Differential Expression Analysis

Before you begin

• Gene expression data must be in a GCT or RES file.
  Example file: all_aml_test.gct.
• The class of each sample must be identified in a CLS file.
  Example file: all_aml_test.cls.

learn more: file formats

Step 1: PreprocessDataset

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

Considerations

• PreprocessDataset can preprocess the data in one or more ways (in this order):
  1. Set threshold and ceiling values. Any value lower/higher than the threshold/ceiling value is reset to the threshold/ceiling value.
  2. Convert each expression value to the log base 2 of the value.
  3. Remove genes (rows) if a given number of its sample values are less than a given threshold.
  4. Remove genes (rows) that do not have a minimum fold change or expression variation.
  5. Discretize or normalize the data.
• ComparativeMarkerSelection expects non-log-transformed data. Some calculations, such as Fold Change, will produce incorrect results on log transformed data.
• If you did not generate the expression data, check whether preprocessing steps have already been taken before running the PreprocessDataset module.

learn more: 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

• If the data set includes at least 10 samples per class, use the default value of 1000 permutations to ensure accurate p-values. If the data set includes fewer than 10 samples in any class, permuting the samples cannot give an accurate p-value; specify 0 permutations to use asymptotic p-values instead.
• If the data set includes more than two classes, use the phenotype test parameter to analyze each class against all others (one-versus-all) or all class pairs (all pairs).

learn more: 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

• Generally, researchers identify marker genes based on FDR rather than the more conservative FWER.
• Often, marker genes are identified based on an FDR cutoff value of .05, which indicates that a gene identified as a marker gene has a 1 in 20 (5%) chance of being a false positive. Select Edit>Filter Features>Custom Filter to filter results based on that criteria (or any other).
• Select File>Save Derived Dataset to create a GCT file that contains a subset of the expression data.

learn more: ComparativeMarkerSelectionViewer