Comprehensive literature review and statistical considerations for microarray meta-analysis

By George C. Tseng, Debashis Ghosh, Eleanor Feingold. Nucleic Acids Research, 2012

356 citations recorded by the source corpus.

With the rapid advances of various high-throughput technologies, generation of ‘-omics’ data is commonplace in almost every biomedical field. Effective data management and analytical approaches are essential to fully decipher the biological knowledge contained in the tremendous amount of experimental data. Meta-analysis, a set of statistical tools for combining multiple studies of a related hypothesis, has become popular in genomic research. Here, we perform a systematic search from PubMed and manual collection to obtain 620 genomic meta-analysis papers, of which 333 microarray meta-analysis papers are summarized as the basis of this paper and the other 249 GWAS meta-analysis papers are discussed in the next companion paper. The review in the present paper focuses on various biological purposes of microarray meta-analysis, databases and software and related statistical procedures. Statistical considerations of such an analysis are further scrutinized and illustrated by a case study. Finally, several open questions are listed and discussed.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d561712462d746a929adbbdf057c93df1e44d7a0

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Citations in the corpus

Integrating single-cell transcriptomic data across different conditions, technologies, and species (2018, methodology): “68).”
MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis (2018, methodology): “This practice is often referred to as biomarker ‘meta-analysis’ (35).”
NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data (2015, methodology): “Cochran’s Q test is often used to evaluate the homogeneity of the data sets.”
Reuse of public genome-wide gene expression data (2012, methodology): “Such meta-analysis studies and their methods have been comprehensively reviewed elsewhere 67,68,69 , and a summary of the steps for such analysis is presented in BOX 4 .”
NetworkAnalyst - integrative approaches for protein–protein interaction network analysis and visual exploration (2014, background): “In addition, integrating multiple gene expression datasets (meta-analysis) have become increasingly applied for improved biomarker identification and biological understanding (40,41).”