Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation. Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Here, the authors present single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer cell type-specific GRN dynamics from scRNA-seq and scATAC-seq datasets collected for diverse cell fate specification trajectories.

Automated summary

The authors developed a method called single-cell Multi-Task Network Inference (scMTNI) to accurately infer the dynamics of cell type-specific Gene Regulatory Networks (GRNs) from scRNA-seq and scATAC-seq datasets. This method can identify key regulators and accurately model network dynamics for diverse processes such as cellular reprogramming and differentiation.