DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads
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 un...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61580-w |
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| author | Kailu Song Yumin Zheng Bowen Zhao David H. Eidelman Jian Tang Jun Ding |
| author_facet | Kailu Song Yumin Zheng Bowen Zhao David H. Eidelman Jian Tang Jun Ding |
| author_sort | Kailu Song |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3b6e719ce6e24e939f8aedfb86bb3cf0 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-3b6e719ce6e24e939f8aedfb86bb3cf02025-08-20T03:37:37ZengNature PortfolioNature Communications2041-17232025-07-0116112610.1038/s41467-025-61580-wDOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction readsKailu Song0Yumin Zheng1Bowen Zhao2David H. Eidelman3Jian Tang4Jun Ding5Quantitative Life Sciences, McGill UniversityQuantitative Life Sciences, McGill UniversityMeakins-Christie Laboratories, Research Institute of the McGill University Health CentreMeakins-Christie Laboratories, Research Institute of the McGill University Health CentreHEC MontréalQuantitative Life Sciences, McGill UniversityAbstract 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.https://doi.org/10.1038/s41467-025-61580-w |
| spellingShingle | Kailu Song Yumin Zheng Bowen Zhao David H. Eidelman Jian Tang Jun Ding DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads Nature Communications |
| title | DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads |
| title_full | DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads |
| title_fullStr | DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads |
| title_full_unstemmed | DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads |
| title_short | DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads |
| title_sort | dolphin advances single cell transcriptomics beyond gene level by leveraging exon and junction reads |
| url | https://doi.org/10.1038/s41467-025-61580-w |
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