ArticlePLoS computational biology2025
Integrating multiple spatial transcriptomics data using community-enhanced graph contrastive learning.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Applications of Spatial Transcriptomics in Veterinary Medicine: A Scoping Review of Research, Diagnostics, and Treatment Strategies.International journal of molecular sciences · 2025Article
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2 authors.
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Abstract
Due to the rapid development of spatial sequencing technologies, large amounts of spatial transcriptomic datasets have been generated across various technological platforms or different biological conditions (e.g., control vs. treatment). Spatial transcriptomics data coming from different platforms usually has different resolutions. Moreover, current methods do not consider the heterogeneity of spatial structures within and across slices when modeling spatial transcriptomics data with graph-based methods. In this study, we propose a community-enhanced graph contrastive learning-based method named Tacos to integrate multiple spatial transcriptomics data. We applied Tacos to several real datasets coming from different platforms under different scenarios. Systematic benchmark analyses demonstrate Tacos's superior performance in integrating different slices. Furthermore, Tacos can accurately denoise the spatially resolved transcriptomics data.
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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.