Surface protein imputation from single cell transcriptomes by deep neural networks

While single cell RNA sequencing (scRNA-seq) is invaluable for studying cell populations, cell-surface proteins are often integral markers of cellular function and serve as primary targets for therapeutic intervention. Here we propose a transfer learning framework, single cell Transcriptome to Prote...

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Detalles Bibliográficos
Autores principales: Zhou, Zilu, Ye, Chengzhong, Wang, Jingshu, Zhang, Nancy R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6994606/
https://www.ncbi.nlm.nih.gov/pubmed/32005835
http://dx.doi.org/10.1038/s41467-020-14391-0
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author Zhou, Zilu
Ye, Chengzhong
Wang, Jingshu
Zhang, Nancy R.
author_facet Zhou, Zilu
Ye, Chengzhong
Wang, Jingshu
Zhang, Nancy R.
author_sort Zhou, Zilu
collection PubMed
description While single cell RNA sequencing (scRNA-seq) is invaluable for studying cell populations, cell-surface proteins are often integral markers of cellular function and serve as primary targets for therapeutic intervention. Here we propose a transfer learning framework, single cell Transcriptome to Protein prediction with deep neural network (cTP-net), to impute surface protein abundances from scRNA-seq data by learning from existing single-cell multi-omic resources.
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spelling pubmed-69946062020-02-03 Surface protein imputation from single cell transcriptomes by deep neural networks Zhou, Zilu Ye, Chengzhong Wang, Jingshu Zhang, Nancy R. Nat Commun Article While single cell RNA sequencing (scRNA-seq) is invaluable for studying cell populations, cell-surface proteins are often integral markers of cellular function and serve as primary targets for therapeutic intervention. Here we propose a transfer learning framework, single cell Transcriptome to Protein prediction with deep neural network (cTP-net), to impute surface protein abundances from scRNA-seq data by learning from existing single-cell multi-omic resources. Nature Publishing Group UK 2020-01-31 /pmc/articles/PMC6994606/ /pubmed/32005835 http://dx.doi.org/10.1038/s41467-020-14391-0 Text en © The Author(s) 2020 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/.
spellingShingle Article
Zhou, Zilu
Ye, Chengzhong
Wang, Jingshu
Zhang, Nancy R.
Surface protein imputation from single cell transcriptomes by deep neural networks
title Surface protein imputation from single cell transcriptomes by deep neural networks
title_full Surface protein imputation from single cell transcriptomes by deep neural networks
title_fullStr Surface protein imputation from single cell transcriptomes by deep neural networks
title_full_unstemmed Surface protein imputation from single cell transcriptomes by deep neural networks
title_short Surface protein imputation from single cell transcriptomes by deep neural networks
title_sort surface protein imputation from single cell transcriptomes by deep neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6994606/
https://www.ncbi.nlm.nih.gov/pubmed/32005835
http://dx.doi.org/10.1038/s41467-020-14391-0
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AT wangjingshu surfaceproteinimputationfromsinglecelltranscriptomesbydeepneuralnetworks
AT zhangnancyr surfaceproteinimputationfromsinglecelltranscriptomesbydeepneuralnetworks

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