE",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                              scale=TRUE,weight=TRUE)
> o2plsRes <- omicsCompAnalysis(Input=list(B1,B2),Names=c("expr","mirna"),method="O2PLS",Rcommon=2,Rspecific=c(2,2),center=TRUE,
+                               scale=TRUE,weight=TRUE)
> 
> ## 3.2 Exploring scores structures
> 
> # Exploring DISCO-SCA scores structure
> discoRes@scores$common ## Common scores
                      1             2
sample1    0.0781574235 -0.0431498891
sample2   -0.1192218202  0.0294093192
sample3   -0.0531412345 -0.0746839980
sample4    0.0292975383 -0.0005954832
sample5    0.0202091834  0.0110464291
sample6    0.1226089098  0.1053465283
sample7    0.1078927889 -0.0322479709
sample8    0.1782895585  0.1449366352
sample9    0.0468698181 -0.0455174509
sample10  -0.0036030360  0.0420113320
sample11  -0.0035566479 -0.0566293133
sample12   0.1006128856  0.0641379860
sample13  -0.1174408048  0.0907489298
sample14   0.0981203239  0.0617736368
sample15   0.0085334109 -0.0087017516
sample16   0.0783148786  0.1581291749
sample17  -0.1483609869  0.0638582066
sample18  -0.0963086380  0.0556636836
sample19  -0.0217244157 -0.0720083220
sample20  -0.0635636577 -0.0779656138
sample21  -0.0201840088  0.1566391999
sample22   0.0218268383 -0.0764107812
sample23   0.0852042226 -0.0032683468
sample24  -0.1287169896  0.1924551792
sample25  -0.0430574045 -0.0456561056
sample26  -0.1453896639  0.0541515900
sample27  -0.0197489166 -0.1185661302
sample28  -0.1025336094  0.0650687657
sample29   0.0706018218 -0.0682991977
sample30  -0.1295627844 -0.0066775737
sample31   0.1147449110  0.1232683784
sample32  -0.0374311057  0.0380172929
sample33   0.0599515667  0.0136862372
sample34  -0.0984200920  0.0375318002
sample35  -0.0543098511 -0.0378110891
sample36   0.1403625256 -0.0343764368
sample37   0.0228941473 -0.0732857294
sample38  -0.0222077546 -0.0962596751
sample39  -0.0941738440  0.0215200501
sample40   0.0643800738 -0.0687881620
sample41  -0.0327638233 -0.1232188231
sample42  -0.0500431850 -0.0292470503
sample43  -0.0184498932  0.0233008718
sample44   0.1487899429  0.1171364472
sample45  -0.1050773855  0.1123205857
sample46  -0.1151196003 -0.1094031084
sample47  -0.0962593883 -0.0288466958
sample48   0.0004837653 -0.0310270695
sample49   0.1135208057  0.1213975558
sample50  -0.0123553344 -0.1740742337
sample51   0.0550530037  0.1258883563
sample52   0.0499121416  0.0728541937
sample53   0.1119773723  0.1588010280
sample54  -0.0360055672  0.0228574877
sample55   0.0210419008  0.0006730360
sample56  -0.0434169074  0.0633125678
sample57   0.0197824883  0.1150710686
sample58   0.0030439835  0.0326095887
sample59   0.0500252866  0.0129411487
sample60   0.0184278552  0.0136075787
sample61   0.0150299446  0.0635020706
sample62  -0.0304764257 -0.0201325264
sample63   0.1102252641  0.1285977583
sample64   0.1552588175  0.0971167137
sample65  -0.0058503000  0.0207116365
sample66  -0.0025605197  0.0424322493
sample67   0.1546634545 -0.0661726449
sample68   0.0536368922 -0.0923689498
sample69   0.0640330187  0.0081981579
sample70   0.0163517466 -0.0663230302
sample71  -0.0102537736 -0.1345918215
sample72  -0.0654196346 -0.0196125613
sample73  -0.1048556342  0.0220933755
sample74   0.0123799408  0.0586112064
sample75   0.0392077812 -0.0209757155
sample76   0.0648953295 -0.0524764766
sample77   0.1172922090 -0.0201185715
sample78  -0.1463067773  0.0708477763
sample79   0.0265211361 -0.1603299332
sample80   0.0279737006 -0.0214208915
sample81   0.0079211448 -0.0738447575
sample82  -0.1544236646 -0.0361469185
sample83  -0.0494211825 -0.0050056579
sample84  -0.0259038380 -0.0346546492
sample85   0.1116484129 -0.0031504631
sample86  -0.1306483320 -0.0377219564
sample87  -0.0554778226 -0.0459749626
sample88  -0.0301623655  0.0382196882
sample89  -0.1016866753  0.0694030530
sample90   0.0086819751 -0.0201319848
sample91   0.1578625016 -0.2097830664
sample92   0.0170937070 -0.1655797320
sample93  -0.0979806944 -0.0121513123
sample94   0.0131483944 -0.0114932283
sample95   0.0315682640 -0.0758854783
sample96   0.0024125600 -0.0470131865
sample97   0.0634545386  0.0270334002
sample98  -0.0359374788 -0.0135490084
sample99  -0.1009163011  0.1124785081
sample100  0.0551753060  0.0246488625
sample101 -0.0080119105 -0.1627365618
sample102 -0.0046443809  0.0095643905
sample103 -0.0472523347 -0.0940394035
sample104  0.0198159632 -0.0591087398
sample105 -0.0400237711 -0.0160909207
sample106 -0.0923808291  0.0369018768
sample107 -0.1019374062  0.0224953162
sample108 -0.0877091663 -0.0128833030
sample109  0.0864824689 -0.0900931176
sample110 -0.1223115470 -0.0096083991
sample111  0.0257354795 -0.0936160637
sample112 -0.0765286655  0.0270345151
sample113  0.0258803492  0.0377501699
sample114  0.0021138762 -0.0882012972
sample115  0.0303460566 -0.0723574298
sample116  0.0780508718 -0.0685059048
sample117  0.0536898405 -0.0911899506
sample118  0.0666651244 -0.0236228766
sample119  0.1021871563 -0.2324931676
sample120  0.0750216573  0.0243381687
sample121 -0.0756936289  0.0942949085
sample122 -0.0259627790  0.0731992222
sample123 -0.1037846374 -0.0369198624
sample124  0.0611208131  0.0421729500
sample125 -0.0738472725  0.0066950571
sample126  0.0972916243  0.0762635526
sample127  0.0824697500 -0.0096636741
sample128 -0.1249407358  0.0929317180
sample129 -0.0734067802 -0.0434366688
sample130 -0.0003502135 -0.0309852327
sample131  0.0930182721  0.0155934616
sample132  0.0736222977  0.0733034231
sample133 -0.0498397951 -0.0462436053
sample134  0.1644873555  0.0720002464
sample135 -0.0752297361  0.0003813791
sample136  0.0227145461 -0.0495508822
sample137  0.0564717091 -0.0288919966
sample138  0.0255988232 -0.0610851403
sample139  0.0621217729  0.0235803851
sample140 -0.0604152805 -0.0435597903
sample141  0.0246744038  0.0532650094
sample142 -0.0409560067  0.0316283347
sample143 -0.0077355137 -0.0476895318
sample144  0.0173240811 -0.0156777331
sample145  0.0485475024  0.1202773111
sample146  0.0419645310 -0.0811284384
sample147 -0.0977308628 -0.0274845315
sample148  0.0368256399  0.0803980448
sample149 -0.0072865862 -0.1532983642
sample150  0.1020825271  0.0624770971
sample151  0.0305399205 -0.0289273692
sample152 -0.0533594768 -0.0638307118
sample153 -0.0891626775  0.1799587931
