DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads
The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutiliz...
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| Vydáno v: | Nature communications Ročník 16; číslo 1; s. 6202 - 26 |
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| Médium: | Journal Article |
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Nature Publishing Group UK
04.07.2025
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2041-1723, 2041-1723 |
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| Abstract | The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets.
Single-cell RNA-seq analysis is conventionally limited to gene-level quantification, missing transcript diversity. Here, authors present DOLPHIN, a deep learning method that enables exon- and junction-level analysis to improve cell representation and detect alternative splicing. |
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| AbstractList | The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets. The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets.The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets. Abstract The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets. The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets. Single-cell RNA-seq analysis is conventionally limited to gene-level quantification, missing transcript diversity. Here, authors present DOLPHIN, a deep learning method that enables exon- and junction-level analysis to improve cell representation and detect alternative splicing. The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets. Single-cell RNA-seq analysis is conventionally limited to gene-level quantification, missing transcript diversity. Here, authors present DOLPHIN, a deep learning method that enables exon- and junction-level analysis to improve cell representation and detect alternative splicing. |
| ArticleNumber | 6202 |
| Author | Song, Kailu Zheng, Yumin Zhao, Bowen Tang, Jian Ding, Jun Eidelman, David H. |
| Author_xml | – sequence: 1 givenname: Kailu orcidid: 0009-0003-5326-7593 surname: Song fullname: Song, Kailu organization: Quantitative Life Sciences, McGill University, Meakins-Christie Laboratories, Research Institute of the McGill University Health Centre – sequence: 2 givenname: Yumin orcidid: 0009-0008-4580-5247 surname: Zheng fullname: Zheng, Yumin organization: Quantitative Life Sciences, McGill University, Meakins-Christie Laboratories, Research Institute of the McGill University Health Centre – sequence: 3 givenname: Bowen surname: Zhao fullname: Zhao, Bowen organization: Meakins-Christie Laboratories, Research Institute of the McGill University Health Centre, Division of Experimental Medicine, Department of Medicine, McGill University – sequence: 4 givenname: David H. surname: Eidelman fullname: Eidelman, David H. organization: Meakins-Christie Laboratories, Research Institute of the McGill University Health Centre, Division of Experimental Medicine, Department of Medicine, McGill University – sequence: 5 givenname: Jian surname: Tang fullname: Tang, Jian organization: HEC Montréal, Mila - Quebec AI Institute – sequence: 6 givenname: Jun orcidid: 0000-0001-5183-6885 surname: Ding fullname: Ding, Jun email: jun.ding@mcgill.ca organization: Quantitative Life Sciences, McGill University, Meakins-Christie Laboratories, Research Institute of the McGill University Health Centre, Division of Experimental Medicine, Department of Medicine, McGill University, Mila - Quebec AI Institute, School of Computer Science, McGill University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40615408$$D View this record in MEDLINE/PubMed |
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| Title | DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads |
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