ArticleGenome biology2024
iIMPACT: integrating image and molecular profiles for spatial transcriptomics analysis.
Article in Genome biology, 2024. 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.
- Translating multimodal foundation models into oncology: Toward a future where AI directs diagnosis and therapy.Genes & diseases · 2026Article
- Spatial Gene Set Enrichment Analysis with Applications to Spatially Resolved Transcriptomic Data.bioRxiv : the preprint server for biology · 2026Article
- DGAT: a dual-graph attention network for inferring spatial protein landscapes from transcriptomics.Nature communications · 2026Article
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Genomic Characterization of Lung Cancer in Never-Smokers Using Deep Learning.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2026Article
- Hist2Cell: Deciphering fine-grained cellular architectures from histology images.Cell genomics · 2026Article
- Image-based epigenetic profiling with deep learning and high-speed super-resolution microscopy.Epigenetics & chromatin · 2026Article
- A comparative study of statistical methods for identifying differentially expressed genes in spatial transcriptomics.PLoS computational biology · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A spatially informed matrix normal model for gene co-expression analysis in spatial transcriptomics studies.Nucleic acids research · 2025Article
- Computer Vision Methods for Spatial Transcriptomics: A Survey.bioRxiv : the preprint server for biology · 2025Article
- Mapping molecular landscapes in triple-negative breast cancer: insights from spatial transcriptomics.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- BISON: bi-clustering of spatial omics data with feature selection.Bioinformatics (Oxford, England) · 2025Article
- Article
- Single-cell technology grows up: Leveraging high-resolution omics approaches to understand neurodevelopmental disorders.Current opinion in neurobiology · 2025Review
- Translational approaches to the study of eosinophils in vasculitis.Rheumatology (Oxford, England) · 2025Review
- A comparative study of statistical methods for identifying differentially expressed genes in spatial transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- Enhancing spatial domain detection in spatial transcriptomics with EnSDD.Communications biology · 2024Article
- BayeSMART: Bayesian clustering of multi-sample spatially resolved transcriptomics data.Briefings in bioinformatics · 2024Article
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9 authors.
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Abstract
Current clustering analysis of spatial transcriptomics data primarily relies on molecular information and fails to fully exploit the morphological features present in histology images, leading to compromised accuracy and interpretability. To overcome these limitations, we have developed a multi-stage statistical method called iIMPACT. It identifies and defines histology-based spatial domains based on AI-reconstructed histology images and spatial context of gene expression measurements, and detects domain-specific differentially expressed genes. Through multiple case studies, we demonstrate iIMPACT outperforms existing methods in accuracy and interpretability and provides insights into the cellular spatial organization and landscape of functional genes within spatial transcriptomics data.
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