MergeMaid: R tools for merging and cross-study validation of gene expression data

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Cross-study validation is essential for robust genomic analysis.
• Integrating diverse gene expression datasets presents significant computational challenges.
• Existing methods often require complex normalization procedures.

Purpose of the Study:

• To develop an R package for merging and visualizing multiple gene expression datasets.
• To facilitate cross-study validation and joint analyses of genomic data.
• To provide tools for exploring gene expression patterns without inter-platform normalization.

Main Methods:

• Development of an R package with specific object definitions for gene expression data.
• Implementation of merging functions that accommodate arbitrary character IDs.
• Creation of visualization tools, including "integrative correlation" plots and gene-specific statistic scatterplots.

Main Results:

• The R package successfully merges multiple gene expression datasets.
• Visualization tools enable effective exploration and cross-study validation.
• The methods allow for the identification of genes with consistent expression changes across studies.
• Gene-specific plots reveal relationships between expression and phenotypes using various regression models.

Conclusions:

• The developed R package provides an efficient framework for integrating and analyzing gene expression data from multiple studies.
• The tools support robust cross-study validation and facilitate the discovery of reliable gene expression patterns.
• This approach enhances the utility of genomic analysis by simplifying data integration and visualization.