Meta-analysis method for discovering reliable biomarkers by integrating statistical and biological approaches: An application to liver toxicity.

Biomarkers that are identified from a single study often appear to be biologically irrelevant or false positives. Meta-analysis techniques allow integrating data from multiple studies that are related but independent in order to identify biomarkers across multiple conditions. However, existing biomarker meta-analysis methods tend to be sensitive to the dataset being analyzed. Here, we propose a meta-analysis method, iMeta, which integrates t-statistic and fold change ratio for improved robustness. For evaluation of predictive performance of the biomarkers identified by iMeta, we compare our method with other meta-analysis methods. As a result, iMeta outperforms the other methods in terms of sensitivity and specificity, and especially shows robustness to study variance increase; it consistently shows higher classification accuracy on diverse datasets, while the performance of the others is highly affected by the dataset being analyzed. Application of iMeta to 59 drug-induced liver injury studies identified three key biomarker genes: Zwint, Abcc3, and Ppp1r3b. Experimental evaluation using RT-PCR and qRT-PCR shows that their expressional changes in response to drug toxicity are concordant with the result of our method. iMeta is available at http://imeta.kaist.ac.kr/index.html.