SMAGEXP: a galaxy tool suite for transcriptomics data meta-analysis

TL;DR

SMAGEXP is a Galaxy-based tool suite that unifies meta-analysis of gene expression data from microarray and NGS experiments, enhancing accessibility and comparability across technologies.

Contribution

It integrates metaMA and metaRNASeq into Galaxy, providing a unified platform for meta-analysis of gene expression data across different technologies.

Findings

Supports microarray data from GEO and custom datasets

Allows meta-analysis of NGS data using DESeq2 and metaRNASeq

Provides quality metrics for meta-analysis results

Abstract

Bakground: With the proliferation of available microarray and high throughput sequencing experiments in the public domain, the use of meta-analysis methods increases. In these experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably enhance the statistical power and give more accurate results. For those purposes, it combines either effect sizes or results of single studies in a appropriate manner. R packages metaMA and metaRNASeq perform meta-analysis on microarray and NGS data, respectively. They are not interchangeable as they rely on statistical modeling specific to each technology. Results: SMAGEXP (Statistical Meta-Analysis for Gene EXPression) integrates metaMA and metaRNAseq packages into Galaxy. We aim to propose a unified way to carry out meta-analysis of gene expression data, while taking care of their specificities. We have developed this tool suite to analyse microarray data from Gene Expression Omnibus (GEO) database or custom data from affymetrix microarrays. These data are then combined to carry out meta-analysis using metaMA package. SMAGEXP also offers to combine raw read counts from Next Generation Sequencing (NGS) experiments using DESeq2 and metaRNASeq package. In both cases, key values, independent from the technology type, are reported to judge the quality of the meta-analysis. These tools are available on the Galaxy main tool shed. Source code, help and installation instructions are available on github. Conclusion: The use of Galaxy offers an easy-to-use gene expression meta-analysis tool suite based on the metaMA and metaRNASeq packages.