Heatmap of gene expression values
Create a heatmap of a matrix of log-expression values.
coolmap(x, cluster.by="de pattern", col=NULL,
linkage.row="complete", linkage.col="complete", show.dendrogram="both", ...)x |
any data object that can be coerced to a matrix of log-expression values, for example an |
cluster.by |
choices are |
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 |
linkage.row |
linkage criterion used to cluster the rows.
Choices are |
linkage.col |
linkage criterion used to cluster the columns.
Choices are the same as for |
show.dendrogram |
choices are |
... |
any other arguments are passed to |
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.
A plot is created on the current graphics device.
A list is also invisibly returned, see heatmap.2 for details.
Gordon Smyth
An overview of diagnostic functions available in LIMMA is given in 09.Diagnostics.
# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.