ArticleBioinformatics (Oxford, England)2017
HopLand: single-cell pseudotime recovery using continuous Hopfield network-based modeling of Waddington's epigenetic landscape.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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24 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
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- Cell Fate Dynamics Reconstruction Identifies TPT1 and PTPRZ1 Feedback Loops as Master Regulators of Differentiation in Pediatric Glioblastoma-Immune Cell Networks.Interdisciplinary sciences, computational life sciences · 2025Article
- EnhancerNet: a predictive model of cell identity dynamics through enhancer selection.Development (Cambridge, England) · 2024Article
- Single-cell transcriptional uncertainty landscape of cell differentiation.F1000Research · 2023Article
- Inferring structural and dynamical properties of gene networks from data with deep learning.NAR genomics and bioinformatics · 2022Article
- Identification of microRNAs related with neural germ layer lineage-specific progenitors during reprogramming.Journal of molecular histology · 2022Article
- Dissecting cell fate dynamics in pediatric glioblastoma through the lens of complex systems and cellular cybernetics.Biological cybernetics · 2022Review
- Comparison of cell state models derived from single-cell RNA sequencing data: graph versus multi-dimensional space.Mathematical biosciences and engineering : MBE · 2022Article
- A Review of Mathematical and Computational Methods in Cancer Dynamics.Frontiers in oncology · 2022Review
- Landscape and kinetic path quantify critical transitions in epithelial-mesenchymal transition.Biophysical journal · 2021Article
- Velo-Predictor: an ensemble learning pipeline for RNA velocity prediction.BMC bioinformatics · 2021Article
- Statistical mechanics meets single-cell biology.Nature reviews. Genetics · 2021Review
- Forecasting cellular states: from descriptive to predictive biology via single-cell multiomics.Current opinion in systems biology · 2021Article
- Inferring TF activation order in time series scRNA-Seq studies.PLoS computational biology · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationThe interpretation of transcriptional dynamics in single-cell data, especially pseudotime estimation, could help understand the transition of gene expression profiles. The recovery of pseudotime increases the temporal resolution of single-cell transcriptional data, but is challenging due to the high variability in gene expression between individual cells. Here, we introduce HopLand, a pseudotime recovery method using continuous Hopfield network to map cells to a Waddington's epigenetic landscape. It reveals from the single-cell data the combinatorial regulatory interactions among genes that control the dynamic progression through successive cell states.
resultsWe applied HopLand to different types of single-cell transcriptomic data. It achieved high accuracies of pseudotime prediction compared with existing methods. Moreover, a kinetic model can be extracted from each dataset. Through the analysis of such a model, we identified key genes and regulatory interactions driving the transition of cell states. Therefore, our method has the potential to generate fundamental insights into cell fate regulation. AVAILABILITY AND IMPLEMENTATION: The MATLAB implementation of HopLand is available at https://github.com/NetLand-NTU/HopLand . CONTACT: zhengjie@ntu.edu.sg.
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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.