Article

scE <sup>2</sup> TM improves single-cell embedding interpretability and reveals cellular perturbation signatures

2025-12-01

Abstract excerpt

Single-cell RNA sequencing technologies have revolutionized our understanding of cellular heterogeneity, yet computational methods often struggle to balance performance with biological interpretability. Embedded topic models have been widely used for interpretable single-cell embedding learning. However, these models suffer from the potential problem of interpretation collapse, where topics semantically collapse t...

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Literature Corpus work: 4414e937-df2b-5a3d-af43-0c8fe0de310c
DOI: 10.1101/2025.11.27.691023

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