ArticlePLoS computational biology2025
A graph neural network-based spatial multi-omics data integration method for deciphering spatial domains.
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 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- MultiSpaceNet: graph-based joint representation learning for paired spatial transcriptome-proteome data.Bioinformatics (Oxford, England) · 2026Article
- Integrative Epigenomics: Bioinformatics Strategies for Multi-Omics Data Analysis in Health and Disease.Epigenomes · 2026Review
- Multimodal spatial omics: From data acquisition to computational integration.Patterns (New York, N.Y.) · 2026Review
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- A Guide for Spatial Omics Technologies: Innovation, Evaluation, and Application.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- The good, the bad, and the ugly: opportunities, challenges, and pitfalls in spatial proteomics modeling.Briefings in bioinformatics · 2026Review
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Recent advancements of spatial sequencing technologies enable measurements of transcriptomic and epigenomic profiles within the same tissue slice, providing an unprecedented opportunity to understand cellular microenvironments. However, effective approaches for the integrative analysis of such spatial multi-omics data are lacking. Here, we propose SpaMI, a graph neural network-based model which extract features by contrastive learning strategy for each omics and integrate different omics by an attention mechanism to integrate spatial multi-omics data. We applied SpaMI to both simulated data and three real spatial multi-omics datasets derived from the same tissue slices, including spatial epigenome-transcriptome and transcriptome-proteome data. By comparing SpaMI with the state-of-the-art methods on simulation and real datasets, we demonstrate the superior performance of SpaMI in identifying spatial domain and data denoising.
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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.