ArticleNPJ systems biology and applications2025
A joint analysis of single cell transcriptomics and proteomics using transformer.
Article in NPJ systems biology and applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- DGAT: a dual-graph attention network for inferring spatial protein landscapes from transcriptomics.Nature communications · 2026Article
- CAPTAIN: a multimodal foundation model pretrained on co-assayed single-cell RNA and protein.Nature communications · 2026Article
- CSIE: cancer subtyping via inference and ensemble.Briefings in bioinformatics · 2026Article
- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Immune cell annotation in the single-cell studies: technologies, challenges, and integrative solutions.Immunologic research · 2026Review
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5 authors.
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Abstract
CITE-seq provides a powerful method for simultaneously measuring RNA and protein expression at the single-cell level. The integrated analysis of RNA and protein expression in identical cells is crucial for revealing cellular heterogeneity. However, the high experimental costs associated with CITE-seq limit its widespread application. In this paper, we propose scTEL, a deep learning framework based on Transformer encoder layers, to establish a mapping from sequenced RNA expression to unobserved protein expression in the same cells. This computation-based approach significantly reduces the experimental costs of protein expression sequencing. We are now able to predict protein expression using single-cell RNA sequencing (scRNA-seq) data, which is well-established and available at a lower cost. Moreover, our scTEL model offers a unified framework for integrating multiple CITE-seq datasets, addressing the challenge posed by the partial overlap of protein panels across different datasets. Empirical validation on public CITE-seq datasets demonstrates scTEL significantly outperforms existing methods.
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