heatmapPhenoTest {phenoTest} | R Documentation

## Produce heatmap from phenotype data.

### Description

Show the associations between clusters that each sample belongs to and each phenotype in a heatmap and/or a Kaplan-Meier plot.

### Usage

    heatmapPhenoTest(x, signatures, vars2test, probes2genes = FALSE,
    filterVar, filteralpha = 0.05, distCol = "pearson", nClust = 2, distRow
    = "cor", p.adjust.method = "none", simulate.p.value = FALSE, B = 10^5,
    linkage = "average", equalize = FALSE, center = TRUE, col, survCol,
    heat.kaplan="both", ...)

### Arguments

`x` | ExpressionSet with phenotype information stored in `pData(x)`.
`signatures` | Either character vector or list of character vectors with gene sets to be used to draw heatmaps (gene names should match those in `featureNames(x)`). A separate heatmap will be produced for each element in the list.
`vars2test` | list with components 'continuous', 'categorical', 'ordinal' and 'survival' indicating which phenotype variables should be tested. 'continuous', 'categorical' and 'ordinal' must be character vectors, 'survival' a matrix with columns named 'time' and 'event'. The names must match names in `names(pData(x))`.
`probes2genes` | If set to `TRUE` a single probe is selected for each gene. `nsFilter` is used to select the probe with highest inter-quartile range.
`filterVar` | If specified, only genes with significant differences in the variable `filterVar` will be displayed in the heatmap. Note that this option will not affec the sample clustering, as this is obtained using both significant and non-significant genes.
`filteralpha` | Significance level for the filtering based on `filterVar`.
`distCol` | Distance metric used to cluster columns (e.g. patients/samples). Can take any value accepted by `dist`. Pearson and Spearman correlations are also allowed. Write 'spearman' or 'pearson' to use them.
`nClust` | Number of desired clusters.
`distRow` | Distance metric used to cluster rows (e.g. genes). Can take any value accepted by `distancematrix`.
`p.adjust.method` | Method for P-value adjustment, passed on to `p.adjust`.
`simulate.p.value` | If set to FALSE the chi-square test p-values are computed using asymptotics, otherwise a simulation is used (see `chisq.test` for details).
`B` | An integer specifying the number of replicates used in the chi-square Monte Carlo test (passed on to `chisq.test`).
`linkage` | Linkage used for clustering. Must be either 'complete', 'average' or 'minimum'.
`equalize` | Should color codes be equalized between genes, i.e. all genes present the same range of colors. Passed on to `heatmap_plus`.
`center` | centering is done by subtracting the column means (omitting NAs).
`col` | Color scheme to be used for heatmap. Defaults to a green/red scheme designed to look nice for microarray data.
`survCol` | Colors for the Kaplan-Meier survival curves.
`heat.kaplan` | can be "heat" if we want to plot a heatmap, "kaplan" if we want to plot a kaplan-meier or "both" if we want both of them.
`...` | Other arguments for the survival plot, e.g. lty etc.

### Details

Makes two clusters of samples based on the expression levels of the genes from the given signature and plots a heatmap and/or a Kaplan-Meier showing the association between belonging to one cluster or the other and each phenotype.

For variables in vars2test\$continuous and vars2test\$ordinal a Kruskal-Wallis Rank Sum test is used; for vars2test\$categorical a chi-square test (with exact p-value if `simulate.p.value` is set to TRUE); for var2test\$survival a Cox proportional hazards likelihood-ratio test.

### Author(s)

David Rossell

### Examples

    #load data
    data(eset)
    eset

    #construct vars2test
    survival <- matrix(c("Relapse","Months2Relapse"),ncol=2,byrow=TRUE)
    colnames(survival) <- c('event','time')
    vars2test <- list(survival=survival)
    vars2test

    #construct a signature
    sign <- sample(featureNames(eset))[1:20]

    #make plot
    heatmapPhenoTest(eset,sign,vars2test=vars2test,heat.kaplan='heat')
    heatmapPhenoTest(eset,sign,vars2test=vars2test,heat.kaplan='kaplan')

[Package phenoTest version 1.40.0 Index]