REACTOR: REgulon Activity analysis and Comparison Tool for single-cell transcriptOmics Research

Area of Science:

• Computational Biology
• Genomics
• Transcriptomics

Background:

• Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity.
• Understanding differential gene activity across conditions is crucial for biological discovery.
• Existing frameworks for regulon analysis require enhancement for robust statistical comparison.

Purpose of the Study:

• Introduce REACTOR, a novel computational tool for differential regulon activity detection.
• Provide a robust statistical method for comparing regulon activity between conditions in scRNA-seq data.
• Facilitate the identification of condition- and cell type-specific regulatory networks.

Main Methods:

• Development of the REACTOR R-package.
• Implementation of a robust statistical test for differential regulon activity.
• Application and validation using a publicly available COVID-19 scRNA-seq dataset.

Main Results:

• REACTOR successfully detects differential activity of transcriptional regulators and their target genes (regulons).
• The tool enables robust statistical comparison of regulon activity across multiple replicates and conditions.
• Identification of key condition- and cell type-specific regulons was demonstrated.

Conclusions:

• REACTOR enhances the framework for regulon analysis in scRNA-seq data.
• The tool provides a powerful method for identifying condition-specific regulatory changes.
• REACTOR's utility is demonstrated in a relevant biological context (COVID-19).