ArticleBioinformatics (Oxford, England)2015
Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.
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Who cites it
76 citing papers in PubMed, 1 synthesis or guideline pooled it.
- What are the applications of single-cell RNA sequencing in cancer research: a systematic review.Journal of experimental & clinical cancer research : CR · 2021Pooled it
- mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities.Bioinformatics (Oxford, England) · 2026Article
- From time-course expression to gene regulation: direct linear ODE inference without finite-difference approximation.bioRxiv : the preprint server for biology · 2026Article
- Generative models of cell dynamics: from Neural ODEs to flow matching.Communications biology · 2026Review
- DeepCE: a deep learning framework for correlation-enhanced gene regulatory network inference in single-cell RNA sequencing data.Bioinformatics advances · 2026Article
- Learning stochastic processes with intrinsic noise from cross-sectional biological data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Dynamic gene regulatory network inference from single-cell data using optimal transport.Bioinformatics (Oxford, England) · 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
- Anomaly Detection with Graph Embedding in Bioinformatics: A Survey.Current genomics · 2025Review
- Inferring upstream regulatory genes of FOXP3 in human regulatory T cells from time-series transcriptomic data.NPJ systems biology and applications · 2024Article
- Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.Nucleic acids research · 2024Review
- Article
- A logic-incorporated gene regulatory network deciphers principles in cell fate decisions.eLife · 2024Article
- Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms.Briefings in bioinformatics · 2024Article
- Studying temporal dynamics of single cells: expression, lineage and regulatory networks.Biophysical reviews · 2024Review
- Quantifying the clusterness and trajectoriness of single-cell RNA-seq data.PLoS computational biology · 2024Article
- Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.Bioinformatics and biology insights · 2024Review
- Principles and challenges of modeling temporal and spatial omics data.Nature methods · 2023Review
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
16 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationHigh-dimensional single-cell snapshot data are becoming widespread in the systems biology community, as a mean to understand biological processes at the cellular level. However, as temporal information is lost with such data, mathematical models have been limited to capture only static features of the underlying cellular mechanisms.
resultsHere, we present a modular framework which allows to recover the temporal behaviour from single-cell snapshot data and reverse engineer the dynamics of gene expression. The framework combines a dimensionality reduction method with a cell time-ordering algorithm to generate pseudo time-series observations. These are in turn used to learn transcriptional ODE models and do model selection on structural network features. We apply it on synthetic data and then on real hematopoietic stem cells data, to reconstruct gene expression dynamics during differentiation pathways and infer the structure of a key gene regulatory network. AVAILABILITY AND IMPLEMENTATION: C++ and Matlab code available at https://www.helmholtz-muenchen.de/fileadmin/ICB/software/inferenceSnapshot.zip.
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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.