title: Meta-analysis method for discovering reliable biomarkers by integrating statistical and biological approaches: An application to liver toxicity.
abstract: 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.
authors.affiliation: Department of Bio and Brain Engineering, KAIST, Yuseong-gu, Daejeon, 34141, Republic of Korea.
authors.forename: Hyeyoung
authors.initials: H
authors.lastname: Cho
authors.affiliation: Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea.
authors.forename: Hyosil
authors.initials: H
authors.lastname: Kim
authors.affiliation: School of Integrative Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Republic of Korea.
authors.forename: Dokyun
authors.initials: D
authors.lastname: Na
authors.affiliation: Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, 16499, Republic of Korea.
authors.forename: So Youn
authors.initials: SY
authors.lastname: Kim
authors.affiliation: Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, 16499, Republic of Korea.
authors.forename: Deokyeon
authors.initials: D
authors.lastname: Jo
authors.affiliation: Department of Bio and Brain Engineering, KAIST, Yuseong-gu, Daejeon, 34141, Republic of Korea. Electronic address: dhlee@kaist.ac.kr.
authors.forename: Doheon
authors.initials: D
authors.lastname: Lee
mesh_terms: D015415:Biomarkers / Q000378:metabolism*; D056486:Chemical and Drug Induced Liver Injury / Q000175:diagnosis* / Q000378:metabolism*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D047908:Intracellular Signaling Peptides and Proteins / Q000378:metabolism; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D027425:ATP-Binding Cassette, Sub-Family C Proteins / Q000378:metabolism; D009687:Nuclear Proteins / Q000378:metabolism; D054645:Protein Phosphatase 1 / Q000378:metabolism; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D018511:Systems Integration
chemical_list: D015415:Biomarkers; D047908:Intracellular Signaling Peptides and Proteins; D027425:ATP-Binding Cassette, Sub-Family C Proteins; D009687:Nuclear Proteins; D054645:Protein Phosphatase 1; C413695:ZWINT protein, human; C114462:multidrug resistance-associated protein 3; C586823:PPP1R3B protein, human
keywords: Biomarker discovery; Drug liver toxicity; Effect size; Meta-analysis