MetaQ: fast, scalable and accurate metacell inference via single-cell quantization
Abstract To overcome the computational barriers of analyzing large-scale single-cell sequencing data, we introduce MetaQ, a metacell algorithm that scales to arbitrarily large datasets with linear runtime and constant memory usage. Inspired by cellular development, MetaQ conceptualizes each metacell...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56424-6 |
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| _version_ | 1832571492093657088 |
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| author | Yunfan Li Hancong Li Yijie Lin Dan Zhang Dezhong Peng Xiting Liu Jie Xie Peng Hu Lu Chen Han Luo Xi Peng |
| author_facet | Yunfan Li Hancong Li Yijie Lin Dan Zhang Dezhong Peng Xiting Liu Jie Xie Peng Hu Lu Chen Han Luo Xi Peng |
| author_sort | Yunfan Li |
| collection | DOAJ |
| description | Abstract To overcome the computational barriers of analyzing large-scale single-cell sequencing data, we introduce MetaQ, a metacell algorithm that scales to arbitrarily large datasets with linear runtime and constant memory usage. Inspired by cellular development, MetaQ conceptualizes each metacell as a collective ancestor of biologically similar cells. By quantizing cells into a discrete codebook, where each entry represents a metacell capable of reconstructing the original cells it quantizes, MetaQ identifies homogeneous cell subsets for efficient and accurate metacell inference. This approach reduces computational complexity from exponential to linear while maintaining or surpassing the performance of existing metacell algorithms. Extensive experiments demonstrate that MetaQ excels in downstream tasks such as cell type annotation, developmental trajectory inference, batch integration, and differential expression analysis. Thanks to its superior efficiency and effectiveness, MetaQ makes analyzing datasets with millions of cells practical, offering a powerful solution for single-cell studies in the era of high-throughput profiling. |
| format | Article |
| id | doaj-art-0688f7368c9b4f56abcbaba6e2882848 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-0688f7368c9b4f56abcbaba6e28828482025-02-02T12:32:43ZengNature PortfolioNature Communications2041-17232025-01-0116111810.1038/s41467-025-56424-6MetaQ: fast, scalable and accurate metacell inference via single-cell quantizationYunfan Li0Hancong Li1Yijie Lin2Dan Zhang3Dezhong Peng4Xiting Liu5Jie Xie6Peng Hu7Lu Chen8Han Luo9Xi Peng10School of Computer Science, Sichuan UniversityDepartment of Thyroid and Parathyroid Surgery, Laboratory of Thyroid and Parathyroid Disease, Frontiers Science Center for Disease Related Molecular Network, West China Hospital, Sichuan UniversitySchool of Computer Science, Sichuan UniversityDepartment of Laboratory Medicine, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan UniversitySchool of Computer Science, Sichuan UniversitySchool of Computer Science, Georgia Insitute of TechnologyCollege of Life Science, Sichuan Normal UniversitySchool of Computer Science, Sichuan UniversityDepartment of Laboratory Medicine, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan UniversityDepartment of Thyroid and Parathyroid Surgery, Laboratory of Thyroid and Parathyroid Disease, Frontiers Science Center for Disease Related Molecular Network, West China Hospital, Sichuan UniversitySchool of Computer Science, Sichuan UniversityAbstract To overcome the computational barriers of analyzing large-scale single-cell sequencing data, we introduce MetaQ, a metacell algorithm that scales to arbitrarily large datasets with linear runtime and constant memory usage. Inspired by cellular development, MetaQ conceptualizes each metacell as a collective ancestor of biologically similar cells. By quantizing cells into a discrete codebook, where each entry represents a metacell capable of reconstructing the original cells it quantizes, MetaQ identifies homogeneous cell subsets for efficient and accurate metacell inference. This approach reduces computational complexity from exponential to linear while maintaining or surpassing the performance of existing metacell algorithms. Extensive experiments demonstrate that MetaQ excels in downstream tasks such as cell type annotation, developmental trajectory inference, batch integration, and differential expression analysis. Thanks to its superior efficiency and effectiveness, MetaQ makes analyzing datasets with millions of cells practical, offering a powerful solution for single-cell studies in the era of high-throughput profiling.https://doi.org/10.1038/s41467-025-56424-6 |
| spellingShingle | Yunfan Li Hancong Li Yijie Lin Dan Zhang Dezhong Peng Xiting Liu Jie Xie Peng Hu Lu Chen Han Luo Xi Peng MetaQ: fast, scalable and accurate metacell inference via single-cell quantization Nature Communications |
| title | MetaQ: fast, scalable and accurate metacell inference via single-cell quantization |
| title_full | MetaQ: fast, scalable and accurate metacell inference via single-cell quantization |
| title_fullStr | MetaQ: fast, scalable and accurate metacell inference via single-cell quantization |
| title_full_unstemmed | MetaQ: fast, scalable and accurate metacell inference via single-cell quantization |
| title_short | MetaQ: fast, scalable and accurate metacell inference via single-cell quantization |
| title_sort | metaq fast scalable and accurate metacell inference via single cell quantization |
| url | https://doi.org/10.1038/s41467-025-56424-6 |
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