ArticleBMC bioinformatics2016
SCOUP: a probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- From time-course expression to gene regulation: direct linear ODE inference without finite-difference approximation.bioRxiv : the preprint server for biology · 2026Article
- A DSSM network for inferring and prioritizing cell-type-specific regulons using single-cell RNA-seq data.BMC bioinformatics · 2025Article
- Gene Regulatory Network Inference from Pseudotime-Ordered scRNA-seq Data via Time-Lagged Divergence Measures.Bioinformatics research and applications : ... international symposium, ISBRA ... proceedings. ISBRA (Conference) · 2025Article
- Reconstructing Dynamic Gene Regulatory Networks Using f-Divergence from Time-Series scRNA-Seq Data.Current issues in molecular biology · 2025Article
- Time-Varying Gene Regulatory Networks Inference Using KL Divergence from Single Cell Data.Proceedings of the ... International Conference on Bioinformatics and Biomedical Technology · 2025Article
- Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders.Briefings in bioinformatics · 2025Article
- Attractor Landscape Analysis Reveals a Reversion Switch in the Transition of Colorectal Tumorigenesis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Stochastic modeling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones.Cell systems · 2025Article
- Advances in modeling cellular state dynamics: integrating omics data and predictive techniques.Animal cells and systems · 2025Review
- Decoding the principle of cell-fate determination for its reverse control.NPJ systems biology and applications · 2024Article
- Stochastic modelling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones.bioRxiv : the preprint server for biology · 2024Article
- Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.Bioinformatics and biology insights · 2024Review
- scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets.International journal of molecular sciences · 2023Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- New perspectives on biology, disease progression, and therapy response of head and neck cancer gained from single cell RNA sequencing and spatial transcriptomics.Oncology research · 2023Review
- A probabilistic Boolean model on hair follicle cell fate regulation by TGF-β.Biophysical journal · 2022Article
- Spatially and Temporally Distributed Complexity-A Refreshed Framework for the Study of GRN Evolution.Cells · 2022Article
- Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm.Entropy (Basel, Switzerland) · 2022Article
- Network inference with Granger causality ensembles on single-cell transcriptomics.Cell reports · 2022Article
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Abstract
backgroundSingle-cell technologies make it possible to quantify the comprehensive states of individual cells, and have the power to shed light on cellular differentiation in particular. Although several methods have been developed to fully analyze the single-cell expression data, there is still room for improvement in the analysis of differentiation.
resultsIn this paper, we propose a novel method SCOUP to elucidate differentiation process. Unlike previous dimension reduction-based approaches, SCOUP describes the dynamics of gene expression throughout differentiation directly, including the degree of differentiation of a cell (in pseudo-time) and cell fate. SCOUP is superior to previous methods with respect to pseudo-time estimation, especially for single-cell RNA-seq. SCOUP also successfully estimates cell lineage more accurately than previous method, especially for cells at an early stage of bifurcation. In addition, SCOUP can be applied to various downstream analyses. As an example, we propose a novel correlation calculation method for elucidating regulatory relationships among genes. We apply this method to a single-cell RNA-seq data and detect a candidate of key regulator for differentiation and clusters in a correlation network which are not detected with conventional correlation analysis.
conclusionsWe develop a stochastic process-based method SCOUP to analyze single-cell expression data throughout differentiation. SCOUP can estimate pseudo-time and cell lineage more accurately than previous methods. We also propose a novel correlation calculation method based on SCOUP. SCOUP is a promising approach for further single-cell analysis and available at https://github.com/hmatsu1226/SCOUP.
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