1
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Chelu A, Cartwright EJ, Dobrzynski H. Empowering artificial intelligence in characterizing the human primary pacemaker of the heart at single cell resolution. Sci Rep 2024; 14:14041. [PMID: 38890395 PMCID: PMC11189420 DOI: 10.1038/s41598-024-63542-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 05/29/2024] [Indexed: 06/20/2024] Open
Abstract
The sinus node (SN) serves as the primary pacemaker of the heart and is the first component of the cardiac conduction system. Due to its anatomical properties and sample scarcity, the cellular composition of the human SN has been historically challenging to study. Here, we employed a novel deep learning deconvolution method, namely Bulk2space, to characterise the cellular heterogeneity of the human SN using existing single-cell datasets of non-human species. As a proof of principle, we used Bulk2Space to profile the cells of the bulk human right atrium using publicly available mouse scRNA-Seq data as a reference. 18 human cell populations were identified, with cardiac myocytes being the most abundant. Each identified cell population correlated to its published experimental counterpart. Subsequently, we applied the deconvolution to the bulk transcriptome of the human SN and identified 11 cell populations, including a population of pacemaker cardiomyocytes expressing pacemaking ion channels (HCN1, HCN4, CACNA1D) and transcription factors (SHOX2 and TBX3). The connective tissue of the SN was characterised by adipocyte and fibroblast populations, as well as key immune cells. Our work unravelled the unique single cell composition of the human SN by leveraging the power of a novel machine learning method.
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Affiliation(s)
- Alexandru Chelu
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, UK.
| | - Elizabeth J Cartwright
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, UK
| | - Halina Dobrzynski
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, UK
- Department of Anatomy, Jagiellonian University Medical College, 31-008, Kraków, Poland
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2
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Fan Y, Li L, Sun S. Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq. Genome Biol 2024; 25:96. [PMID: 38622747 PMCID: PMC11020788 DOI: 10.1186/s13059-024-03237-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 04/03/2024] [Indexed: 04/17/2024] Open
Abstract
We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.
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Affiliation(s)
- Yue Fan
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
- Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region; NHC Key Laboratory of Environment and Endemic Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
- Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an, Shaanxi, 710061, People's Republic of China
| | - Lei Li
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
- Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region; NHC Key Laboratory of Environment and Endemic Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
| | - Shiquan Sun
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China.
- Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region; NHC Key Laboratory of Environment and Endemic Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China.
- Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an, Shaanxi, 710061, People's Republic of China.
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, Shaanxi, 710061, People's Republic of China.
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3
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Zheng Y, Schupp JC, Adams T, Clair G, Justet A, Ahangari F, Yan X, Hansen P, Carlon M, Cortesi E, Vermant M, Vos R, De Sadeleer LJ, Rosas IO, Pineda R, Sembrat J, Königshoff M, McDonough JE, Vanaudenaerde BM, Wuyts WA, Kaminski N, Ding J. Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases. RESEARCH SQUARE 2023:rs.3.rs-3676579. [PMID: 38196613 PMCID: PMC10775382 DOI: 10.21203/rs.3.rs-3676579/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. Here, we introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modeling and discovery. When applied to a dataset from patients with Idiopathic Pulmonary Fibrosis (IPF), UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation via proteomics reveals the accuracy of UNAGI's cellular dynamics analyses, and the use of the Fibrotic Cocktail treated human Precision-cut Lung Slices confirms UNAGI's predictions that Nifedipine, an antihypertensive drug, may have antifibrotic effects on human tissues. UNAGI's versatility extends to other diseases, including a COVID dataset, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond IPF, amplifying its utility in the quest for therapeutic solutions across diverse pathological landscapes.
