Extracting expression modules from perturbational gene expression compendia
2008
ENIGMA: A New Method for Analyzing Gene Expression Data
Sample size: 2871
publication
Evidence: high
Author Information
Author(s): Steven Maere, Patrick Van Dijck, Martin Kuiper
Primary Institution: Department of Plant Systems Biology, VIB, Ghent University
Hypothesis
Can ENIGMA effectively extract expression modules from perturbational gene expression data?
Conclusion
ENIGMA is a valuable method for analyzing perturbational gene expression data, outperforming existing methods.
Supporting Evidence
- ENIGMA identified a network of 100,762 significant positive coexpression links and 30,390 negative links.
- 206 modules were discovered in the Rosetta dataset encompassing 2201 genes.
- 60% of the modules showed enrichment of GO categories and/or TF binding sites.
Takeaway
ENIGMA helps scientists understand how genes work together by looking at their expression patterns when they are disturbed.
Methodology
ENIGMA uses differential expression analysis to extract co-differential expression networks and modules from gene expression data.
Limitations
The method may not capture all biological processes due to varying expression amplitudes.
Statistical Information
P-Value
0.01
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website