ArticleBriefings in bioinformatics2024
xSiGra: explainable model for single-cell spatial data elucidation.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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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.
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Who cites it
15 citing papers in PubMed.
- A trifluoromethyl quinazoline inhibits hepatocellular carcinoma proliferation through HDAC1 binding and multi-target mechanisms.iScience · 2026Article
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- MicNet: integrating spatially resolved transcriptomes and pathology images by contrastive deep neural network.Genome biology · 2026Article
- Cross-modality representation and multi-sample integration of spatially resolved omics data.Briefings in bioinformatics · 2026Article
- Genetic burden across core genes of the PI3K-AKT-mTOR pathway is associated with susceptibility to microscopic polyangiitis: a Chinese cohort study.Frontiers in immunology · 2026Article
- Spatial omics: applications and utility in profiling the tumor microenvironment.Cancer metastasis reviews · 2025Review
- Cracking PRMT5: Mechanistic insights, clinical advances, and AI-driven strategies.Cancer letters · 2025Review
- Identification of a Four-Gene Signature Based on Metal Metabolism for Alzheimer's Disease Diagnosis.Genes · 2025Article
- Spatial transcriptomic data denoising and domain identification by a community strength-augmented graph autoencoder.Briefings in bioinformatics · 2025Article
- Spatial transcriptomics: a bibliometric analysis with large language model on English literatures.Briefings in bioinformatics · 2025Article
- Unveiling fine-scale spatial structures and amplifying gene expression signals in ultra-large ST slices with HERGAST.Nature communications · 2025Article
- Anomaly Detection with Graph Embedding in Bioinformatics: A Survey.Current genomics · 2025Review
- Artificial intelligence-enhanced electrocardiography improves the detection of coronary artery disease.Computational and structural biotechnology journal · 2025Article
- High-throughput gene expression analysis with TempO-LINC sensitively resolves complex brain, lung and kidney heterogeneity at single-cell resolution.Scientific reports · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
Recent advancements in spatial imaging technologies have revolutionized the acquisition of high-resolution multichannel images, gene expressions, and spatial locations at the single-cell level. Our study introduces xSiGra, an interpretable graph-based AI model, designed to elucidate interpretable features of identified spatial cell types, by harnessing multimodal features from spatial imaging technologies. By constructing a spatial cellular graph with immunohistology images and gene expression as node attributes, xSiGra employs hybrid graph transformer models to delineate spatial cell types. Additionally, xSiGra integrates a novel variant of gradient-weighted class activation mapping component to uncover interpretable features, including pivotal genes and cells for various cell types, thereby facilitating deeper biological insights from spatial data. Through rigorous benchmarking against existing methods, xSiGra demonstrates superior performance across diverse spatial imaging datasets. Application of xSiGra on a lung tumor slice unveils the importance score of cells, illustrating that cellular activity is not solely determined by itself but also impacted by neighboring cells. Moreover, leveraging the identified interpretable genes, xSiGra reveals endothelial cell subset interacting with tumor cells, indicating its heterogeneous underlying mechanisms within complex cellular interactions.
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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 Socratic graph.