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Affiliation(s)
- Yumin Zheng
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, Canada
- Meakins-Christie Laboratories, Translational Research in Respiratory Diseases Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Jonas C. Schupp
- Pulmonary, Critical Care and Sleep Medicine, Yale University, School of Medicine, New Haven, CT, United States
| | - Taylor Adams
- Pulmonary, Critical Care and Sleep Medicine, Yale University, School of Medicine, New Haven, CT, United States
| | - Geremy Clair
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Aurelien Justet
- Pulmonary, Critical Care and Sleep Medicine, Yale University, School of Medicine, New Haven, CT, United States
| | - Farida Ahangari
- Pulmonary, Critical Care and Sleep Medicine, Yale University, School of Medicine, New Haven, CT, United States
| | - Xiting Yan
- Pulmonary, Critical Care and Sleep Medicine, Yale University, School of Medicine, New Haven, CT, United States
| | - Paul Hansen
- Meakins-Christie Laboratories, Translational Research in Respiratory Diseases Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Marianne Carlon
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Belgium
| | - Emanuela Cortesi
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Belgium
| | - Marie Vermant
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Belgium
| | - Robin Vos
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Belgium
| | - Laurens J. De Sadeleer
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Belgium
| | - Ivan O Rosas
- Division of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Ricardo Pineda
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - John Sembrat
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Melanie Königshoff
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - John E. McDonough
- Pulmonary, Critical Care and Sleep Medicine, Yale University, School of Medicine, New Haven, CT, United States
| | - Bart M. Vanaudenaerde
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Belgium
| | - Wim A. Wuyts
- Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Belgium
| | - Naftali Kaminski
- Pulmonary, Critical Care and Sleep Medicine, Yale University, School of Medicine, New Haven, CT, United States
| | - Jun Ding
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, Canada
- Meakins-Christie Laboratories, Translational Research in Respiratory Diseases Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
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4
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Liao J, Qian J, Fang Y, Chen Z, Zhuang X, Zhang N, Shao X, Hu Y, Yang P, Cheng J, Hu Y, Yu L, Yang H, Zhang J, Lu X, Shao L, Wu D, Gao Y, Chen H, Fan X. De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution. Nat Commun 2022; 13:6498. [PMID: 36310179 PMCID: PMC9618574 DOI: 10.1038/s41467-022-34271-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 10/19/2022] [Indexed: 12/25/2022] Open
Abstract
Uncovering the tissue molecular architecture at single-cell resolution could help better understand organisms' biological and pathological processes. However, bulk RNA-seq can only measure gene expression in cell mixtures, without revealing the transcriptional heterogeneity and spatial patterns of single cells. Herein, we introduce Bulk2Space ( https://github.com/ZJUFanLab/bulk2space ), a deep learning framework-based spatial deconvolution algorithm that can simultaneously disclose the spatial and cellular heterogeneity of bulk RNA-seq data using existing single-cell and spatial transcriptomics references. The use of bulk transcriptomics to validate Bulk2Space unveils, in particular, the spatial variance of immune cells in different tumor regions, the molecular and spatial heterogeneity of tissues during inflammation-induced tumorigenesis, and spatial patterns of novel genes in different cell types. Moreover, Bulk2Space is utilized to perform spatial deconvolution analysis on bulk transcriptome data from two different mouse brain regions derived from our in-house developed sequencing approach termed Spatial-seq. We have not only reconstructed the hierarchical structure of the mouse isocortex but also further annotated cell types that were not identified by original methods in the mouse hypothalamus.
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Affiliation(s)
- Jie Liao
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Jingyang Qian
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
| | - Yin Fang
- College of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China
- Hangzhou Innovation Center, Zhejiang University, 310058, Hangzhou, China
| | - Zhuo Chen
- College of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China
- Hangzhou Innovation Center, Zhejiang University, 310058, Hangzhou, China
| | - Xiang Zhuang
- College of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China
- Hangzhou Innovation Center, Zhejiang University, 310058, Hangzhou, China
| | - Ningyu Zhang
- College of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China
- Hangzhou Innovation Center, Zhejiang University, 310058, Hangzhou, China
| | - Xin Shao
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Yining Hu
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
| | - Penghui Yang
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
| | - Junyun Cheng
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Innovation Center in Zhejiang University, State Key Laboratory of Component-Based Chinese Medicine, 310058, Hangzhou, China
| | - Yang Hu
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Innovation Center in Zhejiang University, State Key Laboratory of Component-Based Chinese Medicine, 310058, Hangzhou, China
| | - Lingqi Yu
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
| | - Haihong Yang
- College of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China
- Hangzhou Innovation Center, Zhejiang University, 310058, Hangzhou, China
| | - Jinlu Zhang
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- College of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China
| | - Xiaoyan Lu
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Innovation Center in Zhejiang University, State Key Laboratory of Component-Based Chinese Medicine, 310058, Hangzhou, China
| | - Li Shao
- Institute of Translational Medicine, The Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, 310015, Hangzhou, China
| | - Dan Wu
- College of Biomedical Engineering and Instrument Science, Zhejiang University, 310013, Hangzhou, China
| | - Yue Gao
- Department of Pharmaceutical Sciences, Beijing Institute of Radiation Medicine, 100850, Beijing, China.
