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DecOT: Bulk Deconvolution With Optimal Transport Loss Using a Single-Cell Reference

Tissues are constituted of heterogeneous cell types. Although single-cell RNA sequencing has paved the way to a deeper understanding of organismal cellular composition, the high cost and technical noise have prevented its wide application. As an alternative, computational deconvolution of bulk tissu...

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Detalles Bibliográficos
Autores principales: Liu, Gan, Liu, Xiuqin, Ma, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8855157/
https://www.ncbi.nlm.nih.gov/pubmed/35186040
http://dx.doi.org/10.3389/fgene.2022.825896
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author Liu, Gan
Liu, Xiuqin
Ma, Liang
author_facet Liu, Gan
Liu, Xiuqin
Ma, Liang
author_sort Liu, Gan
collection PubMed
description Tissues are constituted of heterogeneous cell types. Although single-cell RNA sequencing has paved the way to a deeper understanding of organismal cellular composition, the high cost and technical noise have prevented its wide application. As an alternative, computational deconvolution of bulk tissues can be a cost-effective solution. In this study, we propose DecOT, a deconvolution method that uses the Wasserstein distance as a loss and applies scRNA-seq data as references to characterize the cell type composition from bulk tissue RNA-seq data. The Wasserstein loss in DecOT is able to utilize additional information from gene space. DecOT also applies an ensemble framework to integrate deconvolution results from multiple individuals’ references to mitigate the individual/batch effect. By benchmarking DecOT with four recently proposed square loss-based methods on pseudo-bulk data from four different single-cell data sets and real pancreatic islet bulk samples, we show that DecOT outperforms other methods and the ensemble framework is robust to the choice of references.
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spelling pubmed-88551572022-02-19 DecOT: Bulk Deconvolution With Optimal Transport Loss Using a Single-Cell Reference Liu, Gan Liu, Xiuqin Ma, Liang Front Genet Genetics Tissues are constituted of heterogeneous cell types. Although single-cell RNA sequencing has paved the way to a deeper understanding of organismal cellular composition, the high cost and technical noise have prevented its wide application. As an alternative, computational deconvolution of bulk tissues can be a cost-effective solution. In this study, we propose DecOT, a deconvolution method that uses the Wasserstein distance as a loss and applies scRNA-seq data as references to characterize the cell type composition from bulk tissue RNA-seq data. The Wasserstein loss in DecOT is able to utilize additional information from gene space. DecOT also applies an ensemble framework to integrate deconvolution results from multiple individuals’ references to mitigate the individual/batch effect. By benchmarking DecOT with four recently proposed square loss-based methods on pseudo-bulk data from four different single-cell data sets and real pancreatic islet bulk samples, we show that DecOT outperforms other methods and the ensemble framework is robust to the choice of references. Frontiers Media S.A. 2022-02-04 /pmc/articles/PMC8855157/ /pubmed/35186040 http://dx.doi.org/10.3389/fgene.2022.825896 Text en Copyright © 2022 Liu, Liu and Ma. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Liu, Gan
Liu, Xiuqin
Ma, Liang
DecOT: Bulk Deconvolution With Optimal Transport Loss Using a Single-Cell Reference
title DecOT: Bulk Deconvolution With Optimal Transport Loss Using a Single-Cell Reference
title_full DecOT: Bulk Deconvolution With Optimal Transport Loss Using a Single-Cell Reference
title_fullStr DecOT: Bulk Deconvolution With Optimal Transport Loss Using a Single-Cell Reference
title_full_unstemmed DecOT: Bulk Deconvolution With Optimal Transport Loss Using a Single-Cell Reference
title_short DecOT: Bulk Deconvolution With Optimal Transport Loss Using a Single-Cell Reference
title_sort decot: bulk deconvolution with optimal transport loss using a single-cell reference
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8855157/
https://www.ncbi.nlm.nih.gov/pubmed/35186040
http://dx.doi.org/10.3389/fgene.2022.825896
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