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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:
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
Descripción
Sumario: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.