Tests for finding complex patterns of differential expression in cancers: towards individualized medicine

Background: Microarray studies in cancer compare expression levels between two or more sample groups on thousands of genes. Data analysis follows a population-level approach (e.g., comparison of sample means) to identify differentially expressed genes. This leads to the discovery of 'population-level' markers, i.e., genes with the expression patterns A > B and B > A. We introduce the PPST test that identifies genes where a significantly large subset of cases exhibit expression values beyond upper and lower thresholds observed in the control samples. Results: Interestingly, the test identifies A > B and B < A pattern genes that are missed by population-level approaches, such as the t-test, and many genes that exhibit both significant overexpression and significant underexpression in statistically significantly large subsets of cancer patients (ABA pattern genes). These patterns tend to show distributions that are unique to individual genes, and are aptly visualize...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: James Lyons-Weiler, Satish Patel, Michael J. Becich, Tony E. Godfrey
• Keywords: BMCBI 2004, Expressed Genes, Large Subsets, Microarray Studies