ArticleGenome biology2022
Universal prediction of cell-cycle position using transfer learning.
Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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68 citing papers in PubMed.
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- Deep learning models for cell cycle phase prediction from single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
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- CRISPRi screening in cultured human astrocytes uncovers distal enhancers controlling genes dysregulated in Alzheimer's disease.Nature neuroscience · 2026Article
- CellUntangler: Separating distinct biological signals in single-cell data with deep generative models.Cell genomics · 2026Article
- Exploration of the mechanism of anlotinib in reversing PD-1 immunotherapy resistance: insights from single-cell sequencing.Cancer gene therapy · 2026Article
- Single-nucleus transcriptomics identifies cell cycle and synaptic pathway dysregulation during OPC-to-glioma progression.Frontiers in cellular neuroscience · 2026Article
- DCPR: a deep learning framework for circadian phase reconstruction.BMC bioinformatics · 2025Article
- A Single Cell Atlas of the Newt Iris During Lens Regeneration.bioRxiv : the preprint server for biology · 2025Article
- Overexpression of Meis factors in late-stage retinal progenitors yields complex effects on temporal patterning and neurogenesis.Scientific reports · 2025Article
- All-Trans Retinoic Acid Impacts Early Palatal Shelves Development via the Wnt and TGF-β Signaling Pathways.Biomedicines · 2025Article
- Developmental control of DNA damage responses in α- and β-cells shapes the selective beta-cell susceptibility in diabetes.bioRxiv : the preprint server for biology · 2025Article
- Shared M0 macrophage infiltration-related gene signatures in osteoarthritis and osteomyelitis by integrating bulk and single-cell RNA transcriptome.European journal of medical research · 2025Article
- Systematic characterization of the composition and dynamics of processing body-associated mRNAs.Nature communications · 2025Article
8 more citing papers are in PubMed but not listed here.
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10 authors.
Funding
Abstract
backgroundThe cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data.
resultsHere, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell-cycle embedding using a fixed reference dataset and project new data into this reference embedding, an approach that overcomes key limitations of learning a dataset-dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species, and even sequencing assays.
conclusionsTricycle generalizes across datasets and is highly scalable and applicable to atlas-level single-cell RNA-seq data.
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