computeChiSquaredTest {divergence} | R Documentation

## Compute chi-squared test

### Description

Given a binary or ternary data matrix with class associations of samples, computes chi-squared tests for each feature between given groups

### Usage

    computeChiSquaredTest(Mat, Groups, classes)

### Arguments

`Mat` | Matrix of digitized binary or ternary data with each column corresponding to a sample and each row corresponding to a feature
`Groups` | Factor indicating class association of samples
`classes` | Vector of class labels; the test will be applied between the classes given.

### Value

A data frame with columns 'statistic' and 'pval'.

### Examples

    baseMat = breastTCGA_Mat[, breastTCGA_Group == "NORMAL"]
    dataMat = breastTCGA_Mat[, breastTCGA_Group != "NORMAL"]
    seMat.base = SummarizedExperiment(assays=list(data=baseMat))
    seMat = SummarizedExperiment(assays=list(data=dataMat))
    div = computeUnivariateDigitization(
      seMat = seMat,
      seMat.base = seMat.base,
     parallel = TRUE
    )
    assays(seMat)$div = div$Mat.div
    sel = which(colnames(seMat) %in% colnames(dataMat))
    div.chi = computeChiSquaredTest(Mat=assays(seMat)$div,
                                    Groups=breastTCGA_ER[sel],
                                    classes=c("Positive", "Negative"))

[Package divergence version 1.6.0 Index]