Principal component analysis-based filtering improves detection for Affymetrix gene expression arrays.

ABSTRACT: Gene expression array technology has reached the stage of being routinely used to study clinical samples in search of diagnostic and prognostic biomarkers. Due to the nature of array experiments, which examine the expression of tens of thousands of genes simultaneously, the number of null hypotheses is large. Hence, multiple testing correction is often necessary to control the number of false positives. However, multiple testing correction can lead to low statistical power in detecting genes that are truly differentially expressed. Filtering out non-informative genes allows for reduction in the number of null hypotheses. While several filtering methods have been suggested, the appropriate way to perform filtering is still debatable. We propose a new filtering strategy for Affymetrix GeneCh

SUBMITTER: Lu J 

PROVIDER: S-EPMC3141272 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature