computeChiSquaredTest {divergence} | R Documentation |
Given a binary or ternary data matrix with class associations of samples, computes chi-squared tests for each feature between given groups
computeChiSquaredTest(Mat, Groups, classes)
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. |
A data frame with columns 'statistic' and 'pval'.
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"))