sample154 -0.0727557701 -0.0834163313
sample155 -0.0880668770 -0.0220823343
sample156 -0.0276561213 -0.0326627100
sample157 -0.1155032235  0.0183614602
sample158 -0.0281507584 -0.0104940671
sample159  0.0663235853  0.0443839443
sample160 -0.0302643883  0.0404262742
sample161  0.0114715760 -0.0591021053
sample162 -0.1337086835  0.1398135802
sample163  0.1330124758  0.1688782196
sample164 -0.0150335943  0.0028419304
sample165  0.0076520378 -0.0164127052
sample166  0.0367794587  0.0630665698
sample167  0.1111988795  0.0030057201
sample168 -0.0672981499  0.0446280270
sample169 -0.0413005073  0.0224390238
> discoRes@scores$dist[[1]] ## Distinctive scores for Block 1
                      1             2
sample1    0.0420518641  0.0867862871
sample2    0.0820827022 -0.0410978713
sample3   -0.0155894868 -0.0195182073
sample4    0.1001336698 -0.0410787443
sample5    0.0153464966 -0.0253259827
sample6   -0.0340332283 -0.0408223187
sample7   -0.0722578085  0.0002332854
sample8    0.0457491078 -0.0370016916
sample9    0.0086251713  0.0820184934
sample10   0.0423596198 -0.0083923700
sample11  -0.0022545279  0.0787766188
sample12  -0.0322107999  0.1479824781
sample13   0.0293883812 -0.0306749049
sample14  -0.0337486529 -0.0367506749
sample15  -0.0815537553  0.1275623130
sample16  -0.0508461145  0.0540604673
sample17  -0.0062599794  0.0041023630
sample18  -0.0705642731 -0.0351047272
sample19   0.0476846036 -0.0509598280
sample20  -0.0522958018  0.0715522440
sample21   0.0119117419 -0.0376093539
sample22  -0.0724388275 -0.0095624386
sample23   0.0992532354  0.0134288037
sample24   0.1595107115  0.0728660368
sample25   0.0920695780 -0.0749757858
sample26   0.0595538092  0.0848965476
sample27  -0.0826478831 -0.0086734522
sample28   0.0384784789  0.0440966439
sample29  -0.0777666354  0.1735309261
sample30  -0.1229471063 -0.0819004558
sample31  -0.0579853543 -0.0238644601
sample32  -0.0970395216 -0.0111425643
sample33  -0.1017588603 -0.0630441822
sample34  -0.0637924382  0.0377942120
sample35  -0.0789983046 -0.0229722529
sample36  -0.1224939761 -0.1274953942
sample37  -0.1798818781 -0.1673425891
sample38  -0.0466297798  0.0888161527
sample39   0.0168687117  0.0421533584
sample40  -0.1756390165 -0.1526640862
sample41  -0.0042363387  0.0004929122
sample42   0.0447851525 -0.0651505274
sample43  -0.0482309656 -0.0253528955
sample44   0.1986706911 -0.0545779671
sample45   0.0741830299  0.0054702465
sample46  -0.0478765362 -0.0007071389
sample47  -0.0608186409  0.0481623176
sample48   0.1381491278  0.0578286785
sample49   0.0530512092 -0.1405533528
sample50   0.0173811384  0.1602389939
sample51  -0.0462568908  0.0303473892
sample52  -0.0280070248  0.0280388430
sample53  -0.0667630694  0.0237702179
sample54  -0.0121835210 -0.0521354279
sample55  -0.0182396191  0.0221328567
sample56   0.0001251524  0.0030907211
sample57  -0.0316683094  0.0530190249
sample58  -0.0393920225 -0.0297798519
sample59  -0.1278292393 -0.0546527006
sample60  -0.1486986666  0.1069157658
sample61  -0.0793126669  0.0569796980
sample62  -0.1172799442 -0.0149197536
sample63   0.0028720241  0.1300519508
sample64  -0.0237369888  0.1073287666
sample65   0.0126534244  0.0589808293
sample66   0.0468191822 -0.0771073150
sample67  -0.1494262947 -0.0769859012
sample68  -0.0977956452 -0.0577350092
sample69  -0.0403087061  0.0156042408
sample70  -0.0221526200  0.0315441286
sample71   0.0546442563 -0.0272396543
sample72  -0.1107486785 -0.0537318474
sample73  -0.0906761604  0.0579967261
sample74  -0.0586558599  0.0121421988
sample75  -0.0390491791  0.0349283159
sample76   0.0022962345 -0.1676558693
sample77   0.0232095838 -0.2067302934
sample78   0.0929750761 -0.0434940355
sample79   0.1619505663 -0.0378115108
sample80  -0.0680363449  0.1424664062
sample81   0.0530788543 -0.0358351081
sample82  -0.0266819780 -0.0577444805
sample83  -0.1517234667 -0.0448553157
sample84   0.0570968951 -0.0273813597
sample85  -0.1086291079 -0.1228118500
sample86  -0.0833857513 -0.0442914246
sample87  -0.0022016679 -0.0943906730
sample88   0.0078220946 -0.1140506684
sample89  -0.0611061182 -0.0094584838
sample90  -0.0022927057 -0.0936253925
sample91  -0.0433576537  0.3205983591
sample92   0.1815344545 -0.0334681300
sample93  -0.0267628977  0.0614429269
sample94  -0.0181875960  0.0605090577
sample95   0.0720380424 -0.0013046029
sample96   0.0559718010 -0.0118791731
sample97   0.0217410281  0.0195413917
sample98  -0.0379175721  0.0588357432
sample99   0.0792421082 -0.0151274659
sample100 -0.0222117661 -0.0023321321
sample101  0.0387239661  0.1224226304
sample102  0.2094613267 -0.0516444240
sample103 -0.0138475329  0.0301052281
sample104  0.0807990019 -0.0162719401
sample105  0.0520493480 -0.1229665523
sample106  0.0192611018 -0.0185238413
sample107 -0.0319017378  0.0405123491
sample108  0.0140692283  0.0163421311
sample109  0.1831935723  0.0613006367
sample110  0.0292791208 -0.0199849275
sample111  0.1423258041  0.0327339461
sample112 -0.0426334354 -0.0029083173
sample113  0.0771902415  0.0268732968
sample114  0.0241647073 -0.0184080391
sample115  0.1959020158  0.0460129352
sample116  0.1394478961 -0.0530806719
sample117  0.1672365663 -0.1386537470
sample118  0.0448345252 -0.0117622226
sample119  0.0910401216  0.2217433220
sample120  0.0331391237 -0.0057274805
sample121 -0.0307579381  0.1392506564
sample122  0.0839776811 -0.0291995228
sample123 -0.0239648487 -0.0642163450
sample124  0.0909148639  0.0130418711
sample125  0.0065349980 -0.1092631873
sample126 -0.0935314830  0.1368284581
sample127 -0.0035386527  0.0292755686
sample128  0.0660291246  0.1018565616
sample129 -0.0693636076 -0.0695421103
sample130 -0.0008491607 -0.0669704246
sample131 -0.0431024948  0.0174065154
sample132  0.0637036342  0.0029374068
sample133  0.0289497113 -0.0390818943
sample134 -0.0446207737  0.0456334665
sample135 -0.0712336453  0.0521635501
sample136 -0.0596267327  0.0197299894
sample137 -0.0793150146 -0.0380627636
sample138  0.0973551651 -0.0454218861
sample139 -0.0539907705 -0.1534327013
sample140 -0.0850823520  0.0955815283
sample141  0.0192678573 -0.0554450337
sample142  0.0672259513 -0.0461321498
sample143  0.0303732244 -0.0519260362
sample144  0.0089365673  0.0145814882
sample145  0.0638761671  0.0122257635
sample146 -0.0585851181  0.0063083982
sample147 -0.0894132289 -0.1124614952
