runIndTest: Computing Differential Analysis for each gene
In EMA: Easy Microarray Data Analysis

Description Usage Arguments Details Value Author(s) See Also Examples

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

1 2	runIndTest(data, labels, gene.names = NULL, plot = TRUE, dirname= NULL, grp.name=c("Group1","Group2"))

`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

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.

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

Nicolas Servant, Eleonore Gravier, Pierre Gestraud, Cecile Laurent, Caroline Paccard, Anne Biton, Jonas Mandel, Bernard Asselain, Emmanuel Barillot, Philippe Hupe

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

## 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)