compareAn2graphfile {MineICA} | R Documentation

## 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.

### 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.

### 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`).

### Author(s)

Anne Biton

### See Also

`compareAn`, `cor2An`

### 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

    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)

[Package MineICA version 1.26.0 Index]