sample148  0.0216361337 -0.0615967481
sample149  0.0515428788 -0.0839903531
sample150 -0.0568287569 -0.0124468643
sample151  0.0789533853 -0.0261831720
sample152  0.0330758132  0.1306443484
sample153  0.1751920874  0.1497730357
sample154 -0.0421419266 -0.0037009677
sample155 -0.0680175844  0.0095711804
sample156 -0.0388908118  0.1057563333
sample157 -0.0314769749  0.0561367629
sample158 -0.0329619785  0.0353947598
sample159  0.0398413084 -0.1007374182
sample160 -0.0424941291  0.0108496405
sample161  0.0888373737 -0.0679700713
sample162  0.0027468553  0.1237843525
sample163  0.0126095629  0.0725433851
sample164  0.0566779092 -0.0458324617
sample165  0.0315336782 -0.0236362554
sample166  0.0612054177 -0.0425233648
sample167 -0.0142729897  0.0179308371
sample168  0.0169500435 -0.0769618125
sample169 -0.0675081662  0.0131505792
> discoRes@scores$dist[[2]] ## Distinctive scores for Block 2
                      1             2
sample1   -0.0012329531 -1.635717e-01
sample2   -0.0724349879 -6.021203e-03
sample3   -0.0188460475 -1.080037e-01
sample4    0.0390145414  3.114623e-04
sample5    0.1774811698 -2.996382e-02
sample6   -0.0451444303 -3.455856e-02
sample7   -0.0226466416 -7.020222e-03
sample8   -0.1033679977 -9.856707e-03
sample9    0.1350011592  8.979097e-02
sample10   0.1259887419 -5.097847e-02
sample11   0.0979788216  7.086532e-02
sample12  -0.0863018991 -8.620317e-02
sample13  -0.1381401071  1.828008e-01
sample14  -0.0615073810 -2.642802e-02
sample15   0.0381598852 -3.101668e-02
sample16  -0.0048776574  1.271894e-03
sample17  -0.0788480816 -1.547550e-02
sample18  -0.0884188715 -3.795487e-02
sample19   0.0703044475 -1.084004e-01
sample20  -0.0025585775  7.975868e-02
sample21   0.0941601960 -4.126731e-02
sample22  -0.0550273570 -7.806752e-02
sample23   0.0679495460 -4.102001e-02
sample24  -0.1310962392  1.649311e-01
sample25   0.0113585385 -4.426860e-02
sample26  -0.1402945736  2.016548e-02
sample27   0.0261560829  1.588346e-03
sample28  -0.0724198594  5.850598e-02
sample29  -0.0330058802  2.060745e-03
sample30  -0.0228752699 -2.015436e-02
sample31  -0.0635067799 -6.670331e-02
sample32   0.0685099624 -4.955274e-02
sample33  -0.0777765239 -1.272079e-01
sample34   0.0157842429 -3.024314e-02
sample35  -0.0529633054  1.500971e-01
sample36   0.0070900324  2.025306e-01
sample37  -0.0442421131  1.802087e-01
sample38  -0.0781511477 -3.676428e-02
sample39   0.0120331965 -3.388838e-02
sample40  -0.0473292571  1.471560e-01
sample41   0.0228189242 -2.673561e-02
sample42  -0.0245360147 -7.960866e-02
sample43   0.1036362853 -8.229577e-02
sample44  -0.1012228429  7.049465e-02
sample45   0.0013732365 -2.450900e-02
sample46  -0.0558510236  2.947304e-03
sample47  -0.0380481331  4.554169e-02
sample48   0.0784342187  4.888986e-02
sample49  -0.0605163711 -1.162348e-02
sample50   0.0530079048 -2.737940e-02
sample51   0.1514646600  5.678350e-02
sample52   0.1860935187  1.246718e-01
sample53  -0.0064176940 -2.700989e-02
sample54   0.0697038389 -2.308387e-02
sample55   0.1633576991  1.366442e-02
sample56   0.1011485168  4.682210e-02
sample57   0.1730374199  1.609604e-01
sample58  -0.0071384697 -1.666955e-02
sample59  -0.0030461882  3.005279e-02
sample60   0.0215834705  2.665877e-01
sample61   0.1510583533  1.002385e-01
sample62  -0.0925534099 -4.845849e-02
sample63  -0.0596311569 -4.137017e-02
sample64  -0.0449225708 -2.600555e-03
sample65   0.0939383852 -4.406906e-02
sample66   0.1063400937 -5.709987e-02
sample67  -0.0201590575  2.361726e-01
sample68   0.0037202887  2.418379e-02
sample69  -0.0645161159 -1.155622e-01
sample70  -0.1013440031 -1.351789e-01
sample71  -0.0016467983 -2.976846e-02
sample72   0.0328892872 -2.835863e-02
sample73   0.0275080020 -5.148188e-02
sample74   0.1341719765 -7.895278e-02
sample75   0.0951575603 -3.943186e-02
sample76  -0.0864722086  3.034988e-02
sample77  -0.1035749568 -2.545355e-02
sample78  -0.1575643907  4.939602e-02
sample79   0.0189137005  4.874678e-02
sample80   0.1384140451  4.261953e-05
sample81  -0.0118846444 -6.357933e-02
sample82  -0.1675308266  3.533908e-02
sample83  -0.0065673551 -7.812617e-02
sample84   0.1486891671 -3.109054e-02
sample85  -0.0532724671  7.417877e-02
sample86  -0.1138477488 -1.921638e-05
sample87   0.0432863863  6.080471e-02
sample88   0.0433450318  1.402491e-01
sample89   0.0331205835 -1.395399e-02
sample90  -0.0607412774 -8.610416e-02
sample91  -0.0566273181  1.303746e-01
sample92  -0.0359582610  1.061604e-01
sample93  -0.0433646358 -4.443636e-02
sample94  -0.0477291251 -1.059574e-01
sample95  -0.0249595760 -3.980526e-02
sample96   0.0035219098 -9.293928e-02
sample97  -0.0066048554 -1.527230e-01
sample98   0.0020366799 -5.579552e-02
sample99  -0.0886615745 -3.728216e-02
sample100 -0.1091259107 -3.560421e-02
sample101 -0.0739726647 -4.318006e-02
sample102  0.0574461495 -2.783903e-02
sample103  0.0142730853  9.705506e-03
sample104  0.0710395190  4.068352e-02
sample105  0.0980831437 -3.452950e-02
sample106 -0.0254259239  3.628987e-02
sample107 -0.0160653352 -9.173394e-02
sample108 -0.0200987617 -2.379692e-02
sample109 -0.0389780570  1.692362e-02
sample110 -0.0326304821  2.988111e-02
sample111  0.0676937668 -6.038210e-02
sample112  0.0167883427  5.336939e-03
sample113  0.0969217208 -2.757597e-02
sample114 -0.0026398376 -9.209160e-02
sample115 -0.0308047175  1.603828e-02
sample116 -0.1240307219  1.273000e-01
sample117  0.0334729113  5.392714e-02
sample118 -0.1037152964  6.252430e-02
sample119 -0.1064177029  1.196201e-01
sample120 -0.0771354929 -1.004932e-01
sample121 -0.0129350658  3.181980e-02
sample122  0.0847492585 -5.568317e-02
sample123 -0.0041336857  7.693155e-03
sample124 -0.0583457739 -8.396384e-02
sample125  0.0634844684 -5.232538e-02
sample126 -0.0662580880 -1.091733e-01
sample127 -0.0865024543 -1.094176e-01
sample128 -0.0627817149 -1.470955e-02
sample129 -0.0336276567 -4.007864e-02
sample130 -0.0293517725 -8.046119e-02
sample131 -0.0469197720 -2.209774e-03
sample132 -0.0241740371 -1.248598e-01
sample133  0.0907303176  1.466701e-02
sample134 -0.0350842133  7.539662e-02
sample135  0.0001333312  9.185349e-03
sample136 -0.0335876140 -9.860280e-02
sample137 -0.0640149013 -7.554476e-02
sample138  0.0060964896 -1.742761e-02
sample139 -0.0592084566  5.614966e-02
sample140  0.0427985758 -1.099557e-02
