Differential Expression Analysis


Find genes that are significantly differentially expressed between classes of samples.

Before you begin

learn more:
file formats

Step 1: PreprocessDataset

Preprocess gene expression data to remove platform noise and genes that have little variation.

learn more:

Step 2: ComparativeMarkerSelection

ComparativeMarkerSelection computes differential gene expression. For each gene, it uses a test statistic to calculate the difference in gene expression between classes and then computes a p-value to estimate the significance of the test statistic score.

Because testing tens of thousands of genes simultaneously increases the possibility of mistakenly identifying a non-marker gene as a marker gene (a false positive), ComparativeMarkerSelection corrects for multiple hypothesis testing by computing both false discovery rates (FDR) and family-wise error rates (FWER).

learn more:

Step 3: ComparativeMarkerSelectionViewer

Run the ComparativeMarkerSelectionViewer module to view the results. The viewer displays the test statistic score, its p value, two FDR statistics and three FWER statistics for each gene.

learn more:


Gould, J., Getz, G., Monti, S., Reich, M., and Mesirov, J.P. 2006. Comparative gene marker selection suite. Bioinformatics 22(15):1924-1925.