The TNoM and fullTNoM Classes

Description

Implements the "Total Number of Misclassifications" method for finding differentially expressed genes.

Usage

TNoM(data, classes, verbose = TRUE)
## S4 method for signature 'TNoM':
summary(object, ...)
## S4 method for signature 'TNoM':
update(object, nPerm, verbose=FALSE, ...)
## S4 method for signature 'TNoM':
selectSignificant(object, cutoff, ...)
## S4 method for signature 'TNoM':
countSignificant(object, cutoff, ...)

## S4 method for signature 'fullTNoM, missing':
plot(x, ...)
## S4 method for signature 'fullTNoM':
hist(x, ...)

Arguments

Details

The TNoM method was developed by Yakhini and Ben-Dor and first applied in the melanoma microarray study by Bittner and colleagues (see references). The goal of the method is to detect genes that are differentially expressed between two groups of samples. The idea is that each gene serve as a potential classifier to distinguish the two groups. One starts by determining an optimal cutoff on the expression of each gene and counting the number of miscalssifications that gene makes. Next, we bin genes based on the total number of misclassifications. This distribution can be compared with the expected value (by simulating normal data sets of the same size). Alternatively, one can estimate the null distribution directly by srambling the sample labels to perform a permutation test.

The TNoM constructor computes the optimal cutoffs and the misclassification rates. The update method performs the simulations and permutation tests, producing an object of the fullTNoM class.

Value

summary returns a TNoMSummary object.
update returns a fullTNoM object.
selectSignificant returns a vector of logical values.
countSignificant returns an integer.

Creating Objects

As usual, objects can be created by new, but better methods are available in the form of the TNoM function. The inputs to this function are the same as those used for row-by-row statistical tests throughout the ClassComparison package; a detailed description can be found in the MultiTtest class.

Slots

Objects of the TNoM class have the following slots:

data:

The data matrix used to contruct the object

tnomData:

A numeric vector, whose length is the number of rows in data, recording the minimum number of misclassification acheived using this data row.

nCol:

The number of columns in data

nRow:

The number of rows in data

classifier:

The classification vector used to create the object.

call:

The function call that created the object

Objects of the fullTNoM class have the following slots:

dex:

Numeric vector of the different possible numbers of misclassifications

fakir:

Numeric vector of expected values based on simulations

obs:

Numeric vector of observed values

scr:

Numeric vector of values based on a permutation test

name:

A character string with a name for the object

Methods

Objects of the TNoM class have the following methods:

summary(object, ...)

Write out a summary of the object, including the number of genes acheiving each possible number of misclassifications.

countSignificant(object, cutoff, ...)

Count the number of significant genes at the given cutoff.

selectSignificant(object, cutoff, ...)

Get a vector for selecting the number of significant genes at the given cutoff.

update(object, nPerm, verbose=FALSE, ...)

Perform simulation and permutation tests on the TNoM object.

Objects of the fullTNoM class have the following methods:

plot(x, ...)

Plot a summary of the TNoM object. This consists of three curves: the observed cumulative number of genes at each misclassification level, along with the corresponding numbers expected based on simulations or permutation tests. The colors of the curves are controlled by the values of COLOR.OBSERVED, COLOR.EXPECTED, and COLOR.PERMTEST

hist(x, ...)

Plot a not terribly useful nor informative histogram of the results.

Author(s)

Kevin R. Coombes <kcoombes@mdanderson.org>

References

Bittner M, Meltzer P, Chen Y, Jiang Y, Seftor E, Hendrix M, Radmacher M, Simon R, Yakhini Z, Ben-Dor A, Sampas N, Dougherty E, Wang E, Marincola F, Gooden C, Lueders J, Glatfelter A, Pollock P, Carpten J, Gillanders E, Leja D, Dietrich K, Beaudry C, Berens M, Alberts D, Sondak V. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature. 2000 Aug 3;406(6795):536-40.

See Also

MultiTtest, MultiWilcoxonTest, link{Bum}

Examples

n.genes <- 200
n.samples <- 10

bogus <- matrix(rnorm(n.samples*n.genes, 0, 3), ncol=n.samples)
splitter <- rep(FALSE, n.samples)
splitter[sample(1:n.samples, trunc(n.samples/2))] <- TRUE

tn <- TNoM(bogus, splitter)
summary(tn)

tnf <- update(tn)
plot(tnf)
hist(tnf)

rm(bogus, splitter, n.genes, n.samples, tn, tnf)