SimiC enables the inference of complex gene regulatory dynamics across cell phenotypes.

ABSTRACT: Single-cell RNA-Sequencing has the potential to provide deep biological insights by revealing complex regulatory interactions across diverse cell phenotypes at single-cell resolution. However, current single-cell gene regulatory network inference methods produce a single regulatory network per input dataset, limiting their capability to uncover complex regulatory relationships across related cell phenotypes. We present SimiC, a single-cell gene regulatory inference framework that overcomes this limitation by jointly inferring distinct, but related, gene regulatory dynamics per phenotype. We show that SimiC uncovers key regulatory dynamics missed by previously proposed methods across a range of systems, both model and non-model alike. In particular, SimiC was able to uncover CAR T cell dyna

SUBMITTER: Peng J 

PROVIDER: S-EPMC9005655 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature