Appearance frequency modulated gene set enrichment testing

Background: Gene set enrichment testing has helped bridge the gap from an individual gene to a systems biology interpretation of microarray data. Although gene sets are defined a priori based on biological knowledge, current methods for gene set enrichment testing treat all genes equal. It is well-known that some genes, such as those responsible for housekeeping functions, appear in many pathways, whereas other genes are more specialized and play a unique role in a single pathway. Drawing inspiration from the field of information retrieval, we have developed and present here an approach to incorporate gene appearance frequency (in KEGG pathways) into two current methods, Gene Set Enrichment Analysis (GSEA) and logistic regression-based LRpath framework, to generate more reproducible and biologically meaningful results. Results: Two breast cancer microarray datasets were analyzed to identify gene sets differentially expressed between histological grade 1 and 3 breast cancer. The correl...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Jun Ma, Maureen A. Sartor, H. V. Jagadish
• Keywords: Appearance Frequency, Artificial Intelligence, BMCBI 2011, Gene, Gene Sets