Work

Toward automated and explainable high-throughput perturbation analysis in single cells

Year: 2025

Type: article

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.

Source: Patterns

DOI: 10.1016/j.patter.2025.101228

Authors: Jesus Gonzalez-Ferrer, Mohammed A. Mostajo-Radji

Institution: University of California, Santa Cruz

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $4,900

Funders: California Institute for Regenerative Medicine, Office of the President, University of California, National Institute of Mental Health