A survey of gene expression meta-analysis: methods and applications
Daniel Toro-Domínguez1, Juan Antonio Villatoro-García1, Jordi Martorell-Marugán1
1GENYO (Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 114, 18016 Granada, Spain.
Briefings in Bioinformatics
|February 26, 2020
Summary
Gene expression meta-analysis integrates public data for new insights. This guide covers methods, tools, and applications for robust gene expression data analysis.
Area of Science:
- Bioinformatics
- Genomics
- Biostatistics
Background:
- High-throughput gene expression technologies have generated vast public data.
- Publicly available gene expression datasets offer significant potential for knowledge discovery.
Purpose of the Study:
- To describe gene expression meta-analysis steps, from data preparation to advanced statistical methods.
- To analyze applications and challenges in gene expression meta-analysis.
- To provide a comparative overview of bioinformatics tools and a practical guide for method selection.
Main Methods:
- Data preparation for meta-analysis.
- Application of state-of-the-art statistical methods for integrating gene expression datasets.
- Comparative analysis of software and bioinformatics tools.
Main Results:
- A comprehensive overview of gene expression meta-analysis.
- Analysis of diverse applications and challenges.
- A comparative assessment of available bioinformatics tools.
Conclusions:
- Gene expression meta-analysis is a powerful approach for leveraging public data.
- The study provides a practical guide for researchers to select appropriate methods and tools.
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