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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Bibliographic Details
Published in:Nature communications 2020-01, Vol.11 (1), p.651-651, Article 651
Main Authors: Zhou, Zilu, Ye, Chengzhong, Wang, Jingshu, Zhang, Nancy R.
Format: Article
Language:English
Subjects:
45/91
631/114/1305
631/114/2397
82/80
96/31
Artificial neural networks
Cell surface
Cells - cytology
Cells - metabolism
Gene expression
Gene Expression Profiling - methods
Gene sequencing
Humanities and Social Sciences
Humans
Learning
Markers
Membrane Proteins - genetics
Membrane Proteins - metabolism
multidisciplinary
Neural networks
Neural Networks, Computer
Proteins
Ribonucleic acid
RNA
Science
Science (multidisciplinary)
Sequence Analysis, RNA
Single-Cell Analysis - methods
Transcriptome
Transfer learning
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Summary: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. Cell-surface proteins serve as phenotypic cell markers and in many cases are more indicative of cellular function than the transcriptome. Here, the authors introduce a transfer learning framework to impute surface protein abundances from scRNA-seq data.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-020-14391-0