Table of Contents
ISRN Bioinformatics
Volume 2014, Article ID 345106, 7 pages
http://dx.doi.org/10.1155/2014/345106
Research Article

Comparison of Merging and Meta-Analysis as Alternative Approaches for Integrative Gene Expression Analysis

Computational Modeling Lab, Vrije Universiteit Brussel, 1050 Brussels, Belgium

Received 26 August 2013; Accepted 24 September 2013; Published 12 January 2014

Academic Editors: J. P. de Magalhaes and S. Liuni

Copyright © 2014 Jonatan Taminau et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

An increasing amount of microarray gene expression data sets is available through public repositories. Their huge potential in making new findings is yet to be unlocked by making them available for large-scale analysis. In order to do so it is essential that independent studies designed for similar biological problems can be integrated, so that new insights can be obtained. These insights would remain undiscovered when analyzing the individual data sets because it is well known that the small number of biological samples used per experiment is a bottleneck in genomic analysis. By increasing the number of samples the statistical power is increased and more general and reliable conclusions can be drawn. In this work, two different approaches for conducting large-scale analysis of microarray gene expression data—meta-analysis and data merging—are compared in the context of the identification of cancer-related biomarkers, by analyzing six independent lung cancer studies. Within this study, we investigate the hypothesis that analyzing large cohorts of samples resulting in merging independent data sets designed to study the same biological problem results in lower false discovery rates than analyzing the same data sets within a more conservative meta-analysis approach.