PT  - JOURNAL ARTICLE
AU  - Claudia Skok Gibbs
AU  - Christopher A Jackson
AU  - Giuseppe-Antonio Saldi
AU  - Aashna Shah
AU  - Andreas Tjärnberg
AU  - Aaron Watters
AU  - Nicholas De Veaux
AU  - Konstantine Tchourine
AU  - Ren Yi
AU  - Tymor Hamamsy
AU  - Dayanne M Castro
AU  - Nicholas Carriero
AU  - David Gresham
AU  - Emily R Miraldi
AU  - Richard Bonneau
TI  - Single-cell gene regulatory network inference at scale: The Inferelator 3.0
AID  - 10.1101/2021.05.03.442499
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.05.03.442499
4099  - http://biorxiv.org/content/early/2021/05/04/2021.05.03.442499.1.short
4100  - http://biorxiv.org/content/early/2021/05/04/2021.05.03.442499.1.full
AB  - 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. In this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator 3.0 reliably learns informative networks from the model organisms Bacillus subtilis and Saccharomyces cerevisiae. We demonstrate its capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data.Competing Interest StatementThe authors have declared no competing interest.