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.
Journal article
2026-03-01T00:00:00+00:00
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Nuffield Department for Women's & Reproductive Health, University of Oxford, Oxford, UK; Health Data Research UK, London, UK. Electronic address: christopher.yau@wrh.ox.ac.uk.
Humans, Sequence Analysis, RNA, Single-Cell Analysis, Deep Learning