从批量表达数据估计的细胞类型比例的统计推断
Statistical Inference of Cell-type Proportions Estimated from Bulk Expression Data
摘要：
有越来越多的兴趣，在细胞类型特异性分析与不同类型的细胞混合物的散装样品。在这种分析中，关键的第一步是准确估计批量样品中的细胞类型比例。虽然最近已经提出了许多方法，量化与估计的细胞类型比例相关的不确定性还没有得到很好的研究。缺乏对这些不确定性的考虑可能会导致下游分析中的遗漏或错误结果。在这篇文章中，我们介绍了一个灵活的统计去卷积框架，允许一个一般的和特定主题的协方差散装基因表达。在这个框架下，我们提出了一个去相关的约束最小二乘方法称为DECALS，估计细胞类型的比例，以及抽样分布的估计。仿真研究表明，DECALS可以准确地量化估计比例的不确定性，而其他方法失败。应用DECALS来分析ROSMAP和GTEx项目的死后脑样本的大量基因表达数据，我们表明，考虑到估计细胞类型比例的不确定性，可以更准确地识别不同受试者群体之间的细胞类型特异性差异表达基因和转录本，例如阿尔茨海默病患者和对照之间以及男性和女性之间。
英文摘要：
There is a growing interest in cell-type-specific analysis from bulk samples with a mixture of different cell types. A critical first step in such analyses is the accurate estimation of cell-type proportions in a bulk sample. Although many methods have been proposed recently, quantifying the uncertainties associated with the estimated cell-type proportions has not been well studied. Lack of consideration of these uncertainties can lead to missed or false findings in downstream analyses. In this article, we introduce a flexible statistical deconvolution framework that allows a general and subject-specific covariance of bulk gene expressions. Under this framework, we propose a decorrelated constrained least squares method called DECALS that estimates cell-type proportions as well as the sampling distribution of the estimates. Simulation studies demonstrate that DECALS can accurately quantify the uncertainties in the estimated proportions whereas other methods fail. Applying DECALS to analyze bulk gene expression data of post mortem brain samples from the ROSMAP and GTEx projects, we show that taking into account the uncertainties in the estimated cell-type proportions can lead to more accurate identifications of cell-type-specific differentially expressed genes and transcripts between different subject groups, such as between Alzheimer's disease patients and controls and between males and females.
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