ArticleNature communications2017
Dpath software reveals hierarchical haemato-endothelial lineages of Etv2 progenitors based on single-cell transcriptome analysis.
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 23 papers.
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Who cites it
23 citing papers in PubMed.
- Transcription factors regulating vasculogenesis and angiogenesis.Developmental dynamics : an official publication of the American Association of Anatomists · 2024Review
- GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership.Genome biology · 2023Article
- GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership.bioRxiv : the preprint server for biology · 2023Article
- SeATAC: a tool for exploring the chromatin landscape and the role of pioneer factors.Genome biology · 2023Article
- Revolutionizing cardiovascular medicine: targeted therapies for the cardiac conduction system.The Journal of clinical investigation · 2022Article
- ETV2 functions as a pioneer factor to regulate and reprogram the endothelial lineage.Nature cell biology · 2022Article
- Multi-omics assessment of dilated cardiomyopathy using non-negative matrix factorization.PloS one · 2022Article
- Blastocyst complementation and interspecies chimeras in gene edited pigs.Frontiers in cell and developmental biology · 2022Review
- FRMC: a fast and robust method for the imputation of scRNA-seq data.RNA biology · 2021Article
- Single-cell transcriptomic analysis identifies the conversion of zebrafish Etv2-deficient vascular progenitors into skeletal muscle.Nature communications · 2020Article
- ETV2-null porcine embryos survive to post-implantation following incomplete enucleation.Reproduction (Cambridge, England) · 2020Article
- scRCMF: Identification of Cell Subpopulations and Transition States From Single-Cell Transcriptomes.IEEE transactions on bio-medical engineering · 2020Article
- Single cell transcriptome dynamics from pluripotency to FLK1Development (Cambridge, England) · 2019Article
- When blood development meets single-cell transcriptomics.Blood science (Baltimore, Md.) · 2019Article
- Etv2 transcriptionally regulates Yes1 and promotes cell proliferation during embryogenesis.Scientific reports · 2019Article
- Hedgehog and Wnt Signaling Pathways Regulate Tail Regeneration.Stem cells and development · 2018Article
- A conserved HH-Gli1-Mycn network regulates heart regeneration from newt to human.Nature communications · 2018Article
- TCM visualizes trajectories and cell populations from single cell data.Nature communications · 2018Article
- scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data.Bioinformatics (Oxford, England) · 2018Article
- DrImpute: imputing dropout events in single cell RNA sequencing data.BMC bioinformatics · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Developmental, stem cell and cancer biologists are interested in the molecular definition of cellular differentiation. Although single-cell RNA sequencing represents a transformational advance for global gene analyses, novel obstacles have emerged, including the computational management of dropout events, the reconstruction of biological pathways and the isolation of target cell populations. We develop an algorithm named dpath that applies the concept of metagene entropy and allows the ranking of cells based on their differentiation potential. We also develop self-organizing map (SOM) and random walk with restart (RWR) algorithms to separate the progenitors from the differentiated cells and reconstruct the lineage hierarchies in an unbiased manner. We test these algorithms using single cells from Etv2-EYFP transgenic mouse embryos and reveal specific molecular pathways that direct differentiation programmes involving the haemato-endothelial lineages. This software program quantitatively assesses the progenitor and committed states in single-cell RNA-seq data sets in a non-biased manner.
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Registered trials
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