%0 Journal Article
%T Comparison of Merging and Meta-Analysis as Alternative Approaches for Integrative Gene Expression Analysis
%A Jonatan Taminau
%A Cosmin Lazar
%A Stijn Meganck
%A Ann Now¨¦
%J ISRN Bioinformatics
%D 2014
%R 10.1155/2014/345106
%X 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¡ªmeta-analysis and data merging¡ªare 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. 1. Introduction Nowadays, an increasing amount of gene expression data sets is available through public repositories (e.g., NCBI GEO [1], ArrayExpress [2]), which might contain the necessary clues for the discovery of new findings, leading to the development of new treatments or therapies. It is one of the most recent challenges to unlock the hidden potential of these data, by using it in large-scale analysis pipe-lines. Integrating this vast amount of data originating from different but independent studies could be beneficial for the discovery of new biological insights by increasing the statistical power of gene expression analysis [3, 4]. With integrative analysis we mean combining the information of multiple and independent studies, designed to study the same biological problem, in order to extract more general and more reliable conclusions. To this purpose, two approaches exist: meta-analysis and analysis by data merging. In the meta-analysis approach the results of individual studies (e.g., values, ranks, classification accuracies, etc.) are combined at the interpretative level. In contrast, the merging approach integrates microarray data at the expression value level after transforming the expression values to numerically comparable measures. Both approaches are illustrated in Figure 1.
%U http://www.hindawi.com/journals/isrn.bioinformatics/2014/345106/