MultiTtest-class {ClassComparison}R Documentation

The MultiTtest Class

Description

Class to perform row-by-row t-tests on microarray or proteomics data.

Usage

MultiTtest(data, classes)
## S4 method for signature 'MultiTtest':
summary(object, ...)
## S4 method for signature 'MultiTtest':
hist(x, xlab='T Statistics', main=NULL, ...)
## S4 method for signature 'MultiTtest, missing':
plot(x, ylab='T statistics', ...)
## S4 method for signature 'MultiTtest, ANY':
plot(x, y, xlab='T Statistics, ylab=deparse(substitute(y)), ...)

Arguments

data Either a data frame or matrix with numeric values or an exprSet as defined in the BioConductor tools for analyzing microarray data.
classes If data is a data frame or matrix, then classes must be either a logical vector or a factor. If data is an exprSet, then classes can be a character string that names one of the factor columns in the associated phenoData subobject.
object A MultiTtest object
x A MultiTtest object
y A numeric vector
xlab Label for the x axis
ylab Label for the y axis
main Plot title
... The usual graphical parameters can by supplied to hist and plot methods.

Value

The graphical routines invisibly return the object against which they were invoked.

Creating objects

Although objects can be created using new, the better method is to use the MultiTtest function. In the simplest case, you simply pass in a data matrix and a logical vector assigning classes to the columns, and the constructor performs row-by-row two-sample t-tests and computes the associated (single test) p-values. To adjust for multiple testing, you can pass the p-values on to the Bum class.

If you use a factor instead of a logical vector, then the t-test compares the first level of trhe factor to everything else. To handle the case of multiple classes, see the MultiLinearModel class.

As with other class comparison functions that are part of the OOMPA, we can also perform statistical tests on exprSet objects from the BioConductor libraries. In this case, we pass in an exprSet object along with the name of a factor to use for splitting the data.

Slots

t.statistics:
Object of class numeric containing the computed t-statistics.
p.values:
Object of class numeric containing the computed p-values.
groups:
Object of class character containing the names of the classes being compared.
call:
Object of class call containing the function call that created the object.

Methods

summary(object, ...)
Write out a summary of the object.
hist(x, xlab='T Statistics', main=NULL, ...)
Produce a histogram of the t-statistics.
plot(x
Produces a scatter plot of the t-statistics against their index.
plot(x,y)
Produces a scatter plot of the t-statistics in the object x against the numeric vector y.

Author(s)

Kevin R. Coombes <kcoombes@mdanderson.org>

See Also

matrixT, Bum, MultiLinearModel, Dudoit

Examples

ng <- 10000
ns <- 50
dat <- matrix(rnorm(ng*ns), ncol=ns)
cla <- factor(rep(c('A', 'B'), each=25))
res <- MultiTtest(dat, cla)
summary(res)
hist(res, breaks=101)
plot(res)
plot(res, res@p.values)
hist(res@p.values, breaks=101)

rm(ng, ns, dat, cla, res)
[Package ClassComparison version 1.1 Index]