comp.unadjp {DEDS}R Documentation

Computing permutation based unadjusted p values for each row of a matrix

Description

This function computes permuation based unadjusted p values for a selected test statistic, e.g., one- or two-sample t-statitics, 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

X A matrix, with m rows corresponding to variables (hypotheses) andn 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.
L A vector of integers corresponding to observation (column) class labels. For k classes, the labels must be integers between 0 and k-1.
B 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.

Details

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

Value

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

Author(s)

Yuanyuan Xiao, yxiao@itsa.ucsf.edu,
Jean Yee Hwa Yang, jean@biostat.ucsf.edu.

See Also

deds.genExtra, comp.stat

Examples


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")

[Package DEDS version 1.0.3 Index]