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GoM DE:解释序列计数数据中的结构与差异表达式分析,允许成员资格等级

Peter Carbonetto^1,2, Kaixuan Luo¹, Abhishek Sarkar^1,3

¹Department of Human Genetics, University of Chicago, Chicago, IL, USA.

|October 20, 2023

概括

在单细胞数据中解释基于部分的结构是具有挑战性的. 成员差异表达度 (GoM DE) 允许部分细胞加入组,改善单细胞RNA-seq和ATAC-seq数据集中的话题注释.

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:

基于部分的表示,如非负矩阵分解和主题建模,揭示了单细胞测序数据中的结构.
这些方法捕获的模式不容易通过传统的集群或缩小维度的技术识别.
解释这些已识别的"部分"或主题的生物学意义仍然是一个重大挑战.

研究的目的:

开发一种新的方法来增强单细胞数据中基于部分的表示的解释.
解决通过主题建模等方法识别的组件对生物意义进行注释的挑战.
引入会员级别差异表达式 (GoM DE) 为此目的的工具.

主要方法:

扩展现有的微分表达式分析方法.
实施一个成员资格 (GoM) 模型,允许细胞在多个生物群体中具有部分成员资格.
应用GoM DE方法来分析单细胞RNA-seq (scRNA-seq) 和单细胞ATAC-seq (scATAC-seq) 数据.

主要成果:

证明了GoM DE在注释来自单细胞测序数据的主题中的实用性.
展示了部分会员分配如何有助于理解已识别的组件的生物背景.
成功地将GoM DE应用于scRNA-seq和scATAC-seq数据集,突出其多功能性.

结论:

成员级差异表达 (GoM DE) 提供了一个强大的框架来解释单细胞数据中的基于部分的结构.
该方法通过允许细胞同时属于多个群体来促进更细微的生物学解释.
在复杂的单细胞数据集中,GoM DE对于注释主题和理解细胞异质性是一个宝贵的进步.

关键词:

微分表达式分析微分表达式分析缩小尺寸的缩小方式基因表达基因表达基因表达矩阵分解因子化基于部分的表示形式.一个单细胞ATAC-seqq.一个单细胞RNA-seqq.主题建模主题建模

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