ArticleiScience2022
Distance covariance entropy reveals primed states and bifurcation dynamics in single-cell RNA-Seq data.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Modeling combinatorial regulation from single-cell multi-omics provides regulatory units underpinning cell type landscape using cRegulon.Genome biology · 2025Article
- Gene regulatory network inference during cell fate decisions by perturbation strategies.NPJ systems biology and applications · 2025Article
- FateNet: an integration of dynamical systems and deep learning for cell fate prediction.Bioinformatics (Oxford, England) · 2024Article
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3 authors.
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Abstract
Cell-fate transitions are fundamental to development and differentiation. Studying them with single-cell omic data is important to advance our understanding of the cell-fate commitment process, yet this remains challenging. Here we present a computational method called DICE, which analyzes the entropy of expression covariation patterns and which is applicable to static and dynamically changing cell populations. Using only single-cell RNA-Seq data, DICE is able to predict multipotent primed states and their regulatory factors, which we subsequently validate with single-cell epigenomic data. DICE reveals that primed states are often defined by epigenetic regulators or pioneer factors alongside lineage-specific transcription factors. In developmental time course single-cell RNA-Seq datasets, DICE can pinpoint the timing of bifurcations more precisely than lineage-trajectory inference algorithms or competing variance-based methods. In summary, by studying the dynamic changes of expression covariation entropy, DICE can help elucidate primed states and bifurcation dynamics without the need for single-cell epigenomic data.
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