simpleaffy (version 2.48.0)

hmap.pc: Draw a heatmap from an PairComp object

Description

Given either a PairComp object draw a heatmap.

Usage

hmap.pc(x,eset,samples=rownames(pData(x)),scluster=standard.pearson,pcluster=standard.pearson,slabs,plabs,col="rbg",scale=T,spread=10,by.fc=F,gp=group(x),mbrs=members(x),show.legend=T,title=NULL,cex=0.1)

Arguments

x

The PairComp object to get the probeset list (and other data) from

eset

The AffyBatch object containing expression data

samples

Which samples to plot -- defaults to those used to calculate 'x', but can be any of the samples in eset

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

col

Vector of colour values to use (see below)

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, do it by fold change?

gp

The column in the expression set's pData object used to select the samples to plot. By default this is the one used to calculate x.

mbrs

The members of the 'group' column that we wish to plot. By default these are the pair used to calculate x. If 'all' is supplied then all samples are used.

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 a PairComp object and an AffyBatch object and plots a heatmap. At its simplest, all that is required are these two objects. The function will then draw a heatmap, coloured red-black-green in increasing intensity, scaled for each gene based on standard deviation. The legend shows how these colours translate into intensity.

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).

Scaling is somewhat complex. If scale is TRUE, then each gene is coloured independently, on a scale based on its standard deviation. This is calculated as follows: 'group' supplies a column in the pData object of 'eset' that is used to collect samples together (generally as replicate groups). 'members' supplies the entries within this column that are to be used. (Unless specified, the function uses the same value for 'group' and 'members' used to calculate the PairComp object). The function uses these data to calculate the standard deviation for each probeset within each set of replicates, and then calculates the average sd for each gene. This is then used to scale the data so that each probeset is plotted on a scale that shows the number of standard deviations away from the mean it is for that sample. For more details on how all of this works see the website http://bioinf.picr.man.ac.uk/simpleaffy.

Alternatively, by setting by.fc to FALSE, scaling can be done simply in terms of fold-change, in which case, spread defines the maximum and minimum fold changes to show.

See Also

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

Examples

  ## Not run: 
#     pc <- pairwise.comparison(eset.mas,group="group",members=c("a","b"),spots=eset)
#     pf <- pairwise.filter(pc)
#     hmap.pc(pf,eset.mas)
#   ## End(Not run)