plotRLDF: Plot of regularized linear discriminant functions for...

In limma: Linear Models for Microarray Data

Description

Plot regularized linear discriminant functions for classifying samples based on expression data.

Usage

plotRLDF(y, design = NULL, z = NULL, nprobes = 100, plot = TRUE,
         labels.y = NULL, labels.z = NULL, pch.y = NULL, pch.z = NULL,
         col.y = "black", col.z = "black",
         show.dimensions = c(1,2), ndim = max(show.dimensions),
         var.prior = NULL, df.prior = NULL, trend = FALSE, robust = FALSE, ...)

Arguments

y	the training dataset. Can be any data object which can be coerced to a matrix, such as ExpressionSet or EList.
design	design matrix defining the training groups to be distinguished. The first column is assumed to represent the intercept. Defaults to model.matrix(~factor(labels.y)).
z	the dataset to be classified. Can be any data object which can be coerced to a matrix, such as ExpressionSet or EList. Rows must correspond to rows of y.
nprobes	number of probes to be used for the calculations. The probes will be selected by moderated F statistic.
plot	logical, should a plot be created?
labels.y	character vector of sample names or labels in y. Defaults to colnames(y) or failing that to 1:n.
labels.z	character vector of sample names or labels in z. Defaults to colnames(z) or failing that to letters[1:n].
pch.y	plotting symbol or symbols for y. See points for possible values. Takes precedence over labels.y if both are specified.
pch.z	plotting symbol or symbols for y. See points for possible values. Takes precedence over labels.z if both are specified.
col.y	colors for the plotting labels.y.
col.z	colors for the plotting labels.z.
show.dimensions	integer vector of length two indicating which two discriminant functions to plot. Functions are in decreasing order of discriminatory power.
ndim	number of discriminant functions to compute
var.prior	prior variances, for regularizing the within-group covariance matrix. By default is estimated by squeezeVar.
df.prior	prior degrees of freedom for regularizing the within-group covariance matrix. By default is estimated by squeezeVar.
trend	logical, should a trend be estimated for var.prior? See eBayes for details. Only used if var.prior or df.prior are NULL.
robust	logical, should var.prior and df.prior be estimated robustly? See eBayes for details. Only used if var.prior or df.prior are NULL.
...	any other arguments are passed to plot.

Details

The function builds discriminant functions from the training data (y) and applies them to the test data (z). The method is a variation on classifical linear discriminant functions (LDFs), in that the within-group covariance matrix is regularized to ensure that it is invertible, with eigenvalues bounded away from zero. The within-group covariance matrix is squeezed towards a diagonal matrix with empirical Bayes posterior variances as diagonal elements.

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

The ndim argument allows all required LDFs to be computed even though only two are plotted.

Value

If plot=TRUE a plot is created on the current graphics device. A list containing the following components is (invisibly) returned:

training	numeric matrix with ncol(y) rows and ndim columns containing discriminant functions evaluated for the training data.
predicting	numeric matrix with ncol(z) rows and ndim columns containing discriminant functions evalulated on the classification data.
top	integer vector of length nprobes giving indices of probes used.
metagenes	numeric matrix with nprobes rows and ndim columns containing probe weights defining each discriminant function.
singular.values	singular.values showing the predictive power of each discriminant function.
rank	maximum number of discriminant functions with singular.values greater than zero.
var.prior	numeric vector of prior variances.
df.prior	numeric vector of prior degrees of freedom.

Note

The default values for df.prior and var.prior were changed in limma 3.27.10. Previously these were preset values. Now the default is to estimate them using squeezeVar.

Author(s)

Gordon Smyth, Di Wu and Yifang Hu

See Also

lda in package MASS

Examples

# 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)

# Samples 1-6 are training set, samples a-f are test set:
plotRLDF(y, design, z=z, col.y="black", col.z="red")
legend("top", pch=16, col=c("black","red"), legend=c("Training","Predicted"))

Example output

limma documentation built on Nov. 8, 2020, 8:28 p.m.