sample141  0.0618796578 -9.301034e-02
sample142  0.0898554592  3.573424e-02
sample143  0.0817389102  8.880524e-02
sample144  0.0787754801 -3.821391e-02
sample145  0.1085821712  1.569477e-01
sample146 -0.0589558124 -4.373368e-02
sample147 -0.0495330601  7.277145e-03
sample148  0.1161592936  9.079145e-03
sample149 -0.0121579700  7.788368e-02
sample150 -0.0314512575  3.520212e-02
sample151  0.0575382251 -1.945350e-02
sample152 -0.0494542207  7.025535e-02
sample153 -0.0941332376  2.153299e-01
sample154 -0.0335932207  2.078722e-02
sample155  0.0690457560 -2.780413e-02
sample156  0.1039901576 -6.292527e-02
sample157 -0.0408645782  8.065514e-03
sample158  0.1018105242  7.816866e-03
sample159 -0.0281730413 -1.207203e-02
sample160  0.1643053017  2.978123e-03
sample161  0.0374329204  8.524612e-02
sample162 -0.0804535142  8.349763e-02
sample163 -0.0743227698 -1.406217e-02
sample164  0.1208806121 -2.139456e-02
sample165  0.1608115913  2.025194e-02
sample166 -0.0425944433 -2.660709e-02
sample167 -0.0226849479 -4.464283e-02
sample168 -0.0180735472 -7.465841e-04
sample169  0.0190778929  2.645401e-02
> # Exploring O2PLS scores structure
> o2plsRes@scores$common[[1]] ## Common scores for Block 1
                   [,1]          [,2]
sample1   -0.0572060227 -1.729087e-02
sample2    0.0875245208  1.112588e-02
sample3    0.0403482602 -3.168994e-02
sample4   -0.0218345996  4.052760e-06
sample5   -0.0150905011  4.795041e-03
sample6   -0.0924362933  4.511003e-02
sample7   -0.0793066751 -1.243823e-02
sample8   -0.1342997187  6.215220e-02
sample9   -0.0338886944 -1.854401e-02
sample10   0.0020547173  1.749421e-02
sample11   0.0037275602 -2.364116e-02
sample12  -0.0753094533  2.772698e-02
sample13   0.0856160091  3.679963e-02
sample14  -0.0737457307  2.668452e-02
sample15  -0.0062111746 -3.554864e-03
sample16  -0.0602355268  6.675115e-02
sample17   0.1086768843  2.524534e-02
sample18   0.0702999472  2.231671e-02
sample19   0.0173785882 -3.024846e-02
sample20   0.0484173812 -3.310904e-02
sample21   0.0124657042  6.517144e-02
sample22  -0.0140989936 -3.159137e-02
sample23  -0.0627028403 -5.393710e-04
sample24   0.0919972100  7.909297e-02
sample25   0.0326998483 -1.945206e-02
sample26   0.1064741246  2.120849e-02
sample27   0.0166058995 -4.964993e-02
sample28   0.0743504770  2.614211e-02
sample29  -0.0511008491 -2.782647e-02
sample30   0.0962250842 -3.974893e-03
sample31  -0.0869563008  5.250819e-02
sample32   0.0271858919  1.552005e-02
sample33  -0.0448364581  6.243160e-03
sample34   0.0718415218  1.469396e-02
sample35   0.0403086451 -1.632629e-02
sample36  -0.1036402827 -1.304320e-02
sample37  -0.0159385744 -3.036525e-02
sample38   0.0182198369 -4.034805e-02
sample39   0.0690363619  8.058350e-03
sample40  -0.0467312750 -2.810325e-02
sample41   0.0263674438 -5.171216e-02
sample42   0.0374578960 -1.268634e-02
sample43   0.0132336869  9.536642e-03
sample44  -0.1119154428  5.028683e-02
sample45   0.0759639367  4.587903e-02
sample46   0.0871885519 -4.670385e-02
sample47   0.0721490571 -1.288540e-02
sample48   0.0005086144 -1.290565e-02
sample49  -0.0858177028  5.173760e-02
sample50   0.0118992665 -7.276215e-02
sample51  -0.0426446855  5.306205e-02
sample52  -0.0381605826  3.086785e-02
sample53  -0.0855757630  6.730043e-02
sample54   0.0261723092  9.184260e-03
sample55  -0.0156418304  4.682404e-04
sample56   0.0307831193  2.597550e-02
sample57  -0.0157242103  4.829381e-02
sample58  -0.0031174404  1.359898e-02
sample59  -0.0373001859  5.868397e-03
sample60  -0.0142609099  5.831654e-03
sample61  -0.0122255144  2.663579e-02
sample62   0.0228002942 -8.692265e-03
sample63  -0.0833127581  5.473229e-02
sample64  -0.1166548159  4.196500e-02
sample65   0.0038808902  8.568590e-03
sample66   0.0011561811  1.766612e-02
sample67  -0.1129311062 -2.608702e-02
sample68  -0.0382526429 -3.804045e-02
sample69  -0.0476502440  4.003241e-03
sample70  -0.0110329882 -2.752719e-02
sample71   0.0096850282 -5.627056e-02
sample72   0.0487124704 -8.800131e-03
sample73   0.0773058132  8.239864e-03
sample74  -0.0102488176  2.454957e-02
sample75  -0.0286613976 -8.387293e-03
sample76  -0.0472655595 -2.129315e-02
sample77  -0.0865043074 -7.296820e-03
sample78   0.1070293698  2.818346e-02
sample79  -0.0165060681 -6.659721e-02
sample80  -0.0206765949 -8.712112e-03
sample81  -0.0050943615 -3.079175e-02
sample82   0.1153622361 -1.647054e-02
sample83   0.0367979217 -2.538114e-03
sample84   0.0199463070 -1.468961e-02
sample85  -0.0827122185 -2.709824e-04
sample86   0.0969487314 -1.699897e-02
sample87   0.0421957457 -1.965953e-02
sample88   0.0215934743  1.566050e-02
sample89   0.0751559502  2.811652e-02
sample90  -0.0057328000 -8.283795e-03
sample91  -0.1134005268 -8.603522e-02
sample92  -0.0101689918 -6.894992e-02
sample93   0.0725967502 -6.003176e-03
sample94  -0.0096878852 -4.693081e-03
sample95  -0.0223502239 -3.139636e-02
sample96  -0.0013232863 -1.963604e-02
sample97  -0.0476541710  1.183660e-02
sample98   0.0269546160 -5.978398e-03
sample99   0.0728179461  4.597884e-02
sample100 -0.0413398038  1.079347e-02
sample101  0.0087536994 -6.796076e-02
sample102  0.0032509529  3.932612e-03
sample103  0.0360342395 -3.973263e-02
sample104 -0.0141722563 -2.453107e-02
sample105  0.0294940465 -7.140722e-03
sample106  0.0686472054  1.462895e-02
sample107  0.0748635927  8.401339e-03
sample108  0.0650175850 -6.211942e-03
sample109 -0.0628017242 -3.681224e-02
sample110  0.0905513691 -5.169053e-03
sample111 -0.0176679473 -3.884777e-02
sample112  0.0570870472  1.066018e-02
sample113 -0.0200110554  1.596044e-02
sample114 -0.0001474542 -3.679272e-02
sample115 -0.0213333038 -2.991667e-02
sample116 -0.0567675453 -2.785636e-02
sample117 -0.0379865990 -3.752078e-02
sample118 -0.0484878786 -9.173691e-03
sample119 -0.0713511831 -9.598634e-02
sample120 -0.0555093586  1.089843e-02
sample121  0.0542443861  3.861344e-02
sample122  0.0178575357  3.027138e-02
sample123  0.0775020581 -1.636852e-02
sample124 -0.0460701050  1.814758e-02
sample125  0.0543846585  2.075898e-03
sample126 -0.0729417144  3.276659e-02
sample127 -0.0609509157 -3.270814e-03
sample128  0.0908136899  3.758801e-02
sample129  0.0552445878 -1.879062e-02
sample130  0.0007128089 -1.294308e-02
sample131 -0.0693311345  7.357082e-03
sample132 -0.0556565156  3.126995e-02
sample133  0.0375870104 -1.977240e-02
