comp.unadjp: Computing permutation based unadjusted p values for each row of a matrix

Description

This function computes permutation based unadjusted p values for a selected test statistic, e.g., one- or two-sample t-statistics, F-statistics, SAM, Fold change, for each row of a matrix.

Usage

comp.unadjp(X, L, B = 1000, test = c("t", "fc", "sam", "f"), tail = c("abs", "lower", "higher"), extra = NULL)

Arguments

A matrix, with $m$ rows corresponding to variables (hypotheses) and$n$ columns corresponding to observations. In the case of gene expression data, rows correspond to genes and columns to mRNA samples. The data can be read using read.table.

A vector of integers corresponding to observation (column) class labels. For $k$ classes, the labels must be integers between 0 and $k-1$.

The number of permutations. For a complete enumeration, B should be 0 (zero) or any number not less than the total number of permutations.

test

A character string specifying the statistic to be used to test the null hypothesis of no association between the variables and the class labels. If test="t", for one-class, the tests are based on one-sample t-statistics; for two-class, the tests are based on two-sample t-statistics (unequal variances). If test="f", the tests are based on F-statistics. If test="fc", the tests are based on fold changes among classes. If test="sam", the tests are based on SAM-statistics.

tail

A character string specifying the type of rejection region. If side="abs", two-tailed tests, the null hypothesis is rejected for large absolute values of the test statistic. If side="higher", one-tailed tests, the null hypothesis is rejected for large values of the test statistic. If side="lower", one-tailed tests, the null hypothesis is rejected for small values of the test statistic.

extra

Extra parameter need for the test specified; see deds.genExtra.

Value

A vector of unadjusted $p$ values for each row of the matrix.

Details

The function comp.unadjp computes unadjusted $p$ values using a permutation scheme.

Examples

Run this code


X <- matrix(rnorm(1000,0,0.5), nc=10)
L <- rep(0:1,c(5,5))

# genes 1-10 are differentially expressed
X[1:10,6:10]<-X[1:10,6:10]+1

# t statistics
unadjp.t <- comp.unadjp(X, L, test="t")