compareAn {MineICA} | R Documentation

## Comparison of IcaSet objects using correlation

### Description

Compare `IcaSet` objects by computing the correlation between either projection values of common features or genes, or contributions of common samples.

### Usage

      compareAn(icaSets, labAn,
        type.corr = c("pearson", "spearman"), cutoff_zval = 0,
        level = c("samples", "features", "genes"))

### Arguments

`icaSets` | list of IcaSet objects, e.g results of ICA decompositions obtained on several datasets.
`labAn` | vector of names for each icaSet, e.g the the names of the datasets on which were calculated the decompositions.
`type.corr` | Type of correlation to compute, either `'pearson'` or `'spearman'`.
`cutoff_zval` | either NULL or 0 (default) if all genes are used to compute the correlation between the components, or a threshold to compute the correlation on the genes that have at least a scaled projection higher than cutoff_zval. Will be used only when correlations are calculated on S or SByGene.
`level` | Data level of the `IcaSet` objects on which is applied the correlation. It must correspond to a feature shared by the IcaSet objects: `'samples'` if they were applied to common samples (correlations are computed between matrix `A`), `'features'` if they were applied to common features (correlations are computed between matrix `S`), `'genes'` if they share gene IDs after annotation into genes (correlations are computed between matrix `SByGene`).

### Details

The user must carefully choose the object on which the correlation will be computed. 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('genes','features')`, 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 `0`, the computation will be much slowler since each pair of component is treated individually.

### Value

A list whose length equals the number of pairs of `IcaSet` and whose elements are outputs of function `cor2An`.

### Author(s)

Anne Biton

### See Also

`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

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

    ## 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)

    ## The pearson correlation is used as a measure of association between the gene projections
    # on the different components (type.corr="pearson").
    listPairCor <- 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).
    listPairCor <-  compareAn(icaSets=list(icaSetMainz,icaSetVdx),
    labAn=c("Mainz","Vdx"), type.corr="pearson", cutoff_zval=1, level="genes")

    ## End(Not run)

[Package MineICA version 1.26.0 Index]