EMA (version 1.4.7)

runIndTest: Computing Differential Analysis for each gene

Description

This function computes test statistics, e.g., two-sample Welch t-statistics, t-statistics, or wilcoxon, independently for each row of a data frame.

Usage

runIndTest(data, labels, gene.names = NULL, plot = TRUE, dirname= NULL,
grp.name=c("Group1","Group2"))

Arguments

data

a matrix, a data frame, or an ExpressionSet object. Each row of 'data' (or 'exprs(data)', respectively) must correspond to a gene, and each column to a sample.

labels

A vector of integers corresponding to observation (column) class labels. For 2 classes, the labels must be 0 and 1.

gene.names

A vector of description or name for each gene.

plot

A logical value specifying if drawing plots or not.

dirname

If specified, the .png plots are created in the directory.

grp.name

Vector with the name of the two groups

Value

A matrix with the gene names, the statistics, and the p-values.

Details

For each gene independently, the function tests for the normality (Shapiro test) and the variance equality (F test) of each groups. According to the results, a welch test, a student test or a wilcoxon test is performed.

See Also

shapiro.test, var.test,t.test,wilcox.test

Examples

# NOT RUN {
## load data
data(marty)

##random choice of genes - in practice genes of interest
geneOfInterest<-sample(1:ncol(marty),5)

##Class label 0/1
marty.type.num <- ifelse(marty.type.cl=="Her2+",0,1)

## run differential analysis
out <- runIndTest(marty[geneOfInterest,], labels=marty.type.num)
# }