coolmap {limma} | R Documentation

## Heatmap of gene expression values

### Description

Create a heatmap of a matrix of log-expression values.

### Usage

    coolmap(x, cluster.by="de pattern", col=NULL,
            linkage.row="complete", linkage.col="complete", show.dendrogram="both", ...)

### Arguments

`x` | any data object that can be coerced to a matrix of log-expression values, for example an `ExpressionSet` or `EList`. Rows represent genes and columns represent RNA samples.
`cluster.by` | choices are `"de pattern"` or `"expression level"`. In the former case, the intention is to cluster by relative changes in expression, so genes are clustered by Pearson correlation and log-expression values are mean-corrected by rows for the plot. In the latter case, the intention is to cluster by absolute expression, so genes are clustered by Euclidean and log-expression values are not mean-corrected.
`col` | character vector specifying the color panel. Can be either the name of the panel or a vector of R colors that can be passed directly to the `heatmap.2` function. Possible panel names are `"redblue"`, `"redgreen"`, `"yellowblue"` or `"whitered"`. Defaults to `"redblue"` if `cluster.by="de pattern"` or `"yellowblue"` if `cluster.by="expression level"`.
`linkage.row` | linkage criterion used to cluster the rows. Choices are `"none"`, `"ward"`, `"ward.D"`, `"ward.D2"`, `"single"`, `"complete"`, `"average"`, `"mcquitty"`, `"median"` or `"centroid"`, with `"ward"` treated as `"ward.D2"`.
`linkage.col` | linkage criterion used to cluster the columns. Choices are the same as for `linkage.row`.
`show.dendrogram` | choices are `"row"`, `"column"`, `"both"` or `"none"`.
`...` | any other arguments are passed to `heatmap.2`.

### Details

This function calls the `heatmap.2` function in the ggplots package with sensible argument settings for genomic log-expression data. The default settings for `heatmap.2` are often not ideal for expression data, and overriding the defaults requires explicit calls to `hclust` and `as.dendrogram` as well as prior standardization of the data values. The `coolmap` function implements our preferred defaults for the two most common types of heatmaps. When clustering by relative expression (`cluster.by="de pattern"`), it implements a row standardization that takes account of `NA` values and standard deviations that might be zero.

### Value

A plot is created on the current graphics device. A list is also invisibly returned, see `heatmap.2` for details.

### Author(s)

Gordon Smyth

### See Also

`heatmap.2`, `hclust`, `dist`.

An overview of diagnostic functions available in LIMMA is given in 09.Diagnostics.

### Examples

    # Simulate gene expression data for 50 genes and 6 microarrays.
    # Samples are in two groups
    # First 50 probes are differentially expressed in second group
    ngenes <- 50
    sd <- 0.3*sqrt(4/rchisq(ngenes,df=4))
    x <- matrix(rnorm(ngenes*6,sd=sd),ngenes,6)
    rownames(x) <- paste("Gene",1:ngenes)
    x <- x + seq(from=0, to=16, length=ngenes)
    x[,4:6] <- x[,4:6] + 2
    coolmap(x)

[Package limma version 3.44.3 Index]