plotContrastHeatmap,ClusterExperiment-method {clusterExperiment} | R Documentation

## Plot heatmaps showing significant genes per contrast

### Description

Plots a heatmap of the data, with the genes grouped based on the contrast for which they were significant.

### Usage

    ## S4 method for signature 'ClusterExperiment'
    plotContrastHeatmap(
      object,
      signifTable,
      whichCluster = NULL,
      contrastColors = NULL,
      ...
    )

### Arguments

`object` | ClusterExperiment object on which biomarkers were found
`signifTable` | A `data.frame` in format of the result of `getBestFeatures`. It must minimally contain columns 'Contrast' and 'IndexInOriginal' giving the grouping and original index of the features in the `assay(object)`
`whichCluster` | if not NULL, indicates cluster used in making the significance table. Used to match to colors in `clusterLegend(object)` (relevant for one-vs-all contrast so that color aligns). See description of argument in `getClusterIndex` for futher details.
`contrastColors` | vector of colors to be given to contrasts. Should match the name of the contrasts in the 'Contrast' column of `signifTable` or 'ContrastName', if given.. If missing, default colors given by match to the cluster names of `whichCluster` (see above), or otherwise given a default assignment.
`...` | Arguments passed to `plotHeatmap`

### Details

If the column 'ContrastName' is given in `signifTable`, these names will be used to describe the contrast in the legend.

Within each contrast, the genes are sorted by log fold-change if the column "logFC" is in the `signifTable` data.frame

Note that if `whichCluster` is NOT given (the default) then there is no automatic match of colors with contrasts based on the information in `object`.

### Value

A heatmap is created. The output of `plotHeatmap` is returned.

### See Also

`plotHeatmap`, `makeBlankData`, `getBestFeatures`

### Examples

    data(simData)

    cl <- clusterSingle(simData, subsample=FALSE,
    sequential=FALSE,
    mainClusterArgs=list(clusterFunction="pam", clusterArgs=list(k=8)))

    #Do all pairwise, only return significant, try different adjustments:
    pairsPerC <- getBestFeatures(cl, contrastType="Pairs", number=5,
    p.value=0.05, DEMethod="limma")
    plotContrastHeatmap(cl,pairsPerC)

[Package clusterExperiment version 2.12.0 Index]