ArticleBioinformatics (Oxford, England)2019
Continuous-state HMMs for modeling time-series single-cell RNA-Seq data.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Synthetic developmental engineering of human liver organogenesis.Development (Cambridge, England) · 2026Review
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases.Nature biomedical engineering · 2025Article
- TemporalVAE: atlas-assisted temporal mapping of time-series single-cell transcriptomes during embryogenesis.Nature cell biology · 2025Article
- Trajectory inference from single-cell genomics data with a process time model.PLoS computational biology · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators.Briefings in bioinformatics · 2024Article
- Cellular proliferation biases clonal lineage tracing and trajectory inference.Bioinformatics (Oxford, England) · 2024Article
- An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data.Fundamental research · 2024Article
- Modeling single cell trajectory using forward-backward stochastic differential equations.PLoS computational biology · 2024Article
- Studying temporal dynamics of single cells: expression, lineage and regulatory networks.Biophysical reviews · 2024Review
- LVPT: Lazy Velocity Pseudotime Inference Method.Biomolecules · 2023Article
- Understanding tumour endothelial cell heterogeneity and function from single-cell omics.Nature reviews. Cancer · 2023Review
- The specious art of single-cell genomics.PLoS computational biology · 2023Article
- Density-based detection of cell transition states to construct disparate and bifurcating trajectories.Nucleic acids research · 2022Article
- Temporal modelling using single-cell transcriptomics.Nature reviews. Genetics · 2022Review
- TraSig: inferring cell-cell interactions from pseudotime ordering of scRNA-Seq data.Genome biology · 2022Article
- Dynamic inference of cell developmental complex energy landscape from time series single-cell transcriptomic data.PLoS computational biology · 2022Article
- Reconstructed signaling and regulatory networks identify potential drugs for SARS-CoV-2 infection.bioRxiv : the preprint server for biology · 2021Article
- Optimal transport analysis reveals trajectories in steady-state systems.PLoS computational biology · 2021Article
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2 authors.
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Abstract
motivationMethods for reconstructing developmental trajectories from time-series single-cell RNA-Seq (scRNA-Seq) data can be largely divided into two categories. The first, often referred to as pseudotime ordering methods are deterministic and rely on dimensionality reduction followed by an ordering step. The second learns a probabilistic branching model to represent the developmental process. While both types have been successful, each suffers from shortcomings that can impact their accuracy.
resultsWe developed a new method based on continuous-state HMMs (CSHMMs) for representing and modeling time-series scRNA-Seq data. We define the CSHMM model and provide efficient learning and inference algorithms which allow the method to determine both the structure of the branching process and the assignment of cells to these branches. Analyzing several developmental single-cell datasets, we show that the CSHMM method accurately infers branching topology and correctly and continuously assign cells to paths, improving upon prior methods proposed for this task. Analysis of genes based on the continuous cell assignment identifies known and novel markers for different cell types. AVAILABILITY AND IMPLEMENTATION: Software and Supporting website: www.andrew.cmu.edu/user/chiehl1/CSHMM/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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