sample134 -0.1229130924  3.159495e-02
sample135  0.0555550315 -5.563250e-04
sample136 -0.0159768414 -2.046339e-02
sample137 -0.0412337694 -1.151652e-02
sample138 -0.0180604476 -2.526505e-02
sample139 -0.0465649201  1.040683e-02
sample140  0.0452288969 -1.876279e-02
sample141 -0.0189142561  2.247042e-02
sample142  0.0297545566  1.280524e-02
sample143  0.0064292003 -1.997706e-02
sample144 -0.0124284903 -6.369733e-03
sample145 -0.0377141491  5.066743e-02
sample146 -0.0296240067 -3.344465e-02
sample147  0.0726083535 -1.239968e-02
sample148 -0.0284795794  3.389732e-02
sample149  0.0082261455 -6.399305e-02
sample150 -0.0765013197  2.704021e-02
sample151 -0.0220567356 -1.178159e-02
sample152  0.0403422737 -2.714879e-02
sample153  0.0629117719  7.425085e-02
sample154  0.0551622927 -3.548984e-02
sample155  0.0654439133 -1.005306e-02
sample156  0.0209310714 -1.390213e-02
sample157  0.0851522597  6.577150e-03
sample158  0.0208354599 -4.663078e-03
sample159 -0.0498794349  1.913257e-02
sample160  0.0216074437  1.656579e-02
sample161 -0.0075742328 -2.455676e-02
sample162  0.0963663017  5.705881e-02
sample163 -0.1009542191  7.174224e-02
sample164  0.0109881996  1.026806e-03
sample165 -0.0053146157 -6.772855e-03
sample166 -0.0275757357  2.673084e-02
sample167 -0.0825048036  2.278863e-03
sample168  0.0486147429  1.793843e-02
sample169  0.0302506727  8.984253e-03
> o2plsRes@scores$common[[2]] ## Common scores for Block 2
                   [,1]          [,2]
sample1   -0.0621842115 -1.364509e-02
sample2    0.0944623785  9.720892e-03
sample3    0.0406196267 -2.236338e-02
sample4   -0.0229316496 -3.932487e-04
sample5   -0.0157330047  3.231033e-03
sample6   -0.0945794025  3.120720e-02
sample7   -0.0854427118 -1.052880e-02
sample8   -0.1376625920  4.286608e-02
sample9   -0.0377115311 -1.415134e-02
sample10   0.0035244506  1.280825e-02
sample11   0.0016639987 -1.717895e-02
sample12  -0.0781403168  1.884368e-02
sample13   0.0938400516  2.838858e-02
sample14  -0.0759839772  1.810989e-02
sample15  -0.0068340837 -2.705361e-03
sample16  -0.0590150849  4.757848e-02
sample17   0.1178805097  2.040526e-02
sample18   0.0767858320  1.756604e-02
sample19   0.0157112113 -2.172867e-02
sample20   0.0485318300 -2.327033e-02
sample21   0.0185928176  4.777095e-02
sample22  -0.0191358702 -2.329775e-02
sample23  -0.0672994194 -1.535656e-03
sample24   0.1047476642  5.935707e-02
sample25   0.0329844953 -1.358036e-02
sample26   0.1154952052  1.741529e-02
sample27   0.0133849853 -3.590922e-02
sample28   0.0821554039  2.042376e-02
sample29  -0.0567643690 -2.123848e-02
sample30   0.1016073931 -1.134728e-03
sample31  -0.0880396372  3.670548e-02
sample32   0.0300363338  1.182406e-02
sample33  -0.0467252272  3.739254e-03
sample34   0.0783666394  1.203777e-02
sample35   0.0424227097 -1.118559e-02
sample36  -0.1107646166 -1.143464e-02
sample37  -0.0191667664 -2.246060e-02
sample38   0.0155968095 -2.909621e-02
sample39   0.0746847148  7.148218e-03
sample40  -0.0517028178 -2.137267e-02
sample41   0.0234979494 -3.723018e-02
sample42   0.0388797356 -8.557228e-03
sample43   0.0149555568  7.210002e-03
sample44  -0.1150305613  3.461805e-02
sample45   0.0846146236  3.486020e-02
sample46   0.0884426404 -3.246853e-02
sample47   0.0748644971 -8.083045e-03
sample48  -0.0012033198 -9.403647e-03
sample49  -0.0872662737  3.616245e-02
sample50   0.0066941314 -5.284863e-02
sample51  -0.0411777630  3.791830e-02
sample52  -0.0379355780  2.180834e-02
sample53  -0.0851639886  4.751761e-02
sample54   0.0288006248  7.184424e-03
sample55  -0.0164920835  5.919925e-05
sample56   0.0355115616  1.951043e-02
sample57  -0.0141146068  3.492409e-02
sample58  -0.0015636132  9.862883e-03
sample59  -0.0390656483  3.590929e-03
sample60  -0.0139454780  3.963030e-03
sample61  -0.0106410274  1.919705e-02
sample62   0.0236748439 -5.922677e-03
sample63  -0.0846790877  3.839102e-02
sample64  -0.1202581015  2.846469e-02
sample65   0.0050548584  6.328644e-03
sample66   0.0028013072  1.291807e-02
sample67  -0.1231623009 -2.112565e-02
sample68  -0.0437782161 -2.845072e-02
sample69  -0.0501199692  2.053469e-03
sample70  -0.0140278645 -2.027157e-02
sample71   0.0057489505 -4.085977e-02
sample72   0.0511212704 -5.522408e-03
sample73   0.0828141409  7.431582e-03
sample74  -0.0085959456  1.772951e-02
sample75  -0.0312180394 -6.636869e-03
sample76  -0.0519051781 -1.640191e-02
sample77  -0.0925924762 -6.907800e-03
sample78   0.1163971046  2.251122e-02
sample79  -0.0240906926 -4.887766e-02
sample80  -0.0221327065 -6.730703e-03
sample81  -0.0072114968 -2.254399e-02
sample82   0.1204416674 -9.907422e-03
sample83   0.0386739485 -1.171663e-03
sample84   0.0195988488 -1.033806e-02
sample85  -0.0877680171 -1.725057e-03
sample86   0.1023541048 -1.062501e-02
sample87   0.0425213089 -1.356865e-02
sample88   0.0244788514  1.180820e-02
sample89   0.0804276691  2.188588e-02
sample90  -0.0074639871 -6.140721e-03
sample91  -0.1278832404 -6.485140e-02
sample92  -0.0162199697 -5.048358e-02
sample93   0.0769344893 -3.045135e-03
sample94  -0.0104345587 -3.593172e-03
sample95  -0.0260058453 -2.330475e-02
sample96  -0.0025018700 -1.433516e-02
sample97  -0.0492358305  7.774183e-03
sample98   0.0279220220 -3.862141e-03
sample99   0.0813921923  3.487339e-02
sample100 -0.0428797405  7.112807e-03
sample101  0.0032855240 -4.940743e-02
sample102  0.0038439317  2.938008e-03
sample103  0.0358511139 -2.831881e-02
sample104 -0.0162784000 -1.815061e-02
sample105  0.0314853405 -4.656633e-03
sample106  0.0726456731  1.192390e-02
sample107  0.0807342975  7.508627e-03
sample108  0.0688338003 -3.336161e-03
sample109 -0.0694151950 -2.800146e-02
sample110  0.0961218924 -2.111997e-03
sample111 -0.0217900036 -2.864702e-02
sample112  0.0599954082  8.820317e-03
sample113 -0.0195006577  1.128215e-02
sample114 -0.0032126533 -2.682851e-02
sample115 -0.0251101087 -2.221077e-02
sample116 -0.0625141551 -2.137258e-02
sample117 -0.0440473375 -2.806256e-02
sample118 -0.0532042630 -7.590494e-03
sample119 -0.0848603028 -7.133574e-02
sample120 -0.0588832131  6.937326e-03
sample121  0.0613899126  2.915307e-02
sample122  0.0218424338  2.241775e-02
sample123  0.0809008460 -1.051759e-02
sample124 -0.0472109313  1.239887e-02
sample125  0.0583180947  2.521167e-03
sample126 -0.0753941872  2.256455e-02
