runCompareIcaSets

Description

This function encompasses the comparison of several IcaSet objects using correlations and the plot of the corresponding correlation graph. The IcaSet objects are compared by calculating the correlation between either projection values of common features or genes, or contributions of common samples.

Usage

  runCompareIcaSets(icaSets, labAn,
    type.corr = c("pearson", "spearman"), cutoff_zval = 0,
    level = c("genes", "features", "samples"),
    fileNodeDescr = NULL, fileDataGraph = NULL,
    plot = TRUE, title = "", col, cutoff_graph = NULL,
    useMax = TRUE, tkplot = FALSE)

Arguments

Details

This function calls four functions: compareAn which computes the correlations, compareAn2graphfile which builds the graph, nodeAttrs which builds the node description data, and plotCorGraph which uses tkplot to plot the graph in an interactive device.

If the user wants to see the correlation graph in Cytoscape, he must fill the arguments fileDataGraph and fileNodeDescr, in order to import the graph and its node descriptions as a .txt file in Cytoscape.

When labAn is missing, each element i of icaSets is labeled as 'Ani'.

The user must carefully choose the data level used in the comparison: If level='samples', the correlations are based on the mixing matrices of the ICA decompositions (of dimension samples x components). 'A' will be typically chosen when the ICA decompositions were computed on the same dataset, or on datasets that include the same samples. If level='features' is chosen, the correlation is calculated between the source matrices (of dimension features x components) of the ICA decompositions. 'S' will be typically used when the ICA decompositions share common features (e.g same microarrays). If level='genes', the correlations are calculated on the attributes 'SByGene' which store the projections of the annotated features. 'SByGene' will be typically chosen when ICA were computed on datasets from different technologies, for which comparison is possible only after annotation into a common ID, like genes.

cutoff_zval is only used when level is one of c('features','genes'), in order to restrict the correlation to the contributing features or genes.

When cutoff_zval is specified, for each pair of components, genes or features that are included in the circle of center 0 and radius cutoff_zval are excluded from the computation of the correlation.

It must be taken into account by the user that if cutoff_zval is different from NULL or zero, the computation will be much slowler since each pair of component is treated individually.

Edges of the graph are built based on the correlation values between the components. Absolute values of correlations are used since components have no direction.

If useMax is TRUE each component will be linked to only one component of each other IcaSet that corresponds to the most correlated component among all components of the same IcaSet. If cutoff_graph is specified, only correlations exceeding this value are taken into account to build the graph. For example, if cutoff is 1, only relationships between components that correspond to a correlation value higher than 1 will be included. Absolute correlation values are used since the components have no direction.

The contents of the returned list are

dataGraph:

dataGraph data.frame that describes the correlation graph,

nodeAttrs:

nodeAttrs data.frame that describes the node of the graph

graph

graph the graph as an igraph-object,

graphid:

graphid the id of the graph plotted using tkplot.

Value

A list consisting of

dataGraph:

a data.frame defining the correlation graph

nodeAttrs:

a data.frame describing the node of the graph,

graph:

the graph as an object of class igraph,

graphid

the id of the graph plotted with tkplot

.

Author(s)

Anne Biton

See Also

compareAn2graphfile, compareAn, cor2An, plotCorGraph

Examples

dat1 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000))
rownames(dat1) <- paste("g", 1:1000, sep="")
colnames(dat1) <- paste("s", 1:10, sep="")
dat2 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000))
rownames(dat2) <- paste("g", 1:1000, sep="")
colnames(dat2) <- paste("s", 1:10, sep="")

## run ICA
resJade1 <- runICA(X=dat1, nbComp=3, method = "JADE")
resJade2 <- runICA(X=dat2, nbComp=3, method = "JADE")

## build params
params <- buildMineICAParams(resPath="toy/")

## build IcaSet objects
icaSettoy1 <- buildIcaSet(params=params, A=data.frame(resJade1$A), S=data.frame(resJade1$S),
                          dat=dat1, alreadyAnnot=TRUE)$icaSet
icaSettoy2 <- buildIcaSet(params=params, A=data.frame(resJade2$A), S=data.frame(resJade2$S),
                          dat=dat2, alreadyAnnot=TRUE)$icaSet

## compare IcaSet objects
## use tkplot=TRUE to get an interactive graph
rescomp <- runCompareIcaSets(icaSets=list(icaSettoy1, icaSettoy2), labAn=c("toy1","toy2"),
                             type.corr="pearson", level="genes", tkplot=FALSE)


## Not run: 
## load the microarray-based gene expression datasets
## of breast tumors
library(breastCancerMAINZ)
library(breastCancerVDX)
data(mainz)
data(vdx)

## Define a function used to build two examples of IcaSet objects
## and annotate the probe sets into gene Symbols
treat <- function(es, annot="hgu133a.db") {
   es <- selectFeatures_IQR(es,10000)
   exprs(es) <- t(apply(exprs(es),1,scale,scale=FALSE))
   colnames(exprs(es)) <- sampleNames(es)
   resJade <- runICA(X=exprs(es), nbComp=10, method = "JADE", maxit=10000)
   resBuild <- buildIcaSet(params=buildMineICAParams(), A=data.frame(resJade$A), S=data.frame(resJade$S),
                        dat=exprs(es), pData=pData(es), refSamples=character(0),
                        annotation=annot, typeID= typeIDmainz,
                        chipManu = "affymetrix", mart=mart)
   icaSet <- resBuild$icaSet
}
## Build the two IcaSet objects
icaSetMainz <- treat(mainz)
icaSetVdx <- treat(vdx)

## compare the IcaSets
runCompareIcaSets(icaSets=list(icaSetMainz, icaSetVdx), labAn=c("Mainz","Vdx"), type.corr="pearson", level="genes")

## End(Not run)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (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.

