Differential Expression Analysis

protocols

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

Before you begin

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file formats

Step 1: PreprocessDataset

Preprocess gene expression data to remove platform noise and genes that have little variation. Although researchers generally preprocess data before differential analysis if doing so removes relevant biological information, skip this step.

Considerations
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PreprocessDataset

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).

Considerations
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ComparativeMarkerSelection

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.

Considerations
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ComparativeMarkerSelectionViewer

Reference

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