ArticleGenome biology2026
PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns.
Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Halo: a pretrained model for whole-cell segmentation from nuclei images in spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Multi-scale spatial testing recovers gene programs missed by existing detection methods.bioRxiv : the preprint server for biology · 2026Article
- Benchmarking cell-type-specific spatially variable gene detection methods.Briefings in bioinformatics · 2026Article
- PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns.Genome biology · 2026Article
- Identifying cell-type-specific spatially variable genes with ctSVG.Genome biology · 2025Article
- Identifying cell-type-specific spatially variable genes with ctSVG.Research square · 2024Article
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Authors and funding
2 authors.
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Abstract
Modeling temporal and spatial gene expression patterns in large-scale single-cell and spatial transcriptomics data is a computationally intensive task. We present PreTSA, a method that offers computational efficiency in modeling these patterns and is applicable to single-cell and spatial transcriptomics data comprising millions of cells. PreTSA consistently matches the results of state-of-the-art methods while significantly reducing computational time. PreTSA provides a unique solution for studying gene expression patterns in extremely large datasets.
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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.