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

• Published in: Methods in molecular biology (Clifton, N.J.) Vol. 2328; p. 171
• Main Authors: Kumar, Nilesh, Mishra, Bharat, Athar, Mohammad, Mukhtar, Shahid
• Format: Journal Article
• Language: English
• Published: United States 2021
• Subjects: Algorithms; Amino Acid Motifs - genetics; Cluster Analysis; Gene Regulatory Networks - genetics; Programming Languages; RNA-Seq - methods; Single-Cell Analysis - methods; Transcription Factors - genetics; Transcription Factors - metabolism; Gene regulatory network; scRNA-seq; RNA-Seq count data; Gene co-expression network
• ISSN: 1940-6029
• DOI: 10.1007/978-1-0716-1534-8_10

With the advent of recent next-generation sequencing (NGS) technologies in genomics, transcriptomics, and epigenomics, profiling single-cell sequencing became possible. The single-cell RNA sequencing (scRNA-seq) is widely used to characterize diverse cell populations and ascertain cell type-specific regulatory mechanisms. The gene regulatory network (GRN) mainly consists of genes and their regulators-transcription factors (TF). Here, we describe the lightning-fast Python implementation of the SCENIC (Single-Cell reEgulatory Network Inference and Clustering) pipeline called pySCENIC. Using single-cell RNA-seq data, it maps TFs onto gene regulatory networks and integrates various cell types to infer cell-specific GRNs. There are two fast and efficient GRN inference algorithms, GRNBoost2 and GENIE3, optionally available with pySCENIC. The pipeline has three steps: (1) identification of potential TF targets based on co-expression; (2) TF-motif enrichment analysis to identify the direct targets (regulons); and (3) scoring the activity of regulons (or other gene sets) on single cell types.