coolmap: Heatmap of gene expression values

In hdeberg/limma: Linear Models for Microarray Data

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. See details for which arguments are reserved.

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.

coolmap sets the following heatmap.2 arguments internally: Rowv, Colv, scale, density.info, trace, col, symbreaks, symkey, dendrogram, key.title and key.xlab. These arguments are therefore reserved and cannot be varied. Other than these reserved arguments, any other heatmap.2 argument can be included in the coolmap call, thereby giving full access to heatmap.2 functionality.

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)

hdeberg/limma documentation built on Dec. 20, 2021, 3:43 p.m.