sample127 -0.0649774209 -3.496964e-03
sample128  0.1000212216  2.908091e-02
sample129  0.0568033049 -1.269016e-02
sample130 -0.0002370832 -9.419675e-03
sample131 -0.0727030877  4.091672e-03
sample132 -0.0566219024  2.179861e-02
sample133  0.0384172955 -1.372840e-02
sample134 -0.1280862736  2.077912e-02
sample135  0.0592633273  6.106685e-04
sample136 -0.0187635410 -1.521173e-02
sample137 -0.0449958970 -9.152840e-03
sample138 -0.0211348699 -1.875415e-02
sample139 -0.0482882861  6.729304e-03
sample140  0.0468926306 -1.285498e-02
sample141 -0.0186248693  1.605439e-02
sample142  0.0328031246  9.887746e-03
sample143  0.0052919839 -1.445666e-02
sample144 -0.0140067923 -4.867248e-03
sample145 -0.0361804310  3.625323e-02
sample146 -0.0345286735 -2.493652e-02
sample147  0.0765025670 -7.714769e-03
sample148 -0.0276016641  2.420589e-02
sample149  0.0027545308 -4.653007e-02
sample150 -0.0792296010  1.831289e-02
sample151 -0.0245894512 -8.991738e-03
sample152  0.0409796547 -1.907063e-02
sample153  0.0734301757  5.528780e-02
sample154  0.0557740684 -2.487723e-02
sample155  0.0689436560 -6.127635e-03
sample156  0.0212272938 -9.747423e-03
sample157  0.0911931194  6.355708e-03
sample158  0.0220840645 -3.016357e-03
sample159 -0.0513244242  1.304175e-02
sample160  0.0246213576  1.248444e-02
sample161 -0.0100369130 -1.805391e-02
sample162  0.1078802043  4.337260e-02
sample163 -0.1017965082  5.047171e-02
sample164  0.0119430799  9.593002e-04
sample165 -0.0063708014 -5.032148e-03
sample166 -0.0283181180  1.899222e-02
sample167 -0.0872832229  1.516582e-04
sample168  0.0540714512  1.397701e-02
sample169  0.0328432652  7.104347e-03
> o2plsRes@scores$dist[[1]] ## Distinctive scores for Block 1
                   [,1]          [,2]
sample1    0.0133684846  2.195848e-02
sample2    0.0254157197 -1.058416e-02
sample3   -0.0049551479 -4.840017e-03
sample4    0.0310390570 -1.063929e-02
sample5    0.0046941318 -6.488426e-03
sample6   -0.0107406753 -1.026702e-02
sample7   -0.0225157631  2.624712e-04
sample8    0.0141320952 -9.505821e-03
sample9    0.0029681280  2.078210e-02
sample10   0.0131729174 -2.275042e-03
sample11  -0.0004164298  1.994019e-02
sample12  -0.0095211620  3.759883e-02
sample13   0.0091018604 -7.953956e-03
sample14  -0.0106557524 -9.181659e-03
sample15  -0.0249924121  3.262724e-02
sample16  -0.0156216400  1.375700e-02
sample17  -0.0019382446  1.073994e-03
sample18  -0.0221072481 -8.703592e-03
sample19   0.0146917619 -1.311712e-02
sample20  -0.0160353760  1.826290e-02
sample21   0.0035947899 -9.616341e-03
sample22  -0.0225060762 -2.532589e-03
sample23   0.0310000683  3.033060e-03
sample24   0.0499544372  1.809450e-02
sample25   0.0284442301 -1.932558e-02
sample26   0.0188220043  2.146985e-02
sample27  -0.0257763219 -1.999228e-03
sample28   0.0120888648  1.125834e-02
sample29  -0.0236482520  4.426726e-02
sample30  -0.0385486305 -2.055935e-02
sample31  -0.0181539336 -5.877838e-03
sample32  -0.0302630460 -2.607192e-03
sample33  -0.0319565715 -1.562628e-02
sample34  -0.0197970124  9.906813e-03
sample35  -0.0247412713 -5.434440e-03
sample36  -0.0386259060 -3.190394e-02
sample37  -0.0566199273 -4.192574e-02
sample38  -0.0142060273  2.259644e-02
sample39   0.0053589035  1.076485e-02
sample40  -0.0552546493 -3.819896e-02
sample41  -0.0013089975  9.278818e-05
sample42   0.0137252142 -1.664652e-02
sample43  -0.0151259626 -6.290953e-03
sample44   0.0617391754 -1.442883e-02
sample45   0.0231410886  1.163143e-03
sample46  -0.0148898209 -1.384176e-04
sample47  -0.0187252536  1.221690e-02
sample48   0.0432839432  1.416671e-02
sample49   0.0160818605 -3.588745e-02
sample50   0.0059333545  4.067003e-02
sample51  -0.0142914866  7.776270e-03
sample52  -0.0086339952  7.208917e-03
sample53  -0.0207386980  6.272432e-03
sample54  -0.0039856719 -1.316934e-02
sample55  -0.0056217017  5.692315e-03
sample56   0.0000123292  8.978290e-04
sample57  -0.0095805555  1.324253e-02
sample58  -0.0124160295 -7.326376e-03
sample59  -0.0400195442 -1.349736e-02
sample60  -0.0460063358  2.770091e-02
sample61  -0.0245266456  1.470710e-02
sample62  -0.0366022783 -3.437352e-03
sample63   0.0013742171  3.288796e-02
sample64  -0.0070599859  2.739588e-02
sample65   0.0041201911  1.498268e-02
sample66   0.0143173351 -1.968812e-02
sample67  -0.0467477531 -1.929938e-02
sample68  -0.0306751978 -1.436184e-02
sample69  -0.0125317217  4.130407e-03
sample70  -0.0068071487  8.080857e-03
sample71   0.0169170264 -7.027348e-03
sample72  -0.0346909749 -1.333770e-02
sample73  -0.0280506153  1.493843e-02
sample74  -0.0182611498  3.294697e-03
sample75  -0.0120563964  8.974612e-03
sample76   0.0001437236 -4.253184e-02
sample77   0.0065330299 -5.252886e-02
sample78   0.0288278141 -1.127782e-02
sample79   0.0503961481 -1.023318e-02
sample80  -0.0207693429  3.648391e-02
sample81   0.0163562768 -9.074596e-03
sample82  -0.0084317129 -1.478976e-02
sample83  -0.0474097918 -1.103126e-02
sample84   0.0177181395 -7.191197e-03
sample85  -0.0342718548 -3.082360e-02
sample86  -0.0261671791 -1.089491e-02
sample87  -0.0009486358 -2.411514e-02
sample88   0.0020528931 -2.894615e-02
sample89  -0.0189361111 -2.638639e-03
sample90  -0.0009863658 -2.390075e-02
sample91  -0.0124352695  8.153234e-02
sample92   0.0564264106 -8.909537e-03
sample93  -0.0081461774  1.570851e-02
sample94  -0.0054896581  1.547251e-02
sample95   0.0224073150 -4.374348e-04
sample96   0.0173528924 -3.050441e-03
sample97   0.0067948115  5.008237e-03
sample98  -0.0116030825  1.498764e-02
sample99   0.0246422688 -4.054795e-03
sample100 -0.0069420745 -4.846343e-04
sample101  0.0124923691  3.091503e-02
sample102  0.0650835386 -1.367400e-02
sample103 -0.0042741828  7.855985e-03
sample104  0.0250591040 -4.171938e-03
sample105  0.0157516368 -3.121990e-02
sample106  0.0060593853 -5.101693e-03
sample107 -0.0098329626  1.044506e-02
sample108  0.0044269853  4.142036e-03
sample109  0.0572473486  1.517542e-02
sample110  0.0090474827 -5.119868e-03
sample111  0.0444263015  7.983232e-03
sample112 -0.0131765484 -9.696342e-04
sample113  0.0241047399  6.706740e-03
sample114  0.0074558775 -4.728652e-03
sample115  0.0611851433  1.117210e-02
sample116  0.0432646951 -1.380556e-02
sample117  0.0516750066 -3.575617e-02
sample118  0.0139942100 -3.279138e-03
sample119  0.0291722987  5.587946e-02
sample120  0.0103515853 -1.690016e-03
