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Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve
We introduce UniCell: Deconvolve Base (UCDBase), a pre-trained, interpretable, deep learning model to deconvolve cell type fractions and predict cell identity across Spatial, bulk-RNA-Seq, and scRNA-Seq datasets without contextualized reference data. UCD is trained on 10 million pseudo-mixtures from...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10008582/ https://www.ncbi.nlm.nih.gov/pubmed/36906603 http://dx.doi.org/10.1038/s41467-023-36961-8 |
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author | Charytonowicz, Daniel Brody, Rachel Sebra, Robert |
author_facet | Charytonowicz, Daniel Brody, Rachel Sebra, Robert |
author_sort | Charytonowicz, Daniel |
collection | PubMed |
description | We introduce UniCell: Deconvolve Base (UCDBase), a pre-trained, interpretable, deep learning model to deconvolve cell type fractions and predict cell identity across Spatial, bulk-RNA-Seq, and scRNA-Seq datasets without contextualized reference data. UCD is trained on 10 million pseudo-mixtures from a fully-integrated scRNA-Seq training database comprising over 28 million annotated single cells spanning 840 unique cell types from 898 studies. We show that our UCDBase and transfer-learning models achieve comparable or superior performance on in-silico mixture deconvolution to existing, reference-based, state-of-the-art methods. Feature attribute analysis uncovers gene signatures associated with cell-type specific inflammatory-fibrotic responses in ischemic kidney injury, discerns cancer subtypes, and accurately deconvolves tumor microenvironments. UCD identifies pathologic changes in cell fractions among bulk-RNA-Seq data for several disease states. Applied to lung cancer scRNA-Seq data, UCD annotates and distinguishes normal from cancerous cells. Overall, UCD enhances transcriptomic data analysis, aiding in assessment of cellular and spatial context. |
format | Online Article Text |
id | pubmed-10008582 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100085822023-03-13 Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve Charytonowicz, Daniel Brody, Rachel Sebra, Robert Nat Commun Article We introduce UniCell: Deconvolve Base (UCDBase), a pre-trained, interpretable, deep learning model to deconvolve cell type fractions and predict cell identity across Spatial, bulk-RNA-Seq, and scRNA-Seq datasets without contextualized reference data. UCD is trained on 10 million pseudo-mixtures from a fully-integrated scRNA-Seq training database comprising over 28 million annotated single cells spanning 840 unique cell types from 898 studies. We show that our UCDBase and transfer-learning models achieve comparable or superior performance on in-silico mixture deconvolution to existing, reference-based, state-of-the-art methods. Feature attribute analysis uncovers gene signatures associated with cell-type specific inflammatory-fibrotic responses in ischemic kidney injury, discerns cancer subtypes, and accurately deconvolves tumor microenvironments. UCD identifies pathologic changes in cell fractions among bulk-RNA-Seq data for several disease states. Applied to lung cancer scRNA-Seq data, UCD annotates and distinguishes normal from cancerous cells. Overall, UCD enhances transcriptomic data analysis, aiding in assessment of cellular and spatial context. Nature Publishing Group UK 2023-03-11 /pmc/articles/PMC10008582/ /pubmed/36906603 http://dx.doi.org/10.1038/s41467-023-36961-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Charytonowicz, Daniel Brody, Rachel Sebra, Robert Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve |
title | Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve |
title_full | Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve |
title_fullStr | Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve |
title_full_unstemmed | Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve |
title_short | Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve |
title_sort | interpretable and context-free deconvolution of multi-scale whole transcriptomic data with unicell deconvolve |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10008582/ https://www.ncbi.nlm.nih.gov/pubmed/36906603 http://dx.doi.org/10.1038/s41467-023-36961-8 |
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