High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0.

ABSTRACT:

Motivation

Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above.

Results

In this work, we present the Inferelator 3.0, which has been significantly updated to integrate da

SUBMITTER: Skok Gibbs C 

PROVIDER: S-EPMC9048651 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature