Bulk tissue cell type deconvolution with multi-subject single-cell expression reference
Bulk tissue cell type deconvolution with multi-subject single-cell expression reference
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DOI：
10.1038/s41467-018-08023-x
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发表时间：
2019-01-22
影响因子：
16.6
通讯作者：
Li, Mingyao
中科院分区：
文献类型：
--
作者：
Wang, Xuran;Park, Jihwan;Li, Mingyao
了解疾病相关组织中的细胞类型组成是鉴定疾病细胞靶标的重要步骤。我们提出了MuSiC，一种利用单细胞RNA测序（RNA-seq）数据中细胞类型特异性基因表达来表征复杂组织中批量RNA-seq数据中细胞类型组成的方法。通过对显示跨受试者和跨细胞一致性的基因进行适当加权，MuSiC能够将细胞类型特异性基因表达信息从一个数据集转移到另一个数据集。当应用于人类、小鼠和大鼠的胰岛和整个肾脏表达数据时，MuSiC优于现有方法，特别是对于具有密切相关细胞类型的组织。MuSiC能够表征复杂组织的细胞异质性，以了解疾病机制。由于批量组织数据比单细胞RNA-seq更容易获得，MuSiC允许利用大量疾病相关的批量组织RNA-seq数据来阐明疾病中的细胞类型贡献。
Knowledge of cell type composition in disease relevant tissues is an important step towards the identification of cellular targets of disease. We present MuSiC, a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. By appropriate weighting of genes showing cross-subject and cross-cell consistency, MuSiC enables the transfer of cell type-specific gene expression information from one dataset to another. When applied to pancreatic islet and whole kidney expression data in human, mouse, and rats, MuSiC outperformed existing methods, especially for tissues with closely related cell types. MuSiC enables the characterization of cellular heterogeneity of complex tissues for understanding of disease mechanisms. As bulk tissue data are more easily accessible than single-cell RNA-seq, MuSiC allows the utilization of the vast amounts of disease relevant bulk tissue RNA-seq data for elucidating cell type contributions in disease.