Multivariate stochastic modeling for transcriptional dynamics with cell-specific latent time using SDEvelo
Abstract Recently, RNA velocity has driven a paradigmatic change in single-cell RNA sequencing (scRNA-seq) studies, allowing the reconstruction and prediction of directed trajectories in cell differentiation and state transitions. Most existing methods of dynamic modeling use ordinary differential e...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55146-5 |
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| author | Xu Liao Lican Kang Yihao Peng Xiaoran Chai Peng Xie Chengqi Lin Hongkai Ji Yuling Jiao Jin Liu |
| author_facet | Xu Liao Lican Kang Yihao Peng Xiaoran Chai Peng Xie Chengqi Lin Hongkai Ji Yuling Jiao Jin Liu |
| author_sort | Xu Liao |
| collection | DOAJ |
| description | Abstract Recently, RNA velocity has driven a paradigmatic change in single-cell RNA sequencing (scRNA-seq) studies, allowing the reconstruction and prediction of directed trajectories in cell differentiation and state transitions. Most existing methods of dynamic modeling use ordinary differential equations (ODE) for individual genes without applying multivariate approaches. However, this modeling strategy inadequately captures the intrinsically stochastic nature of transcriptional dynamics governed by a cell-specific latent time across multiple genes, potentially leading to erroneous results. Here, we present SDEvelo, a generative approach to inferring RNA velocity by modeling the dynamics of unspliced and spliced RNAs via multivariate stochastic differential equations (SDE). Uniquely, SDEvelo explicitly models inherent uncertainty in transcriptional dynamics while estimating a cell-specific latent time across genes. Using both simulated and four scRNA-seq and spatial transcriptomics datasets, we show that SDEvelo can model the random dynamic patterns of mature-state cells while accurately detecting carcinogenesis. Additionally, the estimated gene-shared latent time can facilitate many downstream analyses for biological discovery. We demonstrate that SDEvelo is computationally scalable and applicable to both scRNA-seq and sequencing-based spatial transcriptomics data. |
| format | Article |
| id | doaj-art-2b960c2f1c404d08aeada0d4f5b0c01b |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-2b960c2f1c404d08aeada0d4f5b0c01b2025-01-05T12:34:57ZengNature PortfolioNature Communications2041-17232024-12-0115111610.1038/s41467-024-55146-5Multivariate stochastic modeling for transcriptional dynamics with cell-specific latent time using SDEveloXu Liao0Lican Kang1Yihao Peng2Xiaoran Chai3Peng Xie4Chengqi Lin5Hongkai Ji6Yuling Jiao7Jin Liu8School of Data Science, The Chinese University of Hong Kong-ShenzhenInstitute for Math and AI, Wuhan UniversitySchool of Data Science, The Chinese University of Hong Kong-ShenzhenCancer and Stem Cell Biology Program, Duke-NUS Medical SchoolSchool of Biological Science & Medical Engineering, Southeast UniversityKey Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast UniversityDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public HealthSchool of Artificial Intelligence, Wuhan UniversitySchool of Data Science, The Chinese University of Hong Kong-ShenzhenAbstract Recently, RNA velocity has driven a paradigmatic change in single-cell RNA sequencing (scRNA-seq) studies, allowing the reconstruction and prediction of directed trajectories in cell differentiation and state transitions. Most existing methods of dynamic modeling use ordinary differential equations (ODE) for individual genes without applying multivariate approaches. However, this modeling strategy inadequately captures the intrinsically stochastic nature of transcriptional dynamics governed by a cell-specific latent time across multiple genes, potentially leading to erroneous results. Here, we present SDEvelo, a generative approach to inferring RNA velocity by modeling the dynamics of unspliced and spliced RNAs via multivariate stochastic differential equations (SDE). Uniquely, SDEvelo explicitly models inherent uncertainty in transcriptional dynamics while estimating a cell-specific latent time across genes. Using both simulated and four scRNA-seq and spatial transcriptomics datasets, we show that SDEvelo can model the random dynamic patterns of mature-state cells while accurately detecting carcinogenesis. Additionally, the estimated gene-shared latent time can facilitate many downstream analyses for biological discovery. We demonstrate that SDEvelo is computationally scalable and applicable to both scRNA-seq and sequencing-based spatial transcriptomics data.https://doi.org/10.1038/s41467-024-55146-5 |
| spellingShingle | Xu Liao Lican Kang Yihao Peng Xiaoran Chai Peng Xie Chengqi Lin Hongkai Ji Yuling Jiao Jin Liu Multivariate stochastic modeling for transcriptional dynamics with cell-specific latent time using SDEvelo Nature Communications |
| title | Multivariate stochastic modeling for transcriptional dynamics with cell-specific latent time using SDEvelo |
| title_full | Multivariate stochastic modeling for transcriptional dynamics with cell-specific latent time using SDEvelo |
| title_fullStr | Multivariate stochastic modeling for transcriptional dynamics with cell-specific latent time using SDEvelo |
| title_full_unstemmed | Multivariate stochastic modeling for transcriptional dynamics with cell-specific latent time using SDEvelo |
| title_short | Multivariate stochastic modeling for transcriptional dynamics with cell-specific latent time using SDEvelo |
| title_sort | multivariate stochastic modeling for transcriptional dynamics with cell specific latent time using sdevelo |
| url | https://doi.org/10.1038/s41467-024-55146-5 |
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