ArticleNature communications2020
Surface protein imputation from single cell transcriptomes by deep neural networks.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
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Who cites it
53 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- The dual-enhanced graph learning framework DePass allows paired data integration in single-cell and spatial multiomics.Nature cell biology · 2026Article
- DPAS-Graph: adaptive spatial-feature relation learning for spatial RNA-to-protein prediction and virtual protein profiling.Briefings in bioinformatics · 2026Article
- scDAU: a disentangled representation learning method for cross-modal translation in single-cell multi-omics data.Bioinformatics (Oxford, England) · 2026Article
- NicheTrans: spatial-aware cross-omics translation.Nature methods · 2026Article
- A pre-trained large generative model for translating single-cell transcriptomes to proteomes.Nature biomedical engineering · 2026Article
- Spatial multi-omics imputation and embedding with SpaMIE.Cell reports methods · 2026Article
- T2Pdecoder enables protein-centric analyses from transcriptomic data.Nature communications · 2026Article
- DGAT: a dual-graph attention network for inferring spatial protein landscapes from transcriptomics.Nature communications · 2026Article
- Immune cell annotation in the single-cell studies: technologies, challenges, and integrative solutions.Immunologic research · 2026Review
- Machine learning predictions surpass individual mRNAs as a proxy of single-cell protein expression.Genome biology · 2026Article
- Predictive modeling of molecular activity underlying physical cell-cell interactions.Cell reports methods · 2026Article
- ImmunoPheno: A Computational Framework for Data-Driven Design and Analysis of Immunophenotyping Experiments.bioRxiv : the preprint server for biology · 2026Article
- Single Cell RNA Sequencing in Colorectal Cancer Immunology: Recent Updates, Application, and Emerging Challenges.Current molecular medicine · 2026Review
- ADTnorm: robust integration of single-cell protein measurement across CITE-seq datasets.Nature communications · 2025Article
- Benchmarking single-cell cross-omics imputation methods for surface protein expression.Genome biology · 2025Article
- Proteome-wide association studies for blood lipids and comparison with transcriptome-wide association studies.HGG advances · 2025Article
- A joint analysis of single cell transcriptomics and proteomics using transformer.NPJ systems biology and applications · 2025Article
- SurfR: Riding the wave of RNA-seq data with a comprehensive bioconductor package to identify surface protein-coding genes.Bioinformatics advances · 2025Article
- DOGMA-seq and multimodal, single-cell analysis in acute myeloid leukemia.International review of cell and molecular biology · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.