| | - Huajun Chen
- College of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China.
- Hangzhou Innovation Center, Zhejiang University, 310058, Hangzhou, China.
| | - Xiaohui Fan
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China.
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China.
- Innovation Center in Zhejiang University, State Key Laboratory of Component-Based Chinese Medicine, 310058, Hangzhou, China.
- Westlake Laboratory of Life Sciences and Biomedicine, 310024, Hangzhou, China.
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5
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Li A, Yang J, Qian J, Shao X, Liao J, Lu X, Fan X. Tracing the cell-type-specific modules of immune responses during COVID-19 progression using scDisProcema. Comput Struct Biotechnol J 2022; 20:3545-3555. [PMID: 35811838 PMCID: PMC9250167 DOI: 10.1016/j.csbj.2022.06.066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/30/2022] [Accepted: 06/30/2022] [Indexed: 12/16/2022] Open
Abstract
COVID-19 has caused severe threats to lives and damage to property worldwide. The immunopathology of the disease is of particular concern. Currently, researchers have used gene co-expression networks (GCNs) to deepen the study of molecular mechanisms of immune responses to COVID-19. However, most efforts have not fully explored dynamic changes of cell-type-specific molecular networks in the disease process. This study proposes a GCN construction pipeline named single-cell Disease Progression cellular module analysis (scDisProcema), which can trace dynamic changes of immune system response during disease progression using single-cell data. Here, scDisProcema considers changes in cell fate and expression patterns during disease development, identifying gene modules responsible for different immune cells. The hub genes are screened for each module by the specific expression level and the intercellular connectivity of modules. Based on functional items enriched by each gene module, we elucidate the biological processes of different cells involved in disease development and explain the molecular mechanisms underlying the process of cell depletion or proliferation caused by disease. Compared with traditional WGCNA methods, scDisProcema can make more convenient use of the heterogeneity information provided by scRNA-seq data and has great potential in exploring molecular changes during disease progression and organ development.
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Affiliation(s)
- Anyao Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314102, China
| | - Jihong Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Innovation Center in Zhejiang University, State Key Laboratory of Component-Based Chinese Medicine, Hangzhou 310058, China
- Zhang Boli Intelligent Health Innovation Lab, Hangzhou 311100, China
| | - Jingyang Qian
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314102, China
| | - Xin Shao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314102, China
| | - Jie Liao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314102, China
| | - Xiaoyan Lu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314102, China
- Innovation Center in Zhejiang University, State Key Laboratory of Component-Based Chinese Medicine, Hangzhou 310058, China
| | - Xiaohui Fan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314102, China
- Innovation Center in Zhejiang University, State Key Laboratory of Component-Based Chinese Medicine, Hangzhou 310058, China
- Corresponding author at: College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
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6
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Macnair W, Gupta R, Claassen M. psupertime: supervised pseudotime analysis for time-series single-cell RNA-seq data. Bioinformatics 2022; 38:i290-i298. [PMID: 35758781 PMCID: PMC9235474 DOI: 10.1093/bioinformatics/btac227] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Motivation Improvements in single-cell RNA-seq technologies mean that studies measuring multiple experimental conditions, such as time series, have become more common. At present, few computational methods exist to infer time series-specific transcriptome changes, and such studies have therefore typically used unsupervised pseudotime methods. While these methods identify cell subpopulations and the transitions between them, they are not appropriate for identifying the genes that vary coherently along the time series. In addition, the orderings they estimate are based only on the major sources of variation in the data, which may not correspond to the processes related to the time labels. Results We introduce psupertime, a supervised pseudotime approach based on a