基于多主体单细胞表达参考的组织细胞类型去卷积。
Bulk tissue cell type deconvolution with multi-subject single-cell expression reference.
机构信息
Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Departments of Medicine and Genetics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
出版信息
Nat Commun. 2019 Jan 22;10(1):380. doi: 10.1038/s41467-018-08023-x.
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.
了解疾病相关组织中的细胞类型组成是鉴定疾病相关细胞靶标的重要步骤。我们提出了 MuSiC 方法，该方法利用单细胞 RNA 测序（RNA-seq）数据中的细胞类型特异性基因表达，从复杂组织的批量 RNA-seq 数据中描述细胞类型组成。通过对表现出跨主体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，尤其是对于具有密切相关细胞类型的组织。MuSiC 能够对复杂组织的细胞异质性进行特征描述，从而深入了解疾病机制。由于批量组织数据比单细胞 RNA-seq 更容易获得，因此 MuSiC 允许利用大量与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。
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