> library(MineICA)
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':

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    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: Biobase
Welcome to Bioconductor

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

Loading required package: plyr
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Attaching package: 'S4Vectors'

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    rename

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Attaching package: 'IRanges'

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Loading required package: Matrix

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Loading required package: graph

Attaching package: 'graph'

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    join



Attaching package: 'GOstats'

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Loading required package: cluster
Loading required package: marray
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Attaching package: 'limma'

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    plotMA

Loading required package: mclust
Package 'mclust' version 5.2
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: RColorBrewer
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Loading required package: igraph

Attaching package: 'igraph'

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Loading required package: Rgraphviz
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Attaching package: 'Rgraphviz'

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Attaching package: 'XML'

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    addNode


Attaching package: 'annotate'

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    toFile

Loading required package: Hmisc
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Attaching package: 'Hmisc'

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Loading required package: fastICA
Loading required package: JADE
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MineICA/runCompareIcaSets.Rd_%03d_medium.png", width=480, height=480)
> ### Name: runCompareIcaSets
> ### Title: runCompareIcaSets
> ### Aliases: runCompareIcaSets
> 
> ### ** Examples
> 
> dat1 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000))
> rownames(dat1) <- paste("g", 1:1000, sep="")
> colnames(dat1) <- paste("s", 1:10, sep="")
> dat2 <- data.frame(matrix(rnorm(10000),ncol=10,nrow=1000))
> rownames(dat2) <- paste("g", 1:1000, sep="")
> colnames(dat2) <- paste("s", 1:10, sep="")
> 
> ## run ICA
> resJade1 <- runICA(X=dat1, nbComp=3, method = "JADE")
> resJade2 <- runICA(X=dat2, nbComp=3, method = "JADE")
> 
> ## build params
> params <- buildMineICAParams(resPath="toy/")
> 
> ## build IcaSet objects
> icaSettoy1 <- buildIcaSet(params=params, A=data.frame(resJade1$A), S=data.frame(resJade1$S),
+                           dat=dat1, alreadyAnnot=TRUE)$icaSet
> icaSettoy2 <- buildIcaSet(params=params, A=data.frame(resJade2$A), S=data.frame(resJade2$S),
+                           dat=dat2, alreadyAnnot=TRUE)$icaSet
> 
> ## compare IcaSet objects
> ## use tkplot=TRUE to get an interactive graph
> rescomp <- runCompareIcaSets(icaSets=list(icaSettoy1, icaSettoy2), labAn=c("toy1","toy2"),
+                              type.corr="pearson", level="genes", tkplot=FALSE)
Warning messages:
1: In brewer.pal(nbAn, "Set3") :
  minimal value for n is 3, returning requested palette with 3 different levels

2: In layout_with_fr(list(6, TRUE, c(0, 1, 2, 3, 4, 5), c(3, 3, 4,  :
  Argument `area' is deprecated and has no effect
3: In layout_with_fr(list(6, TRUE, c(0, 1, 2, 3, 4, 5), c(3, 3, 4,  :
  Argument `repulserad' is deprecated and has no effect
> 
> 
> ## Not run: 
> ##D ## load the microarray-based gene expression datasets
> ##D ## of breast tumors
> ##D library(breastCancerMAINZ)
> ##D library(breastCancerVDX)
> ##D data(mainz)
> ##D data(vdx)
> ##D 
> ##D ## Define a function used to build two examples of IcaSet objects
> ##D ## and annotate the probe sets into gene Symbols
> ##D treat <- function(es, annot="hgu133a.db") {
> ##D    es <- selectFeatures_IQR(es,10000)
> ##D    exprs(es) <- t(apply(exprs(es),1,scale,scale=FALSE))
> ##D    colnames(exprs(es)) <- sampleNames(es)
> ##D    resJade <- runICA(X=exprs(es), nbComp=10, method = "JADE", maxit=10000)
> ##D    resBuild <- buildIcaSet(params=buildMineICAParams(), A=data.frame(resJade$A), S=data.frame(resJade$S),
> ##D                         dat=exprs(es), pData=pData(es), refSamples=character(0),
> ##D                         annotation=annot, typeID= typeIDmainz,
> ##D                         chipManu = "affymetrix", mart=mart)
> ##D    icaSet <- resBuild$icaSet
> ##D }
> ##D ## Build the two IcaSet objects
> ##D icaSetMainz <- treat(mainz)
> ##D icaSetVdx <- treat(vdx)
> ##D 
> ##D ## compare the IcaSets
> ##D runCompareIcaSets(icaSets=list(icaSetMainz, icaSetVdx), labAn=c("Mainz","Vdx"), type.corr="pearson", level="genes")
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>