ArticleCell genomics2026
CellUntangler: Separating distinct biological signals in single-cell data with deep generative models.
Article in Cell genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- ProtoCloud: A prototypical self-explaining model for single-cell analysis.Cell genomics · 2026Article
- Untangling biological complexity: A deep learning approach to separating multiple signals in single-cell data.Cell genomics · 2026Article
- Identification of Distinct Topological Structures From High-Dimensional Data.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Single-cell RNA sequencing has provided new insights into both intracellular and intercellular processes. However, multiple processes, such as cell-type programs, differentiation, and the cell cycle, often occur simultaneously within one cell. Existing methods typically target a single process and impose restrictive assumptions, risking the loss of valuable biological information. We introduce CellUntangler, a deep generative model that embeds cells into a latent space composed of multiple subspaces, each tailored with an appropriate geometry to capture a distinct signal. Applied to datasets of cycling-only and mixed cycling/non-cycling cells, CellUntangler disentangles the cell cycle from other processes such as cell type. The framework generalizes to disentangle additional signals, including spatial, tissue dissociation, interferon response, and cell-type identity. By providing flexible embeddings to capture various signals, CellUntangler enables selective enhancement or filtering of signals at the gene-expression level, offering a powerful tool for disentangling complex biological processes in single-cell data.
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Registered trials
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