scPROTEIN : a versatile deep graph contrastive learning framework for single-cell proteomics embedding
© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc..
Single-cell proteomics sequencing technology sheds light on protein-protein interactions, posttranslational modifications and proteoform dynamics in the cell. However, the uncertainty estimation for peptide quantification, data missingness, batch effects and high noise hinder the analysis of single-cell proteomic data. It is important to solve this set of tangled problems together, but the existing methods tailored for single-cell transcriptomes cannot fully address this task. Here we propose a versatile framework designed for single-cell proteomics data analysis called scPROTEIN, which consists of peptide uncertainty estimation based on a multitask heteroscedastic regression model and cell embedding generation based on graph contrastive learning. scPROTEIN can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomic-specific embeddings in a unified framework. We demonstrate that scPROTEIN is efficient for cell clustering, batch correction, cell type annotation, clinical analysis and spatially resolved proteomic data exploration.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:21 |
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| Enthalten in: |
Nature methods - 21(2024), 4 vom: 20. Apr., Seite 623-634 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Wei [VerfasserIn] |
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Date Completed 15.04.2024 Date Revised 15.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s41592-024-02214-9 |
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| 520 | |a Single-cell proteomics sequencing technology sheds light on protein-protein interactions, posttranslational modifications and proteoform dynamics in the cell. However, the uncertainty estimation for peptide quantification, data missingness, batch effects and high noise hinder the analysis of single-cell proteomic data. It is important to solve this set of tangled problems together, but the existing methods tailored for single-cell transcriptomes cannot fully address this task. Here we propose a versatile framework designed for single-cell proteomics data analysis called scPROTEIN, which consists of peptide uncertainty estimation based on a multitask heteroscedastic regression model and cell embedding generation based on graph contrastive learning. scPROTEIN can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomic-specific embeddings in a unified framework. We demonstrate that scPROTEIN is efficient for cell clustering, batch correction, cell type annotation, clinical analysis and spatially resolved proteomic data exploration | ||
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| 700 | 1 | |a Liu, Linjing |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Bingzhe |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Han |e verfasserin |4 aut | |
| 700 | 1 | |a Yao, Jianhua |e verfasserin |4 aut | |
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