A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments

Hong, F. and Breitling, R. (2008) A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments. Bioinformatics, 24(3), pp. 374-382. (doi: 10.1093/bioinformatics/btm620)

Abstract

Motivation: The proliferation of public data repositories creates a need for meta-analysis methods to efficiently evaluate, integrate and validate related datasets produced by independent groups. A t-based approach has been proposed to integrate effect size from multiple studies by modeling both intra- and between-study variation. Recently, a non-parametric rank product method, which is derived based on biological reasoning of fold-change criteria, has been applied to directly combine multiple datasets into one meta study. Fishers Inverse x² method, which only depends on P-values from individual analyses of each dataset, has been used in a couple of medical studies. While these methods address the question from different angles, it is not clear how they compare with each other. Results: We comparatively evaluate the three methods; t-based hierarchical modeling, rank products and Fishers Inverse x² test with P-values from either the t-based or the rank product method. A simulation study shows that the rank product method, in general, has higher sensitivity and selectivity than the t-based method in both individual and meta-analysis, especially in the setting of small sample size and/or large between-study variation. Not surprisingly, Fishers x² method highly depends on the method used in the individual analysis. Application to real datasets demonstrates that meta-analysis achieves more reliable identification than an individual analysis, and rank products are more robust in gene ranking, which leads to a much higher reproducibility among independent studies. Though t-based meta-analysis greatly improves over the individual analysis, it suffers from a potentially large amount of false positives when P-values serve as threshold. We conclude that careful meta-analysis is a powerful tool for integrating multiple array studies.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Breitling, Professor Rainer
• Authors: Hong, F., and Breitling, R.
• Subjects: Q Science > QH Natural history > QH426 Genetics
• College/School: College of Medical Veterinary and Life Sciences > School of Molecular Biosciences
• Journal Name: Bioinformatics
• ISSN: 1367-4803
• ISSN (Online): 1460-2059
• Published Online: 18 January 2008