单细胞基因组学的联合轨迹推断使用深度学习与先前混合的混合
Jin-Hong Du1,2, Tianyu Chen3, Ming Gao4
1Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213.
概括
使用变异推断和自编码器的新方法VITAE准确推断细胞发育轨迹. 它提供了强大的不确定性量化,并集成了多个单细胞数据集,用于全面的谱系分析.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 发展生物学 发展生物学
背景情况:
- 单细胞测序数据分析依赖于轨迹推断来理解细胞分化和发育.
- 现有的轨迹推断工具往往缺乏强大的统计模型和不确定性量化.
研究的目的:
- 引入VITAE (Variational Inference for Trajectory by AutoEncoder),这是一个用于强大的细胞轨迹推断的新型统计框架.
- 通过层次混合模型和变异自动编码器,提高轨迹分析中的解释性和计算效率.
主要方法:
- 开发了VITAE,集成了一个潜伏的等级混合模型与变化自编码器用于轨迹推断.
- 实现了同时的轨迹推断和数据集成,以处理生物和技术异质性.
- 使用后方近似来量化细胞投影的不确定性.
主要成果:
- 与最先进的方法相比,VITAE在各种拓的合成和真实数据集上表现出更高的性能.
- 成功应用VITAE共同分析小鼠新皮质单细胞RNA测序数据,揭示了详细的投影神经元系.
- 展示了VITAE在减少批量效应和在数据集中发现更细微的细胞结构方面的有效性.
- 验证了VITAE在连续细胞群的综合性多原子分析中的实用性.
结论:
- 维泰为细胞轨迹推断和数据集成提供了统计严格和计算高效的方法.
- 该方法增强了对发育过程和血统层次的理解,特别是在复杂的生物系统中.
- 在分析单细胞和多原子数据方面,VITAE是有效的,提供更高的准确性和减少批量效应.
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