Statistical methods for meta-analysis of microarray data: A comparative study

Systematic integration of microarrays from different sources increases statistical power of detecting differentially expressed genes and allows assessment of heterogeneity. The challenge, however, is in designing and implementing efficient analytic methodologies for combining data generated by different research groups and platforms. The widely used strategy mainly focuses on integrating preprocessed data without having access to the original raw data that yielded the initial results. A main disadvantage of this strategy is that the quality of different data sets may be highly variable, but this information is neglected during the integration. We have recently proposed a quality-weighting strategy to integrate Affymetrix microarrays. The quality measure is a function of the detection p-values, which indicate whether a transcript is reliably detected or not on Affymetrix gene chip. In this study, we compare the proposed quality-weighted strategy with the traditional quality-unweighted strategy, and examine how the quality weights influence two commonly used meta-analysis methods: combining p-values and combining effect size estimates. The methods are compared on a real data set for identifying biomarkers for lung cancer. Our results show that the proposed quality-weighted strategy can lead to larger statistical power for identifying differentially expressed genes when integrating data from Affymetrix microarrays.