ArticleNature communications2017
Inference of differentiation time for single cell transcriptomes using cell population reference 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 17 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- scDGCN: A Graph-Based Deep Clustering Framework for Single-Cell Transcriptomics via Neighborhood-Enhanced Context Representation Learning.Interdisciplinary sciences, computational life sciences · 2026Article
- Clustering single-cell multi-omics data via weighted distance penalty and adaptive consistent graph regularization.PLoS computational biology · 2026Article
- Cell type-independent timekeeping gene modules enable embryonic stage prediction in zebrafish.bioRxiv : the preprint server for biology · 2025Article
- scSMD: a deep learning method for accurate clustering of single cells based on auto-encoder.BMC bioinformatics · 2025Article
- scGRN-Entropy: Inferring cell differentiation trajectories using single-cell data and gene regulation network-based transfer entropy.PLoS computational biology · 2024Article
- Spatial Reconstruction of Oligo and Single Cells by De Novo Coalescent Embedding of Transcriptomic Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- BLTSA: pseudotime prediction for single cells by branched local tangent space alignment.Bioinformatics (Oxford, England) · 2023Article
- Application of Single-Cell RNA Sequencing in Ovarian Development.Biomolecules · 2022Review
- IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data.PLoS computational biology · 2022Article
- A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data.Briefings in bioinformatics · 2021Review
- Interspecies chimeric conditions affect the developmental rate of human pluripotent stem cells.PLoS computational biology · 2021Article
- Towards systems tissue engineering: Elucidating the dynamics, spatial coordination, and individual cells driving emergent behaviors.Biomaterials · 2020Review
- The Contribution of lincRNAs at the Interface between Cell Cycle Regulation and Cell State Maintenance.iScience · 2020Article
- Cell lineage inference from SNP and scRNA-Seq data.Nucleic acids research · 2019Article
- Novel insights into cell cycle regulation of cell fate determination.Journal of Zhejiang University. Science. B · 2019Review
- Single-cell RNA-sequencing reveals the existence of naive and primed pluripotency in pre-implantation rhesus monkey embryos.Genome research · 2018Article
- Regulatory network characterization in development: challenges and opportunities.F1000Research · 2018Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA sequencing (scRNA-seq) is a powerful method for dissecting intercellular heterogeneity during development. Conventional trajectory analysis provides only a pseudotime of development, and often discards cell-cycle events as confounding factors. Here using matched cell population RNA-seq (cpRNA-seq) as a reference, we developed an "iCpSc" package for integrative analysis of cpRNA-seq and scRNA-seq data. By generating a computational model for reference "biological differentiation time" using cell population data and applying it to single-cell data, we unbiasedly associated cell-cycle checkpoints to the internal molecular timer of single cells. Through inferring a network flow from cpRNA-seq to scRNA-seq data, we predicted a role of M phase in controlling the speed of neural differentiation of mouse embryonic stem cells, and validated it through gene knockout (KO) experiments. By linking temporally matched cpRNA-seq and scRNA-seq data, our approach provides an effective and unbiased approach for identifying developmental trajectory and timing-related regulatory events.
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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.