A Learned Comparative Expression Measure for Affymetrix GeneChip DNA Microarrays

Perhaps the most common question that a microarray study can ask is, “Between two given biological conditions, which genes exhibit changed expression levels?” Existing methods for answering this question either generate a comparative measure based upon a static model, or take an indirect approach, first estimating absolute expression levels and then comparing the estimated levels to one another. We present a method for detecting changes in gene expression between two samples based on data from Affymetrix GeneChips. Using a library of over 200,000 known cases of differential expression, we create a learned comparative expression measure (LCEM) based on classification of probe-level data patterns as changed or unchanged. LCEM uses perfect match probe data only; mismatch probe values did not prove to be useful in this context. LCEM is particularly powerful in the case of small microarry studies, in which a regression-based method such as RMA cannot generalize, and in detecting smal...

• Type: Conference
• Year: 2005
• Where: CSB
• Authors: Will Sheffler, Eli Upfal, John Sedivy, William Stafford Noble
• Keywords: Absolute Expression Levels, Bioinformatics, CSB 2005, Expression Levels, Gene Expression