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概率张量分解从单细胞多原子数据中提取出更好的潜伏嵌入

Ruo Han Wang¹, Jianping Wang¹, Shuai Cheng Li¹

¹Department of Computer Science, City University of Hong Kong, Hong Kong.

Nucleic acids research

|July 5, 2023

概括

新的框架SCOIT使用张量分解分析多原子单细胞数据. 它改善了细胞聚类和基因表达分析,优于剖析细胞异质性的现有方法.

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

单细胞测序产生了多原子数据,通常被表示为更高等级的张量.
现有的分析工具经常将张量数据减少到矩阵,失去特征对应.
这限制了复杂的单细胞多原子数据集的全面分析.

研究的目的:

介绍SCOIT,用于单细胞多原子数据分析的概率张量分解框架.
从复杂的数据集中提取有意义的细胞,基因和欧米嵌入.
加强下游分析,如细胞聚类,基因表达研究和数据归算.

主要方法:

开发了一个名为SCOIT的概率张量分解框架.
整合了高斯式,波松式和负二项式分布来处理数据稀疏性,噪声和异质性.
将框架应用于八个不同的单细胞多原子数据集.

主要成果:

与九种最先进的工具相比,SCOIT实现了优越的细胞聚类性能.
基因嵌入使得有效的跨欧米基因表达分析和监管网络研究成为可能.
交叉omics的归算性能显著改善 (3.38-39.26%的PCC增加),处理缺失的omic配置文件.

结论:

使用多原子单细胞数据,SCOIT有效地剖析细胞异质性.
该框架提供了强大的工具,用于整合性奥米克分析和归算.
SCOIT通过适应部分可用的omic配置文件的数据集来表现出灵活性.

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