Plots graph using

Description

This function plots the correlation graph in an interactive device using function tkplot.

Usage

  plotCorGraph(dataGraph, edgeWeight = "cor", nodeAttrs,
    nodeShape, nodeCol = "labAn", nodeName = "indComp",
    col, shape, title = "", reciproCol = "reciprocal",
    tkplot = FALSE, ...)

Arguments

Details

You have to slighly move the nodes to see cliques because strongly related nodes are often superimposed. The edgeWeight column is used to weight the edges within the fruchterman.reingold layout available in the package igraph.

The argument nodeCol typically denotes the column containing the names of the datasets. Colors are automatically attributed to the nodes using palette Set3 of package RColorBrewer. The corresponding colors can be directly specified in the 'col' argument. In that case, 'col' must be a vector of colors indexed by the unique elements contained in nodeCol column (e.g dataset ids).

As for colors, one can define the column of nodeAttrs that is used to define the node shapes. The corresponding shapes can be directly specified in the shape argument. In that case, shape must be one of c("circle","square", " vcsquare", "rectangle", "crectangle", "vrectangle") and must be indexed by the unique elements of nodeShape column.

Unfortunately, shapes can't be taken into account when tkplot is TRUE (interactive plot).

If reciproCol is not missing, it is used to color the edges, either in grey if the edge is not reciprocal or in black if the edge is reciprocal.

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 using tkplot

Author(s)

Anne Biton

See Also

compareAn, nodeAttrs, compareAn2graphfile, 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 object
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
icaSets <- list(icaSettoy1, icaSettoy2)

resCompareAn <- compareAn(icaSets=list(icaSettoy1,icaSettoy2), labAn=c("toy1","toy2"),
                         type.corr="pearson", level="genes", cutoff_zval=0)

## Build a graph where edges correspond to maximal correlation value (useVal="cor"),
dataGraph <- compareAn2graphfile(listPairCor=resCompareAn, useMax=TRUE, useVal="cor", file="myGraph.txt")

## construction of the data.frame with the node description
nbComp <- rep(3,2) #each IcaSet contains 3 components
nbAn <- 2 # we are comparing 2 IcaSets
# labels of components created as comp*i*
labComp <- foreach(icaSet=icaSets, nb=nbComp, an=1:nbAn) %do% {
                  paste(rep("comp",sum(nb)),1:nbComp(icaSet),sep = "")}

# creation of the data.frame with the node description
nodeDescr <- nodeAttrs(nbAn = nbAn, nbComp = nbComp, labComp = labComp,
                       labAn = c("toy1","toy2"), file = "nodeInfo.txt")

## Plot correlation graph, slightly move the attached nodes to make the cliques visible
## use tkplot=TRUE to have an interactive graph
res <- plotCorGraph(title = "Compare toy 1 and 2", dataGraph = dataGraph, nodeName = "indComp", tkplot = FALSE,
                 nodeAttrs = nodeDescr, edgeWeight = "cor", nodeShape = "labAn", reciproCol = "reciprocal")


## Not run: 
## load two microarray datasets
library(breastCancerMAINZ)
library(breastCancerVDX)
data(mainz)
data(vdx)

## Define a function used to build two examples of IcaSet objects
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)

icaSets <- list(icaSetMainz, icaSetVdx)
labAn <- c("Mainz", "Vdx")

## correlations between gene projections of each pair of IcaSet
resCompareAn <- compareAn(icaSets = icaSets, level = "genes", type.corr= "pearson",
                          labAn = labAn, cutoff_zval=0)

## construction of the correlation graph using previous output
dataGraph <- compareAn2graphfile(listPairCor=resCompareAn, useMax=TRUE, file="corGraph.txt")

## construction of the data.frame with the node description
nbComp <- rep(10,2) #each IcaSet contains 10 components
nbAn <- 2 # we are comparing 2 IcaSets
# labels of components created as comp*i*
labComp <- foreach(icaSet=icaSets, nb=nbComp, an=1:nbAn) %do% {
                  paste(rep("comp",sum(nb)),1:nbComp(icaSet),sep = "")}

# creation of the data.frame with the node description
nodeDescr <- nodeAttrs(nbAn = nbAn, nbComp = nbComp, labComp = labComp,
    labAn = labAn, file = "nodeInfo.txt")

## Plot correlation graph, slightly move the attached nodes to make the cliques visible
res <- plotCorGraph(title = "Compare two ICA decomsitions obtained on \n two
                 microarray-based data of breast tumors", dataGraph = dataGraph, nodeName = "indComp",
                 nodeAttrs = nodeDescr, edgeWeight = "cor", nodeShape = "labAn", reciproCol = "reciprocal")


## 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':

    IQR, mad, xtabs

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

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    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
Loading required package: ggplot2
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Loading required package: foreach
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Loading required package: gtools
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Loading required package: AnnotationDbi
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Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following object is masked from 'package:plyr':

    rename

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    colMeans, colSums, expand.grid, rowMeans, rowSums


Attaching package: 'IRanges'

