Surface protein imputation from single cell transcriptomes by deep neural networks

Nat Commun. 2020 Jan 31;11(1):651. doi: 10.1038/s41467-020-14391-0.

Abstract

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.

Publication types

  • Evaluation Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cells / cytology
  • Cells / metabolism*
  • Gene Expression Profiling / methods*
  • Humans
  • Membrane Proteins / genetics*
  • Membrane Proteins / metabolism
  • Neural Networks, Computer
  • Sequence Analysis, RNA
  • Single-Cell Analysis / methods*
  • Transcriptome*

Substances

  • Membrane Proteins