使用单细胞表达特征来估计基因表达的cis遗传性,可以控制eGene检测的错误阳性率
bioRxiv : the preprint server for biology
|March 10, 2025
概括
我们开发了scGeneHE,这是一种从单细胞RNA测序数据中估计基因表达遗传性的新方法. 这种方法准确地量化了特定细胞类型内的遗传调节,克服了以前的伪球体方法的局限性.
科学领域:
- 遗传学 遗传学 是一个
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 估计基因表达遗传性对于理解遗传调节和疾病关联至关重要.
- 标准方法需要聚合基因表达数据 (伪造),这可能会扭曲单细胞RNA测序 (scRNA-seq) 数据中细胞类型特定的遗传效应.
- 伪obulking导致遗传性的高估和膨胀的假阳性率识别cis-可遗传的基因.
研究的目的:
- 引入scGeneHE,这是一个新的统计方法,用于直接从scRNA-seq数据中的单个细胞配置文件中估计cis基因遗传性.
- 为了解决伪布尔克在基因表达特征遗传性估计中的局限性.
- 准确量化细胞类型特定的遗传调节.
主要方法:
- 开发了scGeneHE,这是一个Poisson混合效应模型,旨在分析原生单细胞表达数据.
- 通过模拟验证scGeneHE以评估其在假阳性率和遗传性估计准确性方面的表现.
- 将scGeneHE应用于一个大型scRNA-seq数据集 (OneK1K队列),包括969个个体和11种免疫细胞类型.
主要成果:
- 在模拟中,scGeneHE证明了在模拟中检测 cis-可遗传基因 (eGenes) 的精确校准的错误阳性率.
- 该方法在各种参数设置中提供了对cis-heritability的公正估计.
- 对OneK1K队列的分析揭示了免疫介导疾病风险基因的细胞类型特异性遗传调节以及细胞群体间的cis-heritability变异.
结论:
- scGeneHE有效地解决了从原生scRNA-seq数据中估计基因表达的cis-heritability的分析挑战.
- 开发的方法可以准确推断细胞类型特定的遗传结构.
- 这一进步有助于更深入地了解细胞水平疾病的遗传基础.
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