sample121 -0.0091396331  3.552116e-02
sample122  0.0260431679 -7.583975e-03
sample123 -0.0076666389 -1.628489e-02
sample124  0.0283466326  3.127845e-03
sample125  0.0016472378 -2.770692e-02
sample126 -0.0286529417  3.489336e-02
sample127 -0.0010224500  7.483214e-03
sample128  0.0209049296  2.572016e-02
sample129 -0.0218184878 -1.755347e-02
sample130 -0.0005009620 -1.697978e-02
sample131 -0.0134032968  4.637390e-03
sample132  0.0198526786  5.723983e-04
sample133  0.0088812957 -9.988115e-03
sample134 -0.0137484514  1.172591e-02
sample135 -0.0220314568  1.347465e-02
sample136 -0.0185173353  5.168079e-03
sample137 -0.0248352123 -9.472788e-03
sample138  0.0301635767 -1.175283e-02
sample139 -0.0173576929 -3.872592e-02
sample140 -0.0262157762  2.456863e-02
sample141  0.0058369763 -1.420854e-02
sample142  0.0207886071 -1.188764e-02
sample143  0.0092832598 -1.324238e-02
sample144  0.0028442140  3.627979e-03
sample145  0.0199749569  2.862202e-03
sample146 -0.0182236697  1.726556e-03
sample147 -0.0282519995 -2.825595e-02
sample148  0.0065435868 -1.572917e-02
sample149  0.0158233820 -2.159451e-02
sample150 -0.0177383738 -3.020633e-03
sample151  0.0245166984 -6.888241e-03
sample152  0.0107259913  3.314630e-02
sample153  0.0550963965  3.758760e-02
sample154 -0.0131452472 -8.153903e-04
sample155 -0.0211742574  2.642246e-03
sample156 -0.0117803505  2.698265e-02
sample157 -0.0096167165  1.433840e-02
sample158 -0.0101754772  9.137620e-03
sample159  0.0120662931 -2.565236e-02
sample160 -0.0132238202  2.916023e-03
sample161  0.0274491966 -1.748284e-02
sample162  0.0012482909  3.152261e-02
sample163  0.0042031315  1.830701e-02
sample164  0.0174896157 -1.175915e-02
sample165  0.0097517662 -6.119019e-03
sample166  0.0190134679 -1.121582e-02
sample167 -0.0044140836  4.665585e-03
sample168  0.0049689168 -1.941822e-02
sample169 -0.0209802098  3.498729e-03
> o2plsRes@scores$dist[[2]] ## Distinctive scores for Block 2
                   [,1]          [,2]
sample1   -0.0515543627 -0.0305856787
sample2   -0.0144993256  0.0236342950
sample3   -0.0371833108 -0.0140263348
sample4    0.0068945388 -0.0132539692
sample5    0.0215035333 -0.0663338101
sample6   -0.0187055152  0.0088773016
sample7   -0.0061521552  0.0064029054
sample8   -0.0210874459  0.0334652901
sample9    0.0516865043 -0.0291142799
sample10   0.0059440366 -0.0527217447
sample11   0.0393010793 -0.0200624712
sample12  -0.0420837100  0.0131331362
sample13   0.0333252565  0.0818552509
sample14  -0.0190062644  0.0160202175
sample15  -0.0030968049 -0.0189230681
sample16  -0.0004452158  0.0018880102
sample17  -0.0185848615  0.0240170131
sample18  -0.0273093598  0.0230213640
sample19  -0.0217761111 -0.0445894441
sample20   0.0245820821  0.0159812738
sample21   0.0034527644 -0.0400016054
sample22  -0.0340789054  0.0039289109
sample23  -0.0010344929 -0.0310161212
sample24   0.0289468503  0.0760962436
sample25  -0.0119098496 -0.0122798760
sample26  -0.0181001057  0.0517892852
sample27   0.0050465417 -0.0086515844
sample28   0.0057491502  0.0358830107
sample29  -0.0051104246  0.0116605117
sample30  -0.0103085904  0.0039678538
sample31  -0.0319929858  0.0090606113
sample32  -0.0036232521 -0.0328202010
sample33  -0.0534742153  0.0024751837
sample34  -0.0067495749 -0.0111000311
sample35   0.0378745721  0.0465929296
sample36   0.0647886800  0.0359987924
sample37   0.0488441236  0.0492906912
sample38  -0.0251514062  0.0197110110
sample39  -0.0085428066 -0.0105117852
sample40   0.0379324087  0.0440810741
sample41  -0.0044199152 -0.0128820644
sample42  -0.0292553573 -0.0067045265
sample43  -0.0077829155 -0.0510178219
sample44   0.0045122248  0.0479660309
sample45  -0.0074444298 -0.0051116726
sample46  -0.0088025512  0.0196186661
sample47   0.0076696301  0.0215947965
sample48   0.0290108585 -0.0175568376
sample49  -0.0141754858  0.0184717099
sample50   0.0006282201 -0.0233054373
sample51   0.0441995177 -0.0410022921
sample52   0.0715329391 -0.0399499475
sample53  -0.0095954087 -0.0029140909
sample54   0.0048933768 -0.0281884386
sample55   0.0327325487 -0.0532290012
sample56   0.0323068984 -0.0256595538
sample57   0.0806603122 -0.0286748097
sample58  -0.0064792049 -0.0006945349
sample59   0.0088958941  0.0067389649
sample60   0.0874124612  0.0431964341
sample61   0.0577604571 -0.0326112099
sample62  -0.0313318464  0.0224391756
sample63  -0.0233625220  0.0125110562
sample64  -0.0086426068  0.0148770341
sample65   0.0025256193 -0.0404466327
sample66   0.0006014071 -0.0471576264
sample67   0.0706087042  0.0516228406
sample68   0.0082301011  0.0033109509
sample69  -0.0475076743  0.0001452708
sample70  -0.0600773716  0.0089986962
sample71  -0.0096321627 -0.0050761187
sample72  -0.0031773546 -0.0166221542
sample73  -0.0113700517 -0.0191726684
sample74  -0.0014179662 -0.0608101325
sample75   0.0041911740 -0.0399981269
sample76  -0.0055326449  0.0353114263
sample77  -0.0260214459  0.0305731380
sample78  -0.0119267436  0.0632236007
sample79   0.0186017239  0.0027402910
sample80   0.0241047889 -0.0472697181
sample81  -0.0220288317 -0.0079577210
sample82  -0.0180751258  0.0639051029
sample83  -0.0256671713 -0.0125898269
sample84   0.0161392598 -0.0567222449
sample85   0.0139988188  0.0322763454
sample86  -0.0198382995  0.0389225776
sample87   0.0266270281 -0.0032979996
sample88   0.0515677078  0.0117902495
sample89   0.0014022125 -0.0140510488
sample90  -0.0375949749  0.0044004551
sample91   0.0310397965  0.0440610926
sample92   0.0270570567  0.0324380452
sample93  -0.0215009202  0.0063993941
sample94  -0.0415702912 -0.0037692077
sample95  -0.0168416047  0.0010019120
sample96  -0.0285582661 -0.0187991000
sample97  -0.0490843868 -0.0266760748
sample98  -0.0171579033 -0.0112897471
sample99  -0.0271316525  0.0232395583
sample100 -0.0301789816  0.0305498693
sample101 -0.0264371151  0.0170723968
sample102  0.0012767734 -0.0248949597
sample103  0.0055214687 -0.0030040587
sample104  0.0251346074 -0.0165212671
sample105  0.0062424215 -0.0400309901
sample106  0.0069768684  0.0154982315
sample107 -0.0315912602 -0.0118883820
sample108 -0.0109690679  0.0023637162
sample109 -0.0014762845  0.0165583675
sample110  0.0036971063  0.0168260726
sample111 -0.0071624739 -0.0345651461
sample112  0.0046098120 -0.0048009350
sample113  0.0082236008 -0.0383233357
sample114 -0.0293642209 -0.0165595240
