Modeling interpretable correspondence between cell state and perturbation response with CellCap.

ABSTRACT: Single-cell transcriptomics, in conjunction with genetic and compound perturbations, offers a robust approach for exploring cellular behaviors in diverse contexts. Such experiments allow uncovering cell-state-specific responses to perturbations, a crucial aspect in unraveling the intricate molecular mechanisms governing cellular behavior and potentially discovering novel regulatory pathways and therapeutic targets. However, prevailing computational methods predominantly focus on predicting average cellular responses, disregarding the inherent response heterogeneity associated with cell state diversity. In this study, we present CellCap, a deep generative model designed for the end-to-end analysis of single-cell perturbation experiments. CellCap employs sparse dictionary learning in a laten

SUBMITTER: Xu Y 

PROVIDER: S-EPMC10979976 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature