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Updated: May 24, 2025

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在开发时间序列中学习潜轨迹,使用隐藏的马尔科夫最佳运输
Peter Halmos1, Julian Gold2, Xinhao Liu1
1Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08544.
bioRxiv : the preprint server for biology
|March 3, 2025
概括
我们开发了Hidden-Markov最佳运输 (HM-OT),这是一个新的算法,用于在开发过程中映射细胞类型转换. HM-OT从单细胞测序数据中重建了分化途径,揭示了发育轨迹.
科学领域:
- 发展生物学 发展生物学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 了解细胞分化序列对于发育生物学至关重要.
- 单细胞和空间测序为发育研究提供数据.
- 推断差异化地图需要轨迹分析和细胞类型粗粒度.
研究的目的:
- 引入隐藏马尔科夫最佳运输 (HM-OT),一种用于在开发过程中学习细胞类型转换的算法.
- 同时将细胞分组为类型,并从时间序列转录组学数据中学习分化途径.
- 提供一种非监督或半监督的差异化地图学习方法.
主要方法:
- HM-OT使用低级最佳运输来对准时间序列样本.
- 该算法通过最大限度地降低运输总成本来学习集群和差异化图.
- 它假定隐藏细胞类型轨迹的马尔科夫属性.
主要成果:
- HM-OT成功地识别了细胞类型及其分化过渡.
- 该算法在斑马鱼发育数据 (立体-seq) 上得到验证.
- 在大规模的小鼠胚胎发育数据 (立体-seq) 上,HM-OT证明了可扩展性.
结论:
- HM-OT提供了一种可靠的方法来重建发育轨迹和差异化图.
- 该算法可以在无监督或半监督的环境中应用.
- HM-OT推进了用于发育研究的单细胞转录组学分析.
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