SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models.

SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models.
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DOI:
10.1186/s13059-023-03152-z
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发表时间:
2024-01-22
期刊:
影响因子:
12.3
通讯作者:
--
中科院分区:
生物学1区
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--
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基因表达的遗传调控是一个复杂的过程,已知遗传效应在细胞环境(如细胞类型和环境条件)中各不相同。我们开发了SURGE,这是一种从单细胞转录组数据中无监督发现上下文特异性表达数量性状基因座(eQTL)的方法。这允许在没有先验知识的情况下发现调节遗传调节的背景或细胞类型。应用于外周血单细胞eQTL数据,SURGE上下文捕获不同细胞类型和生物相关细胞类型分组的连续表示。我们使用共定位分析和分层LD评分回归证明了SURGE上下文特异性eQTL的疾病相关性。在线版本包含补充材料,可通过10.1186/s13059-023-03152-z获得。
Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression. The online version contains supplementary material available at 10.1186/s13059-023-03152-z.
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