title: Meta-Analysis in Gene Expression Studies.
abstract: This chapter introduces methods to synthesize experimental results from independent high-throughput genomic experiments, with a focus on adaptation of traditional methods from systematic review of clinical trials and epidemiological studies. First, it reviews methods for identifying, acquiring, and preparing individual patient data for meta-analysis. It then reviews methodology for synthesizing results across studies and assessing heterogeneity, first through outlining of methods and then through a step-by-step case study in identifying genes associated with survival in high-grade serous ovarian cancer.
authors.affiliation: Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY, 10035, USA.
authors.forename: Levi
authors.initials: L
authors.lastname: Waldron
authors.affiliation: Novartis Institutes for BioMedical Research (NIBR), 250 Massachusetts Avenue, Cambridge, MA, 02139, USA. markus.riester@novartis.com.
authors.forename: Markus
authors.initials: M
authors.lastname: Riester
mesh_terms: D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D066264:Datasets as Topic; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D015201:Meta-Analysis as Topic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods
keywords: Biomarkers; Computational molecular biology; Gene expression profiling; Meta-analysis; Microarray analysis; Ovarian neoplasms