metaDEA

metaDEA integrates differential expression results across transcriptomic datasets to identify genes and gene clusters with consistent deregulation patterns for the analysis of genomic disorders such as trisomy 21 (Down syndrome).

Key Features:

• Integration of Transcriptomic Data: Integrates differential expression (DE) results from public transcriptomic datasets to compare trisomic and euploid transcriptomes across human and mouse.
• Identification of Consistent Deregulation Patterns: Identifies clusters of consistently deregulated genes ranging from 2 to 13 members, often grouping genes within the same Gene Ontology (GO) categories or associated disease classes.
• Focus on Molecular Cascades and Interactions: Highlights molecular cascades in which chromosome-specific genes and their interactors display consistent deregulation, emphasizing non-arbitrary effects of trisomy 21 on gene networks.
• Enrichment Analysis: Detects enrichment of the most consistent expression changes in categories related to interferon response and neutrophil activation.
• Tissue-Specific Impact Assessment: Assesses how trisomy impacts different tissues by comparing divergent gene set deregulation patterns despite shared triplicated genes.
• Discovery of Novel Genes and Therapeutic Targets: Confirms known genes such as SOD1 and identifies novel candidate genes and potential therapeutic targets relevant to Down syndrome and cancer.
• Ensembl Gene ID Queries: Supports queries by Ensembl gene IDs to retrieve consolidated differential expression information across datasets.

Scientific Applications:

• Down syndrome (trisomy 21): Analysis of consistent transcriptomic deregulation and inflammatory signatures to investigate molecular mechanisms of Down syndrome.
• Cancer: Examination of recurrent gene expression patterns to inform hypotheses about tumor biology and potential therapeutic strategies.
• Other genomic disorders: Application to complex conditions where integrated transcriptomic deregulation analysis can reveal disease-relevant genes and pathways.

Methodology:

Performs a meta-analysis that synthesizes differential expression data from multiple studies, implemented as an R package and enabling queries by Ensembl gene IDs to retrieve DE information across datasets.