The following object is masked from 'package:plyr':

    desc

Loading required package: Matrix

Attaching package: 'Matrix'

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    expand

Loading required package: graph

Attaching package: 'graph'

The following object is masked from 'package:plyr':

    join



Attaching package: 'GOstats'

The following object is masked from 'package:AnnotationDbi':

    makeGOGraph

Loading required package: cluster
Loading required package: marray
Loading required package: limma

Attaching package: 'limma'

The following object is masked from 'package:BiocGenerics':

    plotMA

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

Attaching package: 'igraph'

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

    degree, edges, intersection, union

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

    simplify, union

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    compare, union

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    permute

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    normalize, union

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    decompose, spectrum

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    union

Loading required package: Rgraphviz
Loading required package: grid

Attaching package: 'Rgraphviz'

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    from, to

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    from, to

Loading required package: annotate
Loading required package: XML

Attaching package: 'XML'

The following object is masked from 'package:graph':

    addNode


Attaching package: 'annotate'

The following object is masked from 'package:Rgraphviz':

    toFile

Loading required package: Hmisc
Loading required package: lattice
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Attaching package: 'Hmisc'

The following object is masked from 'package:AnnotationDbi':

    contents

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    label, label<-

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    is.discrete, summarize

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    combine, contents

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    combine

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    format.pval, round.POSIXt, trunc.POSIXt, units

Loading required package: fastICA
Loading required package: JADE
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MineICA/plotCorGraph.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotCorGraph
> ### Title: Plots graph using
> ### Aliases: 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 object
> 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
> icaSets <- list(icaSettoy1, icaSettoy2)
> 
> resCompareAn <- compareAn(icaSets=list(icaSettoy1,icaSettoy2), labAn=c("toy1","toy2"),
+                          type.corr="pearson", level="genes", cutoff_zval=0)
> 
> ## Build a graph where edges correspond to maximal correlation value (useVal="cor"),
> dataGraph <- compareAn2graphfile(listPairCor=resCompareAn, useMax=TRUE, useVal="cor", file="myGraph.txt")
> 
> ## construction of the data.frame with the node description
> nbComp <- rep(3,2) #each IcaSet contains 3 components
> nbAn <- 2 # we are comparing 2 IcaSets
> # labels of components created as comp*i*
> labComp <- foreach(icaSet=icaSets, nb=nbComp, an=1:nbAn) %do% {
+                   paste(rep("comp",sum(nb)),1:nbComp(icaSet),sep = "")}
> 
> # creation of the data.frame with the node description
> nodeDescr <- nodeAttrs(nbAn = nbAn, nbComp = nbComp, labComp = labComp,
+                        labAn = c("toy1","toy2"), file = "nodeInfo.txt")
> 
> ## Plot correlation graph, slightly move the attached nodes to make the cliques visible
> ## use tkplot=TRUE to have an interactive graph
> res <- plotCorGraph(title = "Compare toy 1 and 2", dataGraph = dataGraph, nodeName = "indComp", tkplot = FALSE,
+                  nodeAttrs = nodeDescr, edgeWeight = "cor", nodeShape = "labAn", reciproCol = "reciprocal")
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(5, 4, 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(5, 4, 4,  :
  Argument `repulserad' is deprecated and has no effect
> 
> 
> ## Not run: 
> ##D ## load two microarray datasets
> ##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 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 icaSets <- list(icaSetMainz, icaSetVdx)
> ##D labAn <- c("Mainz", "Vdx")
> ##D 
> ##D ## correlations between gene projections of each pair of IcaSet
> ##D resCompareAn <- compareAn(icaSets = icaSets, level = "genes", type.corr= "pearson",
> ##D                           labAn = labAn, cutoff_zval=0)
> ##D 
> ##D ## construction of the correlation graph using previous output
> ##D dataGraph <- compareAn2graphfile(listPairCor=resCompareAn, useMax=TRUE, file="corGraph.txt")
> ##D 
> ##D ## construction of the data.frame with the node description
> ##D nbComp <- rep(10,2) #each IcaSet contains 10 components
> ##D nbAn <- 2 # we are comparing 2 IcaSets
> ##D # labels of components created as comp*i*
> ##D labComp <- foreach(icaSet=icaSets, nb=nbComp, an=1:nbAn) %do% {
> ##D                   paste(rep("comp",sum(nb)),1:nbComp(icaSet),sep = "")}
> ##D 
> ##D # creation of the data.frame with the node description
> ##D nodeDescr <- nodeAttrs(nbAn = nbAn, nbComp = nbComp, labComp = labComp,
> ##D     labAn = labAn, file = "nodeInfo.txt")
> ##D 
> ##D ## Plot correlation graph, slightly move the attached nodes to make the cliques visible
> ##D res <- plotCorGraph(title = "Compare two ICA decomsitions obtained on \n two
> ##D                  microarray-based data of breast tumors", dataGraph = dataGraph, nodeName = "indComp",
> ##D                  nodeAttrs = nodeDescr, edgeWeight = "cor", nodeShape = "labAn", reciproCol = "reciprocal")
> ##D 
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>