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从批量表达数据估计的细胞类型比例的统计推断

Biao Cai¹, Emma Jingfei Zhang², Hongyu Li³

¹Department of Management Sciences, City University of Hong Kong.

Journal of the American Statistical Association

|April 17, 2025

概括

在批量样本中准确估计细胞类型比例至关重要. 我们的新方法DECALS量化了不确定性,改进了细胞类型特定的基因表达分析,并确定了与疾病相关的差异.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
统计建模统计建模

背景情况:

批量样本的细胞类型特定分析越来越重要.
准确估计细胞类型比例是一个关键的先决条件.
这些估计中的量化不确定性仍未得到充分研究,可能会影响下游分析.

研究的目的:

引入一个统计解卷框架来估计细胞类型比例.
开发一种方法来量化与这些估计相关的不确定性.
为了提高细胞类型特异差异基因表达分析的准确性.

主要方法:

开发了一个灵活的统计解卷框架.
提出了一种非相关的受约束最小方程方法 (DECALS).
DECALS估计了细胞类型比例及其采样分布,允许对特定主体的协差.

主要成果:

模拟研究表明,DECALS与其他方法不同,准确量化估计不确定性.
在死后的大脑样本 (ROSMAP,GTEx) 上应用了DECALS.
考虑到不确定性,在疾病和性别比较中改善了细胞类型特定的差异表达基因/转录的识别.

结论:

DECALS提供了一种可靠的方法来估计细胞类型比例及其不确定性.
考虑不确定性可以提高细胞类型特异差异表达分析的准确性.
这种方法对于分析复杂的生物样本,如大脑组织是有价值的.

关键词:

细胞类型的解解.细胞类型的比例.细胞类型特定的分析分析.它们的关系是 decorrelation.不确定性量化不确定性量化

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