sample115 -0.0003260453  0.0135805368
sample116  0.0183575759  0.0665377581
sample117  0.0227640036 -0.0012287760
sample118  0.0015695248  0.0472617382
sample119  0.0190084932  0.0590034062
sample120 -0.0449645755  0.0072755697
sample121  0.0077307184  0.0104738937
sample122 -0.0027132063 -0.0394983138
sample123  0.0016959300  0.0028593594
sample124 -0.0365091615  0.0040382925
sample125 -0.0053658663 -0.0316029164
sample126 -0.0458032408  0.0019165544
sample127 -0.0494064872  0.0088209044
sample128 -0.0155454766  0.0186819802
sample129 -0.0184340400  0.0038684312
sample130 -0.0303640987 -0.0052225766
sample131 -0.0088697422  0.0156339713
sample132 -0.0433916471 -0.0154075483
sample133  0.0204029276 -0.0282209049
sample134  0.0175513332  0.0262883962
sample135  0.0029009925  0.0017003151
sample136 -0.0367997573 -0.0072249751
sample137 -0.0348600323  0.0075400273
sample138 -0.0044063824 -0.0053752428
sample139  0.0073103935  0.0308956174
sample140  0.0039925654 -0.0167019605
sample141 -0.0184093462 -0.0387953445
sample142  0.0268670676 -0.0239229634
sample143  0.0421049126 -0.0110888235
sample144  0.0017253664 -0.0341766012
sample145  0.0681741320 -0.0073526377
sample146 -0.0239965222  0.0118396767
sample147 -0.0063453522  0.0183130585
sample148  0.0230825251 -0.0379753037
sample149  0.0223298673  0.0188909118
sample150  0.0055709108  0.0174179009
sample151  0.0039177786 -0.0233533275
sample152  0.0134325667  0.0302344591
sample153  0.0511990309  0.0730230140
sample154  0.0006698324  0.0154177486
sample155  0.0032926626 -0.0288651601
sample156 -0.0016463495 -0.0474657733
sample157 -0.0045857599  0.0154934573
sample158  0.0201775524 -0.0332982124
sample159 -0.0086909001  0.0073496711
sample160  0.0295437331 -0.0555734536
sample161  0.0332754288  0.0033779619
sample162  0.0121954537  0.0433540412
sample163 -0.0173490933  0.0227219128
sample164  0.0143374783 -0.0453542590
sample165  0.0343612593 -0.0511194536
sample166 -0.0157536004  0.0094621170
sample167 -0.0179654624 -0.0006982358
sample168 -0.0033829919  0.0060747155
sample169  0.0116231468 -0.0015112800
> 
> ## 3.3 Plotting VAF
> 
> # DISCO-SCA plotVAF
> plotVAF(discoRes)
> 
> # JIVE plotVAF
> plotVAF(jiveRes)
> 
> 
> #########################
> ## PART 4. Plot Results
> 
> # Scores for common part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,2),what="scores",type="common",
+              combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+              background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+              axisSize=NULL,titleSize=NULL)
> 
> # Scores for common part. JIVE
> plotRes(object=jiveRes,comps=c(1,2),what="scores",type="common",
+              combined=FALSE,block=NULL,color="classname",shape=NULL,labels=NULL,
+              background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+              axisSize=NULL,titleSize=NULL)
> 
> # Scores for common part. O2PLS.
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="scores",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for common part. O2PLS.
> plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="common",
+              combined=TRUE,block=NULL,color="classname",shape=NULL,
+              labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+              labelSize=NULL,axisSize=NULL,titleSize=NULL)
> 
> 
> # Scores for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="scores",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for distinctive part. DISCO-SCA
> plotRes(object=discoRes,comps=c(1,1),what="scores",type="individual",
+              combined=TRUE,block=NULL,color="classname",shape=NULL,
+              labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+              labelSize=NULL,axisSize=NULL,titleSize=NULL)
> 
> # Combined plot of scores for common and distinctive part. O2PLS (two plots one for each block)
> p1 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Combined plot of scores for common and distinctive part. DISCO  (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="scores",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> legend <- g_legend(p1)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              legend,heights=c(6/7,1/7))
> 
> # Loadings for common part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> # Loadings for distinctive part. DISCO-SCA. (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="loadings",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> # Combined plot for loadings from common and distinctive part  (two plots one for each block)
> p1 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+               combined=FALSE,block="expr",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,1),what="loadings",type="both",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,
+               labels=NULL,background=TRUE,palette=NULL,pointSize=4,
+               labelSize=NULL,axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> 
> ## Plot scores and loadings togheter: Common components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+         combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+         background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+         axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> ## Plot scores and loadings togheter:  Common components O2PLS
> p1 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=o2plsRes,comps=c(1,2),what="both",type="common",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> ## Plot scores and loadings togheter: Distintive components DISCO-SCA
> p1 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+               combined=FALSE,block="expr",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> p2 <- plotRes(object=discoRes,comps=c(1,2),what="both",type="individual",
+               combined=FALSE,block="mirna",color="classname",shape=NULL,labels=NULL,
+               background=TRUE,palette=NULL,pointSize=4,labelSize=NULL,
+               axisSize=NULL,titleSize=NULL)
> grid.arrange(arrangeGrob(p1+theme(legend.position="none"),
+                          p2+theme(legend.position="none"),nrow=1),
+              heights=c(6/7,1/7))
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
  13.32    0.50   13.81