A scalable SCENIC workflow for single-cell gene regulatory network analysis.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) generates high-dimensional data for understanding cellular heterogeneity.
• SCENIC (Single-Cell Inference of Transcriptional regulatory Networks Using Gene Ontology) is a method for reconstructing gene regulatory networks and inferring cell types.
• Previous SCENIC implementations faced computational challenges with large scRNA-seq datasets.

Purpose of the Study:

• To present an improved and significantly faster version of SCENIC, named pySCENIC, implemented in Python.
• To provide a streamlined protocol for analyzing scRNA-seq data using software containers and Nextflow pipelines.
• To enable efficient reconstruction of regulons and assessment of their activity in individual cells for clustering.

Main Methods:

• Refactored SCENIC into Python (pySCENIC) for a tenfold speed increase.
• Packaged pySCENIC into software containers for enhanced usability.
• Workflow stages: 1. Infer coexpression modules (GRNBoost2). 2. Prune indirect targets using cis-regulatory motif discovery (cisTarget). 3. Quantify regulon activity (AUCell).

Main Results:

• Achieved a tenfold increase in speed for SCENIC analysis.
• Demonstrated efficient processing of large datasets (e.g., 50,000 cells, 10,000 genes in <2 hours).
• Enabled refinement of regulons using epigenomic track databases and motifs.
• Facilitated visualization of cell clusters based on regulon activity patterns using SCope.

Conclusions:

• pySCENIC offers a substantially faster and more accessible method for scRNA-seq analysis.
• The protocol supports standard best practices, integrating Nextflow pipelines and software containers.
• This advancement accelerates the discovery of gene regulatory mechanisms and cell populations from scRNA-seq data.