regression model, which explicitly uses time-series labels as input. It identifies genes that vary coherently along a time series, in addition to pseudotime values for individual cells, and a classifier that can be used to estimate labels for new data with unknown or differing labels. We show that psupertime outperforms benchmark classifiers in terms of identifying time-varying genes and provides better individual cell orderings than popular unsupervised pseudotime techniques. psupertime is applicable to any single-cell RNA-seq dataset with sequential labels (e.g. principally time series but also drug dosage and disease progression), derived from either experimental design and provides a fast, interpretable tool for targeted identification of genes varying along with specific biological processes. Availability and implementation R package available at github.com/wmacnair/psupertime and code for results reproduction at github.com/wmacnair/psupplementary. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Will Macnair
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich 8093, Switzerland
| | - Revant Gupta
- Inner Medicine I, Faculty of Medicine, University of Tübingen, University Hospital Tübingen, 72074, Germany
| | - Manfred Claassen
- Inner Medicine I, Faculty of Medicine, University of Tübingen, University Hospital Tübingen, 72074, Germany.,Department of Computer Science, University of Tübingen, Tübingen 72074, Germany
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7
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Shao X, Yang H, Zhuang X, Liao J, Yang P, Cheng J, Lu X, Chen H, Fan X. scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network. Nucleic Acids Res 2021; 49:e122. [PMID: 34500471 PMCID: PMC8643674 DOI: 10.1093/nar/gkab775] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/04/2021] [Accepted: 08/26/2021] [Indexed: 01/16/2023] Open
Abstract
Advances in single-cell RNA sequencing (scRNA-seq) have furthered the simultaneous classification of thousands of cells in a single assay based on transcriptome profiling. In most analysis protocols, single-cell type annotation relies on marker genes or RNA-seq profiles, resulting in poor extrapolation. Still, the accurate cell-type annotation for single-cell transcriptomic data remains a great challenge. Here, we introduce scDeepSort (https://github.com/ZJUFanLab/scDeepSort), a pre-trained cell-type annotation tool for single-cell transcriptomics that uses a deep learning model with a weighted graph neural network (GNN). Using human and mouse scRNA-seq data resources, we demonstrate the high performance and robustness of scDeepSort in labeling 764 741 cells involving 56 human and 32 mouse tissues. Significantly, scDeepSort outperformed other known methods in annotating 76 external test datasets, reaching an 83.79% accuracy across 265 489 cells in humans and mice. Moreover, we demonstrate the universality of scDeepSort using more challenging datasets and using references from different scRNA-seq technology. Above all, scDeepSort is the first attempt to annotate cell types of scRNA-seq data with a pre-trained GNN model, which can realize the accurate cell-type annotation without additional references, i.e. markers or RNA-seq profiles.
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Affiliation(s)
- Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,iMedicine Lab, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Zhejiang University, Hangzhou 310058, China
| | - Haihong Yang
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China.,Hangzhou Innovation Center, Zhejiang University, Hangzhou 310058, China
| | - Xiang Zhuang
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,iMedicine Lab, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Zhejiang University, Hangzhou 310058, China
| | - Penghui Yang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Junyun Cheng
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiaoyan Lu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Center in Zhejiang University, State Key Laboratory of Component-Based Chinese Medicine, Hangzhou 310058, China
| | - Huajun Chen
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China.,The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China.,Hangzhou Innovation Center, Zhejiang University, Hangzhou 310058, China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,iMedicine Lab, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Zhejiang University, Hangzhou 310058, China.,Innovation Center in Zhejiang University, State Key Laboratory of Component-Based Chinese Medicine, Hangzhou 310058, China.,Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310058, China
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