An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and...

Area of Science:

• Bioinformatics
• Genomic Data Analysis
• Biomedical Research

Background:

• High-throughput genomic technologies generate vast data, necessitating robust methods for integrating multiple studies.
• Microarray meta-analysis is crucial but lacks systematic, user-friendly software pipelines.
• Integrating diverse genomic datasets presents significant computational and analytical challenges.

Purpose of the Study:

• To introduce MetaOmics, a comprehensive suite of R packages designed for microarray meta-analysis.
• To provide user-friendly tools for quality control, differential gene expression analysis, and pathway enrichment.
• To facilitate the comparison and implementation of various meta-analysis strategies.

Main Methods:

• Developed three R packages: MetaQC for quality control, MetaDE for differential expression, and MetaPath for pathway analysis.
• Incorporated flexible input options for experimental data, clinical outcomes, and pathway databases.
• Utilized multi-core parallel computing for efficient processing and handled missing data.

Main Results:

• MetaQC offers objective criteria for study selection in meta-analysis.
• MetaDE and MetaPath enable robust candidate marker and pathway detection with customizable methods.
• The suite supports various data types and generates informative visualizations.

Conclusions:

• MetaOmics provides a powerful and accessible platform for conducting and comparing microarray meta-analysis pipelines.
• The software enhances the integration and interpretation of large-scale genomic data in biomedical research.
• It addresses the need for systematic and user-friendly tools in the rapidly evolving field of genomics.