runWilcox: Computing Multiple Wilcoxon Tests
In EMA: Easy Microarray Data Analysis

Description Usage Arguments Value Author(s) See Also Examples

This function provides a convenient way to compute the wilcoxon statistics, for each row of a data frame using the multtest package. It returns the raw and adjusted pvalues for each genes as well as the significance of the genes and a quantile-quantile plot.

1	runWilcox(data, labels, typeFDR = "FDR-BH", q = 0.05, plot = TRUE)

`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.
`typeFDR`	The method to apply fo the multiple testing correction.
`q`	A numeric value specifying the pvalue threshold.
`plot`	A logical value specifying if drawing plots or not.

A matrix with the probes ID, the statistics, the raw p-value and the adjust p-value

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

mt.teststat,multiple.correction

## load data
data(marty)

## filtering data
##marty <- expFilter(marty, threshold=3.5, graph=FALSE)

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

## run differential analysis
example.subset<-marty[1:100,]
out <- runWilcox(example.subset, labels=marty.type.num, typeFDR="FDR-BH", plot=FALSE)