ArticleBioinformatics (Oxford, England)2018
scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 2 of them syntheses that pooled it.
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Who cites it
67 citing papers in PubMed, 2 syntheses or guidelines pooled it, 141 citations in OpenAlex.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Learning stochastic dynamics and cell-fate landscapes from single-cell snapshots via optimal transport.Science advances · 2026Article
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- Single-cell multi-omics and machine learning for dissecting stemness in cancer.Briefings in bioinformatics · 2025Review
- Quantifying Landscape and Flux from Single-Cell Omics: Unraveling the Physical Mechanisms of Cell Function.JACS Au · 2025Review
- Multi-Transcriptomic Analysis Reveals GSC-Driven MES-Like Differentiation via EMT in GBM Cell-Cell Communication.Biomedicines · 2025Article
- Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis.Entropy (Basel, Switzerland) · 2025Review
- Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges.Genes & development · 2025Review
- Single-cell multiome characterizing intercellular communication and intracellular regulation of epithelium and mesenchymal during secondary palate development in mice.Computational and structural biotechnology journal · 2025Article
- Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics.Nature reviews. Molecular cell biology · 2025Review
- Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing.Burns & trauma · 2025Review
- Transiently increased coordination in gene regulation during cell phenotypic transitions.PRX life · 2024Article
- Cancerous time estimation for interpreting the evolution of lung adenocarcinoma.Briefings in bioinformatics · 2024Article
- Leveraging gene correlations in single cell transcriptomic data.BMC bioinformatics · 2024Article
- Tipping points in epithelial-mesenchymal lineages from single-cell transcriptomics data.Biophysical journal · 2024Article
- Uncovering underlying physical principles and driving forces of cell differentiation and reprogramming from single-cell transcriptomics.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
7 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Motivation: Single-cell RNA-sequencing (scRNA-seq) offers unprecedented resolution for studying cellular decision-making processes. Robust inference of cell state transition paths and probabilities is an important yet challenging step in the analysis of these data. Results: Here we present scEpath, an algorithm that calculates energy landscapes and probabilistic directed graphs in order to reconstruct developmental trajectories. We quantify the energy landscape using 'single-cell energy' and distance-based measures, and find that the combination of these enables robust inference of the transition probabilities and lineage relationships between cell states. We also identify marker genes and gene expression patterns associated with cell state transitions. Our approach produces pseudotemporal orderings that are-in combination-more robust and accurate than current methods, and offers higher resolution dynamics of the cell state transitions, leading to new insight into key transition events during differentiation and development. Moreover, scEpath is robust to variation in the size of the input gene set, and is broadly unsupervised, requiring few parameters to be set by the user. Applications of scEpath led to the identification of a cell-cell communication network implicated in early human embryo development, and novel transcription factors important for myoblast differentiation. scEpath allows us to identify common and specific temporal dynamics and transcriptional factor programs along branched lineages, as well as the transition probabilities that control cell fates. Availability and implementation: A MATLAB package of scEpath is available at https://github.com/sqjin/scEpath. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.