Toward automated and explainable high-throughput perturbation analysis in single cells.
basic_science · Level V
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- Record sourced from PubMed, PMID 40264958.
- Also identified by DOI 10.1016/j.patter.2025.101228 and PMC identifier 12010446.
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Abstract
Perturbation analysis in single-cell RNA sequencing (scRNA-seq) data is challenging due to the complexity of cellular responses. To address this, Xu and Fleming et al. developed CellCap, a generative deep-learning model that decodes the perturbation effect on a particular cell state. CellCap extracts interpretable latent representations of perturbation response modules, identifying key cellular pathways activated under various conditions. This allows for a deeper understanding of cell-state-specific responses to genetic, chemical, or biological perturbations.