ArticleCell systems2025
DeST-OT: Alignment of spatiotemporal transcriptomics data.
Article in Cell systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- OT-knn: A neighborhood-aware optimal transport framework for aligning spatial transcriptomics data.PLoS computational biology · 2026Article
- CellRank: consistent and data view agnostic fate mapping for single-cell genomics.Nature protocols · 2026Review
- Riemannian metric learning for alignment of spatial multiomics.Bioinformatics (Oxford, England) · 2026Article
- Accurate trajectory inference in time-series spatial transcriptomics with structurally-constrained optimal transport.Nature communications · 2026Article
- Review
- Topography-aware optimal transport for alignment of spatial omics data.Cell reports methods · 2026Article
- Multimodal spatial alignment and morphology mapping with MOSAICField.bioRxiv : the preprint server for biology · 2026Article
- stVCR: spatiotemporal dynamics of single cells.Nature methods · 2026Article
- JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Riemannian Metric Learning for Alignment of Spatial Multiomics.bioRxiv : the preprint server for biology · 2025Article
- From features to slice: parameter-cloud modeling of spatial transcriptomics for simulation and 3D interpolatory augmentation.bioRxiv : the preprint server for biology · 2025Article
- Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data.NPJ systems biology and applications · 2025Review
- JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.Advances in neural information processing systems · 2025Article
- Randomized Spatial PCA (RASP): A computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data.PLoS computational biology · 2025Article
- sc4D: spatio-temporal single-cell transcriptomics analysis through embedded optimal transport identifies joint glial response to Alzheimer's disease pathology.bioRxiv : the preprint server for biology · 2025Article
- STIFT: spatiotemporal transcriptomics integration through spatially informed multi-timepoint bridging.Briefings in bioinformatics · 2025Article
- MUSE: A Multi-slice Joint Analysis Method for Spatial Transcriptomics Experiments.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2025Article
- A spatiotemporal atlas of mouse gastrulation and early organogenesis to explore axial patterning and project in vitro models onto in vivo space.Cell reports · 2025Article
- Quantifying Landscape and Flux from Single-Cell Omics: Unraveling the Physical Mechanisms of Cell Function.JACS Au · 2025Review
- Randomized Spatial PCA (RASP): a computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data.bioRxiv : the preprint server for biology · 2025Article
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Abstract
Spatially resolved transcriptomics (SRT) measures mRNA transcripts at thousands of locations within a tissue slice, revealing spatial variations in gene expression and cell types. SRT has been applied to tissue slices from multiple time points during the development of an organism. We introduce developmental spatiotemporal optimal transport (DeST-OT), a method to align spatiotemporal transcriptomics data using optimal transport (OT). DeST-OT uses semi-relaxed OT to model cellular growth, death, and differentiation processes. We also derive a growth distortion metric and a migration metric to quantify the plausibility of spatiotemporal alignments. DeST-OT outperforms existing methods on the alignment of spatiotemporal transcriptomics data from developing mouse kidney and axolotl brain. DeST-OT estimated growth rates also provide insights into the gene expression programs governing the growth and differentiation of cells over space and time.
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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.