ArticleNature biotechnology2023
scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching.
Article in Nature biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- OscillomeR infers ultradian oscillations and targets of the Hes family.Patterns (New York, N.Y.) · 2026Article
- The temporal architecture of the seminiferous epithelial cycle revealed by spatial transcriptomics.Cell · 2026Article
- CellUntangler: Separating distinct biological signals in single-cell data with deep generative models.Cell genomics · 2026Article
- VIST: variational inference for single cell time series.Genome biology · 2026Article
- Identification of Distinct Topological Structures From High-Dimensional Data.bioRxiv : the preprint server for biology · 2025Article
- Variational inference of single cell time series.bioRxiv : the preprint server for biology · 2025Article
- Rhythm profiling using COFE reveals multi-omic circadian rhythms in human cancers in vivo.PLoS biology · 2025Article
- Topological data analysis in single cell biology.Frontiers in immunology · 2025Review
- Deciphering the circadian rhythm in colorectal cancer: a bibliometric analysis of research landscape and trends.Frontiers in oncology · 2025Article
- Interpreting single-cell and spatial omics data using deep neural network training dynamics.Nature computational science · 2024Article
- scCompressSA: dual-channel self-attention based deep autoencoder model for single-cell clustering by compressing gene-gene interactions.BMC genomics · 2024Article
- SiFT: uncovering hidden biological processes by probabilistic filtering of single-cell data.Nature communications · 2024Article
- Matrix Reordering for Noisy Disordered Matrices: Optimality and Computationally Efficient Algorithms.IEEE transactions on information theory · 2024Article
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3 authors.
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Abstract
Single-cell RNA sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is challenging as cells simultaneously encode multiple, potentially cross-interfering, biological signals. Here we propose scPrisma, a spectral computational method that uses topological priors to decouple, enhance and filter different classes of biological processes in single-cell data, such as periodic and linear signals. We apply scPrisma to the analysis of the cell cycle in HeLa cells, circadian rhythm and spatial zonation in liver lobules, diurnal cycle in Chlamydomonas and circadian rhythm in the suprachiasmatic nucleus in the brain. scPrisma can be used to distinguish mixed cellular populations by specific characteristics such as cell type and uncover regulatory networks and cell-cell interactions specific to predefined biological signals, such as the circadian rhythm. We show scPrisma's flexibility in incorporating prior knowledge, inference of topologically informative genes and generalization to additional diverse templates and systems. scPrisma can be used as a stand-alone workflow for signal analysis and as a prior step for downstream single-cell analysis.
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