RegEnrich

RegEnrich identifies key transcriptional regulators and infers gene/protein regulatory interactions from gene expression data to elucidate regulatory mechanisms.

Key Features:

• Differential Expression Analysis: Performs differential expression analysis to detect genes with significant expression changes across conditions.
• Data-Driven Gene Regulatory Network Inference: Infers gene/protein regulatory interactions to construct gene regulatory networks.
• Enrichment Analysis and Regulator Ranking (GSEA): Uses Gene Set Enrichment Analysis (GSEA) to rank potential regulators based on their influence within inferred networks.
• Benchmarking with Gene Silencing Datasets: Benchmarks performance across multiple gene expression datasets derived from gene silencing studies.
• Application to Interferon-Stimulation Datasets: Demonstrated on 21 publicly available interferon-stimulation datasets across various cell types.

Scientific Applications:

• Cell Differentiation: Identifies regulators underlying cell differentiation by linking expression changes to regulatory network influence.
• Cellular Responses to Drug Stimulation: Pinpoints regulators involved in cellular responses to drug stimulation by integrating differential expression and network inference.
• Disease Progression: Reveals key regulators associated with disease progression through network-based regulator ranking.
• Therapeutic Development and Personalized Medicine: Supports identification of candidate regulatory targets for therapeutic intervention and personalized approaches.
• Interferon Signaling and ETS Transcription Factors: Elucidates regulators such as the ETS transcription factor family in interferon signaling using interferon-stimulation datasets.

Methodology:

Performs differential expression analysis, infers gene/protein regulatory interactions to build networks, ranks regulators using Gene Set Enrichment Analysis (GSEA), and benchmarks results across gene silencing datasets including application to 21 interferon-stimulation datasets.