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Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC.

Nilesh Kumar¹, Bharat Mishra¹, Mohammad Athar²

¹Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA.

Methods in Molecular Biology (Clifton, N.J.)

|July 12, 2021

Summary

We introduce pySCENIC, a fast Python tool for analyzing single-cell RNA sequencing data. It infers gene regulatory networks (GRNs) and cell-specific transcription factor activity from gene expression patterns.

Keywords:

Gene co-expression network Gene regulatory network RNA-Seq count data scRNA-seq

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Area of Science:

Genomics
Transcriptomics
Epigenomics
Computational Biology

Background:

Next-generation sequencing (NGS) enables single-cell profiling.
Single-cell RNA sequencing (scRNA-seq) is crucial for characterizing cell populations and regulatory mechanisms.
Gene regulatory networks (GRNs) involve genes and transcription factors (TFs).

Purpose of the Study:

To present pySCENIC, a rapid Python implementation of the SCENIC pipeline.
To enable inference of cell-specific GRNs from scRNA-seq data.
To map TFs to gene regulatory networks and integrate diverse cell types.

Main Methods:

Utilizes scRNA-seq data for GRN inference.
Incorporates GRNBoost2 and GENIE3 algorithms for efficient GRN construction.
Employs a three-step pipeline: co-expression target identification, TF-motif enrichment for regulon discovery, and regulon activity scoring.

Main Results:

Provides a lightning-fast Python implementation of the SCENIC pipeline.
Facilitates the mapping of TFs onto GRNs.
Enables the inference of cell-specific GRNs by integrating various cell types.

Conclusions:

pySCENIC offers an efficient computational tool for GRN inference from scRNA-seq data.
The pipeline effectively identifies direct TF targets and assesses regulon activity across single cells.
Accelerates the analysis of gene regulatory mechanisms at the single-cell level.