Evaluation of deep convolutional neural networks for in situ hybridization gene expression image representation
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Le résumé fourni par la source
Abstract High resolution in situ hybridization (ISH) images of the brain capture spatial gene expression at cellular resolution. These spatial profiles are key to understanding brain organization at the molecular level. Previously, manual qualitative scoring and informatics pipelines have been applied to ISH images to determine expression intensity and pattern. To better capture the complex patterns of gene expression in the human cerebral cortex, we applied a machine learning approach. We propose gene re-identification as a contrastive learning task to compute representations of ISH images. We train our model on a ISH dataset of ~1,000 genes obtained from postmortem samples from 42 individuals. This model reaches a gene re-identification rate of 38.3%, a 13x improvement over random chance. We find that the learned embeddings predict expression intensity and pattern. To test generalization, we generated embeddings in a second dataset that assayed the expression of 78 genes in 53 individuals. In this set of images, 60.2% of genes are re-identified, suggesting the model is robust. Importantly, this dataset assayed expression in individuals diagnosed with schizophrenia. Gene and donor-specific embeddings from the model predict schizophrenia diagnosis at levels similar to that reached with demographic information. Mutations in the most discriminative gene, SCN4B , may help understand cardiovascular associations with schizophrenia and its treatment. We have publicly released our source code, embeddings, and models to spur further application to spatial transcriptomics. In summary, we propose and evaluate gene re-identification as a machine learning task to represent ISH gene expression images.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Evaluation of deep convolutional neural networks for in situ hybridization gene expression image representation
- Date Crossref
- 25/01/2021
- Éditeur
- openRxiv
- Type
- posted-content
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Centre for Addiction and Mental Health pays non établi dans la noticeÉtablissement de santé
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Hospital for Sick Children Mouse Imaging Centre pays non établi dans la noticeÉtablissement de santé
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University of Toronto Institute for Medical Science pays non établi dans la noticeUniversité ou école supérieure
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University of Oxford Wellcome Centre for Integrative Neuroimaging pays non établi dans la noticeUniversité ou école supérieure
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Wellcome Centre for Integrative Neuroimaging pays non établi dans la noticeStructure de recherche
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Krembil Centre for Neuroinformatics pays non établi dans la noticeInstitution
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Campbell Family Mental Health Research Institute pays non établi dans la noticeStructure de recherche
Centre for Addiction and Mental Health, Mouse Imaging Centre — Hospital for Sick Children et Institute for Medical Science — University of Toronto, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.