Abstract
BackgroundWith 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 an appropriate manner. R packages metaMA and metaRNASeq perform meta-analysis on microarray and next generation sequencing (NGS) data, respectively. They are not interchangeable as they rely on statistical modeling specific to each technology.ResultsSMAGEXP (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 analyze microarray data from the Gene Expression Omnibus 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 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. A dockerized instance of galaxy containing SMAGEXP and its dependencies is available on Docker hub. Source code, help, and installation instructions are available on GitHub.ConclusionThe use of Galaxy offers an easy-to-use gene expression meta-analysis tool suite based on the metaMA and metaRNASeq packages.
Highlights
With the proliferation of available microarray and high throughput sequencing experiments in the public domain, the use of meta-analysis methods increases
We developed SMAGEXP, a toolsuite dedicated to gene-expression data meta-analysis
Integrated into Galaxy, SMAGEXP is easy to use for biologists and life scientists
Summary
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. We have developed this tool suite to analyse microarray data from Gene Expression Omnibus (GEO) database or custom data from affymetrix c microarrays These data are combined to carry out meta-analysis using metaMA package. SMAGEXP offers to combine raw read counts from Generation Sequencing (NGS) experiments using DESeq and metaRNASeq package In both cases, key values, independent from the technology type, are reported to judge the quality of the meta-analysis. These integrated tools can be shared via the Galaxy toolshed which serves as an appstore
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