SMAGEXP

SMAGEXP performs meta-analysis of gene expression data by integrating the R packages metaMA and metaRNASeq within a Galaxy framework to analyze microarray data (including Gene Expression Omnibus and Affymetrix arrays) and next-generation sequencing (NGS) raw read counts.

Key Features:

• Integration of metaMA and metaRNASeq: Integrates the R packages metaMA and metaRNASeq within Galaxy to combine analyses across microarray and NGS datasets.
• Microarray-specific analysis: Supports microarray datasets from the Gene Expression Omnibus (GEO) and custom Affymetrix arrays using metaMA.
• NGS-specific analysis: Processes NGS raw read counts using DESeq2 and performs meta-analysis with metaRNASeq.
• Technology-specific statistical modeling: Applies distinct statistical approaches for microarray and NGS data to address their different modeling requirements.
• Statistical power enhancement: Aggregates results across multiple studies or datasets to increase detection power for differential expression.
• Quality assessment metrics: Reports key values independent of technology type for assessing meta-analysis quality.

Scientific Applications:

• Transcriptomics meta-analysis: Combine microarray and NGS studies to identify consistently differentially expressed genes across experiments.
• Cross-platform integration: Integrate GEO, Affymetrix, and NGS raw count datasets to compare gene expression patterns across platforms and populations.
• Disease and biological process investigation: Support studies aiming to elucidate molecular mechanisms and disease-associated expression signatures via aggregated evidence.

Methodology:

Integration of the R packages metaMA and metaRNASeq within Galaxy; microarray analysis via metaMA on GEO and Affymetrix data; NGS raw read counts processed with DESeq2 followed by meta-analysis with metaRNASeq; aggregation of results across studies and reporting of technology-independent quality metrics.