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Meta-Analysis in Gene Expression Studies.

Levi Waldron1, Markus Riester2

  • 1Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY, 10035, USA.

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|March 24, 2016
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Summary
This summary is machine-generated.

This chapter presents methods for combining results from multiple high-throughput genomic experiments. It adapts clinical trial systematic review techniques for genomic data analysis, focusing on gene discovery for ovarian cancer survival.

Keywords:
BiomarkersComputational molecular biologyGene expression profilingMeta-analysisMicroarray analysisOvarian neoplasms

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Area of Science:

  • Genomics
  • Bioinformatics
  • Biostatistics

Background:

  • High-throughput genomic experiments generate vast amounts of data.
  • Synthesizing results across independent studies is crucial for robust findings.
  • Existing methods from clinical trials can be adapted for genomic data meta-analysis.

Purpose of the Study:

  • To introduce methods for synthesizing results from independent high-throughput genomic experiments.
  • To adapt systematic review and meta-analysis techniques for genomic data.
  • To provide a practical case study in gene discovery for ovarian cancer.

Main Methods:

  • Review of methods for identifying, acquiring, and preparing individual patient data.
  • Methodology for synthesizing results across studies.
  • Assessment of statistical heterogeneity in genomic meta-analysis.
  • Step-by-step case study application.

Main Results:

  • The chapter outlines a framework for genomic data meta-analysis.
  • It demonstrates the application of these methods using a case study.
  • The methods facilitate the identification of genes associated with survival in high-grade serous ovarian cancer.

Conclusions:

  • Adapting systematic review methods is effective for synthesizing genomic data.
  • This approach enhances the power of independent high-throughput genomic studies.
  • The presented methodology aids in identifying key genes for disease survival prediction.