Differential Expression Analysis
Find genes that are significantly differentially expressed between classes of samples.
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.
- PreprocessDataset can preprocess the data in one or more ways (in this order):
- Set threshold and ceiling values. Any value lower/higher than the threshold/ceiling
value is reset to the threshold/ceiling value.
- Convert each expression value to the log base 2 of the value.
- Remove genes (rows) if a given number of its sample values are less than
a given threshold.
- Remove genes (rows) that do not have a minimum fold change or expression
- 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.
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).
- 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
- 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
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.
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.
Gould, J., Getz, G., Monti, S., Reich, M., and Mesirov, J.P. 2006.
Comparative gene marker selection suite. Bioinformatics 22(15):1924-1925.