# runTtest: Computing Multiple Student Tests In EMA: Easy Microarray Data Analysis

## Description

This function provides a convenient way to compute test statistics, e.g., two-sample Welch t-statistics, t-statistics, paired t-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.

## Usage

 `1` ```runTtest(data,labels,typeFDR="FDR-BH",algo="t", q=0.05, plot=TRUE) ```

## 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. `typeFDR` The method to apply fo the multiple testing correction. `algo` 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"', the tests are based on two-sample Welch t-statistics (unequal variances). The number of ddl is computed using the Satterthwaite approximation. If 'test="t.equalvar"', the tests are based on two-sample t-statistics with equal variance for the two samples. The square of the t-statistic is equal to an F-statistic for k=2. If 'test="pairt"', the tests are based on paired t-statistics. The square of the paired t-statistic is equal to a block F-statistic for k=2. `q` A numeric value specifying the pvalue threshold. `plot` A logical value specifying if drawing plots or not.

## Value

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

## Author(s)

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

`mt.teststat`,`multiple.correction`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```## load data data(marty) ## Not run: ## filtering data marty <- expFilter(marty, threshold=3.5, graph=FALSE) ## End(Not run) ##Class label 0/1 marty.type.num <- ifelse(marty.type.cl=="Her2+",0,1) ## run differential analysis on example set example.subset <- marty[1:100,] out <- runTtest(example.subset, labels=marty.type.num, typeFDR="FDR-BH", plot=FALSE) ```