ArticleNature communications2017
Reconstructing cell cycle pseudo time-series via single-cell transcriptome data.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers.
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Who cites it
83 citing papers in PubMed, 178 citations in OpenAlex.
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- OscillomeR infers ultradian oscillations and targets of the Hes family.Patterns (New York, N.Y.) · 2026Article
- Deciphering Cell Cycle Dynamics and Cell States in Single-cell RNA-seq data with SPAE.bioRxiv : the preprint server for biology · 2026Article
- CellUntangler: Separating distinct biological signals in single-cell data with deep generative models.Cell genomics · 2026Article
- Sequential RNA polymerase II activation drives human hematopoiesis.Cell reports · 2026Article
- Tumor prognostic risk stratification based on pseudo-time analysis of single-cell sequencing for patients with lung adenocarcinoma.Journal of thoracic disease · 2025Article
- Dynamic modelling of cell cycle arrest through integrated single-cell and mathematical modelling approaches.PLoS computational biology · 2025Article
- Mitigating Cell Cycle Effects in Multi-Omics Data: Solutions and Analytical Frameworks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Image-based inference of tumor cell trajectories enables large-scale cancer progression analysis.Science advances · 2025Article
- Characterizing the Cellular Constituents of Proximal Airway Disease in Granulomatosis With Polyangiitis.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2025Article
- Multi-omics integrated analysis reveals the molecular mechanism of tail fat deposition differences in sheep with different tail types.BMC genomics · 2025Article
- scNET: learning context-specific gene and cell embeddings by integrating single-cell gene expression data with protein-protein interactions.Nature methods · 2025Article
- Gene trajectory inference for single-cell data by optimal transport metrics.Nature biotechnology · 2025Article
- Classifying cell cycle states and a quiescent-like G0 state using single-cell transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- vmrseq: probabilistic modeling of single-cell methylation heterogeneity.Genome biology · 2024Article
- Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model.Communications biology · 2024Article
- A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information.Communications biology · 2024Article
23 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
Single-cell mRNA sequencing, which permits whole transcriptional profiling of individual cells, has been widely applied to study growth and development of tissues and tumors. Resolving cell cycle for such groups of cells is significant, but may not be adequately achieved by commonly used approaches. Here we develop a traveling salesman problem and hidden Markov model-based computational method named reCAT, to recover cell cycle along time for unsynchronized single-cell transcriptome data. We independently test reCAT for accuracy and reliability using several data sets. We find that cell cycle genes cluster into two major waves of expression, which correspond to the two well-known checkpoints, G1 and G2. Moreover, we leverage reCAT to exhibit methylation variation along the recovered cell cycle. Thus, reCAT shows the potential to elucidate diverse profiles of cell cycle, as well as other cyclic or circadian processes (e.g., in liver), on single-cell resolution.In single-cell RNA sequencing data of heterogeneous cell populations, cell cycle stage of individual cells would often be informative. Here, the authors introduce a computational model to reconstruct a pseudo-time series from single cell transcriptome data, identify the cell cycle stages, identify candidate cell cycle-regulated genes and recover the methylome changes during the cell cycle.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.