Surface Protein Imputation from Single Cell Transcriptomes by Deep Neural Networks

Overview

Journal Nat Commun

Specialty Biology

Date 2020 Feb 2

PMID 32005835

Citations 36

Authors

Zilu Zhou

Chengzhong Ye

Jingshu Wang

Nancy R Zhang

Affiliations

Soon will be listed here.

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.

Citing Articles

Benchmarking single-cell cross-omics imputation methods for surface protein expression.

Li C, Hong Y, Li B, Zhang X Genome Biol. 2025; 26(1):46.

PMID: 40038818 PMC: 11881419. DOI: 10.1186/s13059-025-03514-9.

A joint analysis of single cell transcriptomics and proteomics using transformer.

Chen Y, Fan X, Shi C, Shi Z, Wang C NPJ Syst Biol Appl. 2025; 11(1):1.

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SurfR: Riding the wave of RNA-seq data with a comprehensive bioconductor package to identify surface protein-coding genes.

Maurizio A, Tascini A, Morelli M Bioinform Adv. 2024; 5(1):vbae201.

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Proteome-wide association studies for blood lipids and comparison with transcriptome-wide association studies.

Zhang D, Gao B, Feng Q, Manichaikul A, Peloso G, Tracy R HGG Adv. 2024; 6(1):100383.

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Extrapolated cross-validation for randomized ensembles.

Du J, Patil P, Roeder K, Kuchibhotla A J Comput Graph Stat. 2024; 33(3):1061-1072.

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