Temporal modelling using single-cell transcriptomics
Open accessAbstract
Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for.
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A combined LSTM and GNN model has been used to represent patient status sequences and temporal medical event graphs to predict future prescriptions or disease codes.
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scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
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Self-citationA visual–omics foundation model to bridge histopathology with spatial transcriptomics
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- Authors
- Ding, J., Sharon, N., & Bar-Joseph, Z.
- Affiliations
- McGill University Health Centre (CA)
- Technion – Israel Institute of Technology (IL)
- Venue
- Nature Reviews Genetics
- Published
- 2022-01-31
- Volume
- 23
- Issue
- 6
- Pages
- 355-368
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC10354343
- OpenAlex
- W4210403261
- Cited by
- 195
- References
- 103
- Citations by year
- 2026: 34 · 2025: 62 · 2024: 51 · 2023: 39 · 2022: 8 · 2020: 1
- FWCI
- 4.21
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5r01gm122096-04, OT2OD026682, 1R01GM122096)
- National Institute on Aging (U54AG075931)
- National Cancer Institute (U24CA268108)
- National Institute of General Medical Sciences (R01 GM122096)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- temporal modeling, cell state dynamics, cell trajectory inference, single-cell data integration, gene regulatory networks, cell differentiation, cell fate transitions
- MeSH terms
- Cell Differentiation, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis, Transcriptome
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