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

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.

Gene-set activity toolbox (GAT): A platform for microarray-based cancer diagnosis using an integrative gene-set analysis approach

“…To overcome this issue, these genes are further considered in their functional role on the basis of interaction to obtain the genes aggregating the signal for disease state to be considered as disease biomarker. PinnacleZ proposed by Taminau et al 42 is a method for network-based biomarker identi¯cation using gene expression and PPI network. In their work, PinnacleZ has been used for breast cancer metastasis classi¯cation.…”

Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery

“…Direct integration of data sets performed on different microarray platforms may introduce undesirable batch effects due to systematic multiplicative biases [23,32,56]. The level of difficulty present to combine multiple datasets has been termed “dataset complexity” [53].…”

“…In a comparative study, Taminau et al [23] found significantly more differentially-expressed genes using cross-platform normalization than meta-analysis. An additional advantage of cross-platform normalization is that it allows prediction models applied to a subset of studies to be applied across additional studies from other platforms [27].…”

Integrative Analysis of Axolotl Gene Expression Data From Regenerative and Wound Healing Limb Tissues

“…Despite such advancements and success in utilizing microarrays and RNA-Seq technologies in the quest for unraveling the mechanisms of axolotl limb regeneration, a strong and popular methodology that has the potential for enhancing our current knowledge on limb regeneration is missing in the axolotl literature. Integrative data analysis (IDA) is a key methodology that is applied across many scientific disciplines and aims to derive scientific consensus on a particular research question [38][39][40]. Although the concept of IDA has recently been expanded to refer to experiments aiming to integrate information from several layers of "omics" information (aka multi-omics) [41], the utilized IDA in this study refers to the process of combining information from different platforms across independent studies [40].…”

“…Lastly but not least, an overlap of experimental design of individual studies may in some cases generate similar data, due to lack of an established IDA pipeline. Therefore, when IDA is applied, sample size increases, individual study-specific biases are minimized, and more statistical power is achieved [39,40,[45][46][47]. Metaanalysis and cross-platform normalization (aka "merging") are two fundamental approaches to perform IDA [40].…”