bio-gene-regulatory-networks-scenic-regulons

What problem does it solve?

Infer transcription factor regulons and gene regulatory networks from single-cell RNA-seq data using pySCENIC, enabling identification of master regulators and regulatory programs.

Core Features & Use Cases

• pySCENIC three-step pipeline: GRNBoost2 for co-expression modules, cisTarget motif pruning, and AUCell scoring of regulon activity per cell.
• Identify TF regulons and score TF activity across cells to understand cellular identity and regulatory states; suitable for master regulator discovery and cell-type annotation.
• Use Case: analyze a preprocessing scRNA-seq dataset to derive regulon activities and cluster cells by regulatory programs.

Quick Start

Run pySCENIC on your preprocessed scRNA-seq data to infer regulons, score activity per cell, and identify master regulators of cell identity.