plotRLDF: Plot of regularized linear discriminant functions for microarray data

Description

Plot of regularized linear discriminant functions for microarray data.

Usage

plotRLDF(y,design=NULL,z=NULL,labels.y=NULL,labels.z=NULL,col.y=1,col.z=1,
df.prior=5,show.dimensions=c(1,2),main=NULL,nprobes=500,...)

Arguments

any data object which can be coerced to a matrix, such as ExpressionSet or EList. The training dataset.

any data object which can be coerced to a matrix, such as ExpressionSet or EList. The dataset to be classified.

design

the design matrix ofthe microarray experiment for y, with rows corresponding to arrays and columns to coefficients to be estimated. Defaults to the unit vector meaning that the arrays are treated as replicates.

labels.y

character vector of sample names or labels in y. Default is integers starting from 1.

labels.z

character vector of sample names or labels in z. Default is letters.

col.y

numeric or character vector of colors for the plotting characters of y. Default is black.

col.z

numeric or character vector of colors for the plotting characters of z. Default is black.

df.prior

prior degrees of freedom for residual variances. Used in gene selection.

show.dimensions

which two dimensions should be plotted, numeric vector of length two.

main

title of the plot.

nprobes

number of probes to be used for the calculations. Selected by moderated F tests.

...

any other arguments are passed to plot.

Value

A plot is created on the current graphics device.

Details

This function is a variation on the plot of usual linear discriminant fuction, in that the within-group covariance matrix is regularized to ensure that it is invertible, with eigenvalues bounded away from zero. A diagonal regulation using df.prior and the median within-group variance is used.

The calculations are based on a filtered list of probes. The nprobes probes with largest moderated F statistics are used to discriminate.

See text for possible values for col and cex.

Examples

Run this code


# Simulate gene expression data for 1000 probes and 6 microarrays.
# Samples are in two groups
# First 50 probes are differentially expressed in second group
sd <- 0.3*sqrt(4/rchisq(1000,df=4))
y <- matrix(rnorm(1000*6,sd=sd),1000,6)
rownames(y) <- paste("Gene",1:1000)
y[1:50,4:6] <- y[1:50,4:6] + 2

z <- matrix(rnorm(1000*6,sd=sd),1000,6)
rownames(z) <- paste("Gene",1:1000)
z[1:50,4:6] <- z[1:50,4:6] + 1.8
z[1:50,1:3] <- z[1:50,1:3] - 0.2

design <- cbind(Grp1=1,Grp2vs1=c(0,0,0,1,1,1))
options(digit=3)

plotRLDF(y,z, design=design)