A Quantitative Computational Framework for Allopolyploid Single-Cell Data Integration and Core Gene Ranking in Development
文献类型：期刊论文
| 作者 | Wang, Meiyue; Li, Zijuan; Wang, Haoyu; Zhao, Junwei; Zhang, Yuyun; Lin, Kande; Zheng, Shusong; Feng, Yilong; Zhang, Yu'e; Teng, Wan |
| 刊名 | MOLECULAR BIOLOGY AND EVOLUTION
 |
| 出版日期 | 2024 |
| 卷号 | 41期号:9 |
| 英文摘要 | Polyploidization drives regulatory and phenotypic innovation. How the merger of different genomes contributes to polyploid development is a fundamental issue in evolutionary developmental biology and breeding research. Clarifying this issue is challenging because of genome complexity and the difficulty in tracking stochastic subgenome divergence during development. Recent single-cell sequencing techniques enabled probing subgenome-divergent regulation in the context of cellular differentiation. However, analyzing single-cell data suffers from high error rates due to high dimensionality, noise, and sparsity, and the errors stack up in polyploid analysis due to the increased dimensionality of comparisons between subgenomes of each cell, hindering deeper mechanistic understandings. In this study, we develop a quantitative computational framework, called pseudo-genome divergence quantification (pgDQ), for quantifying and tracking subgenome divergence directly at the cellular level. Further comparing with cellular differentiation trajectories derived from single-cell RNA sequencing data allows for an examination of the relationship between subgenome divergence and the progression of development. pgDQ produces robust results and is insensitive to data dropout and noise, avoiding high error rates due to multiple comparisons of genes, cells, and subgenomes. A statistical diagnostic approach is proposed to identify genes that are central to subgenome divergence during development, which facilitates the integration of different data modalities, enabling the identification of factors and pathways that mediate subgenome-divergent activity during development. Case studies have demonstrated that applying pgDQ to single-cell and bulk tissue transcriptomic data promotes a systematic and deeper understanding of how dynamic subgenome divergence contributes to developmental trajectories in polyploid evolution. |
| 源URL | [http://210.75.249.4/handle/363003/61947]  |
| 专题 | 西北高原生物研究所_中国科学院西北高原生物研究所 |
| 推荐引用方式 GB/T 7714 | Wang, Meiyue,Li, Zijuan,Wang, Haoyu,et al. A Quantitative Computational Framework for Allopolyploid Single-Cell Data Integration and Core Gene Ranking in Development[J]. MOLECULAR BIOLOGY AND EVOLUTION,2024,41(9). |
| APA | Wang, Meiyue.,Li, Zijuan.,Wang, Haoyu.,Zhao, Junwei.,Zhang, Yuyun.,...&Zhang, Yijing.(2024).A Quantitative Computational Framework for Allopolyploid Single-Cell Data Integration and Core Gene Ranking in Development.MOLECULAR BIOLOGY AND EVOLUTION,41(9). |
| MLA | Wang, Meiyue,et al."A Quantitative Computational Framework for Allopolyploid Single-Cell Data Integration and Core Gene Ranking in Development".MOLECULAR BIOLOGY AND EVOLUTION 41.9(2024). |
入库方式： OAI收割
来源：西北高原生物研究所
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