Dissecting cell identity via network inference and in silico gene perturbation.

ABSTRACT: Cell identity is governed by the complex regulation of gene expression, represented as gene-regulatory networks¹. Here we use gene-regulatory networks inferred from single-cell multi-omics data to perform in silico transcription factor perturbations, simulating the consequent changes in cell identity using only unperturbed wild-type data. We apply this machine-learning-based approach, CellOracle, to well-established paradigms-mouse and human haematopoiesis, and zebrafish embryogenesis-and we correctly model reported changes in phenotype that occur as a result of transcription factor perturbation. Through systematic in silico transcription factor perturbation in the developing zebrafish, we simulate and experimentally validate a previously unreported phenotype that results from t

SUBMITTER: Kamimoto K 

PROVIDER: S-EPMC9946838 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature