ArticleNature communications2021
Joint analysis of expression levels and histological images identifies genes associated with tissue morphology.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits.PLoS computational biology · 2020Pooled it
- Genome-scale molecular profiling from routine histopathology with a multimodal pathology foundation model.Research square · 2026Article
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Artificial intelligence and oral microbiome: Reshaping the diagnostic and therapeutic paradigm of OSCC.Clinical and translational medicine · 2026Review
- Compositional and interpretable representation of histology using AI foundation models and sparse autoencoders.bioRxiv : the preprint server for biology · 2026Article
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- HistoGWAS: an AI-enabled framework for automated genetic analysis of tissue phenotypes in histology cohorts.Genome biology · 2026Article
- The Evolution of Spatial Omics Technologies Introduces A Novel Avenue for Lung Cancer Research.Genomics, proteomics & bioinformatics · 2026Review
- FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis.Nature communications · 2026Article
- Advances in predicting omics profiles from imaging data.Briefings in bioinformatics · 2026Review
- SHEST: single-cell-level artificial intelligence from haematoxylin and eosin morphology for cell-type prediction and spatial transcriptomics reconstruction.Briefings in bioinformatics · 2026Article
- Machine-learning models based on histological images from healthy donors identify imageQTLs and predict chronological age.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Computer Vision Methods for Spatial Transcriptomics: A Survey.bioRxiv : the preprint server for biology · 2025Article
- Machine Learning Models Based on Histological Images from Healthy Donors Identify ImageQTLs and Predict Chronological Age.bioRxiv : the preprint server for biology · 2025Article
- A novel multislice framework for precision 3D spatial domain reconstruction and disease pathology analysis.Genome research · 2025Article
- Histology image analysis of 13 healthy tissues reveals molecular-histological correlations.Scientific reports · 2025Article
- Answering open questions in biology using spatial genomics and structured methods.BMC bioinformatics · 2024Review
- Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology.Nature biotechnology · 2024Article
- Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes.Annual review of biomedical data science · 2024Review
- Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types.Nature communications · 2024Article
Corrections and comments
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4 authors.
Funding
Abstract
Histopathological images are used to characterize complex phenotypes such as tumor stage. Our goal is to associate features of stained tissue images with high-dimensional genomic markers. We use convolutional autoencoders and sparse canonical correlation analysis (CCA) on paired histological images and bulk gene expression to identify subsets of genes whose expression levels in a tissue sample correlate with subsets of morphological features from the corresponding sample image. We apply our approach, ImageCCA, to two TCGA data sets, and find gene sets associated with the structure of the extracellular matrix and cell wall infrastructure, implicating uncharacterized genes in extracellular processes. We find sets of genes associated with specific cell types, including neuronal cells and cells of the immune system. We apply ImageCCA to the GTEx v6 data, and find image features that capture population variation in thyroid and in colon tissues associated with genetic variants (image morphology QTLs, or imQTLs), suggesting that genetic variation regulates population variation in tissue morphological traits.
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