Statistical inference for cell type deconvolution

Dongyue Xie

doi:10.1093/jrsssb/qkag054

2026-03-01

PRE

Dongyue Xie

Lin Gui

Jingshu Wang *

DOI：10.1093/jrsssb/qkag054

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摘要

En 中文

整合来自不同测量平台的不同类型数据集给统计推断带来了根本性挑战。一个重要例子是细胞类型解卷积，其中通过来自不同来源的参考单细胞数据估计批量RNA-seq数据中的细胞类型比例，导致平台特异性的缩放效应、测量噪声和生物异质性。现有方法通常将估计的比例视为下游分析中的观测值，在比较多个个体时可能损害其有效性。我们引入了测量误差调整的解卷积方法，这是一种用于解卷积中估计和推断的统计框架，其中设计矩阵采用外部近似方法。我们建立了在任意基因特异性的跨平台缩放差异下识别性的必要充分条件，并开发了针对个体水平比例和跨个体比较的有效推断程序，考虑了基因-基因相关性和共享估计不确定性。模拟和真实数据分析表明该方法具有竞争性的估计精度和可靠的统计推断能力。

Keyword:

error-in-variable models

single-cell sequencing

transfer learning

期刊

Journal of the Royal Statistical Society Series B-Statistical Methodology

IF:

3.6

论文数:

1.5K

被引数:

3.2W

机构

university of chicago

学者数:

4.5W

论文数: 3.7W

被引数: 80

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