Integrating Affymetrix microarray data sets using probe-level test statistic for predicting prostate cancer

TL;DR: These analyses show that the prognostic gene expression signatures identified through the probe-level test statistics are more strongly differentially expressed and have better prediction accuracy than signatures derived from a probeset-level model.

Abstract: Microarray technology has previously been used to identify differentially expressed genes between tumor and normal prostate samples in a single study as well as in a synthesis involving multiple studies. When integrating results from several Affymetrix microarray datasets, previous studies have used probeset-level data which may lead to a loss of information contained at the probe-level. Here, we propose a new approach for combining results across studies, based on a probe-level test statistic. Each probe-level test statistic is transformed into an effect size measure for each probeset and a random-effects model (REM) is used to integrate effect sizes across studies. We compared statistical and biological significance of the prognostic gene expression signatures identified in the probe-level model (PLM) with those in the probeset-level model (PSLM). Support vector machines (SVMs)-based predictive models were built using these two sets of signatures and their performances were evaluated using independent test datasets. Our analyses show that the prognostic gene expression signatures identified through the probe-level test statistics are more strongly differentially expressed and have better prediction accuracy than signatures derived from a probeset-level model.