compareAn2graphfile: compareAn2graphfile

Description

This function builds a correlation graph from the outputs of function compareAn.

Usage

compareAn2graphfile(listPairCor, useMax = TRUE, cutoff = NULL, useVal = c("cor", "pval"), file = NULL)

Arguments

listPairCor

The output of the function compareAn, containing the correlation between several pairs of objects of class IcaSet.

useMax

If TRUE, the graph is restricted to edges that correspond to maximum score, see details

cutoff

Cutoff used to select pairs that will be included in the graph.

useVal

The value on which is based the graph, either "cor" for correlation or "pval" for p-values of correlation tests.

file

File name.

Value

A data.frame with the graph description, has two columns n1 and n2 filled with node IDs, each row denotes that there is an edge from n1 to n2. Additional columns quantify the strength of association: correlation (cor), p-value (pval), (1-abs(cor)) (distcor), log10-pvalue (logpval).

Details

When correlations are considered (useVal="cor"), absolute values are used since the components have no direction.

If useMax is TRUE each component is linked to the most correlated component of each different IcaSet.

If cutoff is specified, only correlations exceeding this value are taken into account during the graph construction. For example, if cutoff is 1, only relationships between components that correspond to a correlation value larger than 1 will be included.

When useVal="pval" and useMax=TRUE, the minimum value is taken instead of the maximum.

Examples

Run this code

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

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"),
compareAn2graphfile(listPairCor=resCompareAn, useMax=TRUE, useVal="cor", file="myGraph.txt")


## Not run: 
# #### Comparison of 2 ICA decompositions obtained on 2 different gene expression datasets.
# ## load the two 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)
# 
# ## Compute correlation between every pair of IcaSet objects.
# resCompareAn <- compareAn(icaSets=list(icaSetMainz,icaSetVdx),
# labAn=c("Mainz","Vdx"), type.corr="pearson", level="genes", cutoff_zval=0)
# 
# ## Same thing but adding a selection of genes on which the correlation between two components is computed:
# # when considering pairs of components, only projections whose scaled values are not located within
# # the circle of radius 1 are used to compute the correlation (cutoff_zval=1).
# resCompareAn <-  compareAn(icaSets=list(icaSetMainz,icaSetVdx),
# labAn=c("Mainz","Vdx"), type.corr="pearson", cutoff_zval=1, level="genes")
# 
# ## Build a graph where edges correspond to maximal correlation value (useVal="cor"),
# ## i.e, component A of analysis i is linked to component B of analysis j,
# ## only if component B is the most correlated component to A amongst all component of analysis j.
# compareAn2graphfile(listPairCor=resCompareAn, useMax=TRUE, useVal="cor", file="myGraph.txt")
# 
# ## Restrict the graph to correlation values exceeding 0.4
# compareAn2graphfile(listPairCor=resCompareAn, useMax=FALSE, cutoff=0.4,  useVal="cor", file="myGraph.txt")
# 
# ## End(Not run)