ArticleFrontiers in genetics2019
Revealing Dynamic Mechanisms of Cell Fate Decisions From Single-Cell Transcriptomic Data.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 29 citations in OpenAlex.
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- Branch-specific gene discovery in cell differentiation using multi-omics graph attention.PLoS computational biology · 2025Article
- Marker genes reveal dynamic features of cell evolving processes.Bioinformatics advances · 2025Article
- Decoding the principle of cell-fate determination for its reverse control.NPJ systems biology and applications · 2024Article
- Integrating frontiers: a holistic, quantum and evolutionary approach to conquering cancer through systems biology and multidisciplinary synergy.Frontiers in oncology · 2024Review
- LVPT: Lazy Velocity Pseudotime Inference Method.Biomolecules · 2023Article
- exFINDER: identify external communication signals using single-cell transcriptomics data.Nucleic acids research · 2023Article
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- The Quaking RNA-binding proteins as regulators of cell differentiation.Wiley interdisciplinary reviews. RNA · 2022Review
- RNA velocity unraveled.PLoS computational biology · 2022Article
- GEOMETRIC STRUCTURE GUIDED MODEL AND ALGORITHMS FOR COMPLETE DECONVOLUTION OF GENE EXPRESSION DATA.Foundations of data science (Springfield, Mo.) · 2022Article
- Differential Single Cell Responses of Embryonic Stem Cells Versus Embryoid Bodies to Gravity Mechanostimulation.Stem cells and development · 2022Article
- Spatially and Temporally Distributed Complexity-A Refreshed Framework for the Study of GRN Evolution.Cells · 2022Article
- Network inference with Granger causality ensembles on single-cell transcriptomics.Cell reports · 2022Article
- Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics.Nature communications · 2021Article
- Deciphering cell-cell interactions and communication from gene expression.Nature reviews. Genetics · 2021Review
- Revealing Dynamic Mechanisms of Cell Fate Decisions From Single-Cell Transcriptomic Data.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Cell fate decisions play a pivotal role in development, but technologies for dissecting them are limited. We developed a multifunction new method, Topographer, to construct a "quantitative" Waddington's landscape of single-cell transcriptomic data. This method is able to identify complex cell-state transition trajectories and to estimate complex cell-type dynamics characterized by fate and transition probabilities. It also infers both marker gene networks and their dynamic changes as well as dynamic characteristics of transcriptional bursting along the cell-state transition trajectories. Applying this method to single-cell RNA-seq data on the differentiation of primary human myoblasts, we not only identified three known cell types, but also estimated both their fate probabilities and transition probabilities among them. We found that the percent of genes expressed in a bursty manner is significantly higher at (or near) the branch point (~97%) than before or after branch (below 80%), and that both gene-gene and cell-cell correlation degrees are apparently lower near the branch point than away from the branching. Topographer allows revealing of cell fate mechanisms in a coherent way at three scales: cell lineage (macroscopic), gene network (mesoscopic), and gene expression (microscopic).
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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.