simpleaffy (version 2.48.0)

hmap.eset: Draw a heatmap from an AffyBatch object

Description

Given either an AffyBatch draw a heatmap.

Usage

hmap.eset(x,probesets,samples=1:length(sampleNames(x)),scluster=standard.pearson,pcluster=standard.pearson,slabs=sampleNames(x)[samples],plabs,col="bwr",min.val=NULL ,max.val=NULL,scale=FALSE,spread=6,by.fc=F,sdev=NULL,show.legend=T,title=NULL,cex=0.5)

Arguments

x

The AffyBatch object to get the expression data from

probesets

What probesets to plot, defaults to all of them

samples

Which samples to plot

scluster

The function to use to cluster the samples by. Can also be a dendrogram object.

pcluster

The function to use to cluster the probesets by. Can also be a dendrogram object.

slabs

Labels for the sample axis

plabs

Labels for the probeset axis defaults to geneNames(x)

col

Vector of colour values to use (see below)

min.val

The minimum intensity to plot

max.val

The maximum intensity to plot

scale

Scale each gene's clouring based on standard deviation (See below)

spread

If the data is scaled, how many standard deviations (or fold changes) either way should we show. If no scaling, then does nothing

by.fc

If the data is scaled, scale by s.d. or by fold.change?

sdev

A vector of standard deviaitions for each gene to be plotted. If no value is supplied these are worked out from the data.

show.legend

Draw a scale on the graph and show the title if supplied

title

The title of the graph

cex

Character expansion

Value

Details

Takes an AffyBatch object and plots a heatmap. At its simplest, all that is required is an AffyBatch object (as calculated by call.exprs) and a vector supplying the probesets to plot. These can be specified by name, as an integer index or as a vector of TRUEs and FALSES. The function will try to do something sensible with the labels. If it fails you will need to specify this with plabs. The function will then draw a heatmap, coloured blue-white-red in increasing intensity, scaled so that 100

Col can be used to change the colouring. "bwr" specifies blue-white-red, "rbg" specifies red-black-green, and "ryw" specifies red-yellow-white. Alternatively, a vector of arbitrary colours can be supplied (try rainbow(21), for example).

The clustering method can also be changed by supplying, either, a function that takes a matrix of expression values and returns an hclust or dendrogram object, or alternatively, an hclust or dendrogram object itself. Setting either of these to NULL will stop the heatmap being clustered on that axis.

Scaling is somewhat more complex. If scale is TRUE, then each gene is coloured independently, on a scale based on its standard deviation. By default this is calculated for the samples that are being plotted, unless a value is supplied for sdev -- in which case this should be a vector of standard deviations, one for each probeset being plotted (and in the same order). This scaling is done after the clustering. For more details on how all of this works see the website http://bioinf.picr.man.ac.uk/simpleaffy and also look at hmap.pc which uses the scaling to plot transcripts identified as being differentially expressed.

See Also

hmap.pc blue.white.red.cols standard.pearson

Examples

  ## Not run:  
#     eset.mas <- call.exprs(eset,"mas5")
#     hmap.eset(eset.mas,1:100,1:6,col="rbg")
#   ## End(Not run)