ScRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network.

ABSTRACT: Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage

SUBMITTER: Li C 

PROVIDER: S-EPMC11228399 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature