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

Jianhao Peng, Guillermo Serrano, Ian M. Traniello, Maria E. Calleja-Cervantes, Ullas V. Chembazhi, Sushant Bangru, Teresa Ezponda, Juan Roberto Rodriguez-Madoz, Auinash Kalsotra, Felipe Prosper, Idoia Ochoa, Mikel Hernaez

Research output: Contribution to journalArticlepeer-review


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 dynamics after tumor recognition and key regulatory patterns on a regenerating liver, and was able to implicate glial cells in the generation of distinct behavioral states in honeybees. SimiC hence establishes a new approach to quantitating regulatory architectures between distinct cellular phenotypes, with far-reaching implications for systems biology.

Original languageEnglish (US)
Article number351
JournalCommunications biology
Issue number1
StatePublished - Dec 2022

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences


Dive into the research topics of 'SimiC enables the inference of complex gene regulatory dynamics across cell phenotypes'. Together they form a unique fingerprint.

Cite this