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Untangling biological complexity: A deep learning approach to separating multiple signals in single-cell data
1Nuffield Department for Women's & Reproductive Health, University of Oxford, Oxford, UK; Health Data Research UK, London, UK.
Cell Genomics
|March 12, 2026
Abstract:
Single-cell RNA sequencing (scRNA-seq) provides an instantaneous snapshot of the transcriptional state of a cell, which results from the simultaneous activity of many cellular processes. In this issue of Cell Genomics, Chen et al.1 describe the development of CellUntangler, a deep-learning-based model that allows the capture and filtering of multiple biological signals in scRNA-seq data.

