MuSiC2: cell-type deconvolution for multi-condition bulk RNA-seq data

反褶积 核糖核酸 计算机科学 电池类型 水准点(测量) 细胞 类型(生物学) RNA序列 计算生物学 算法 生物系统 生物 基因 基因表达 转录组 遗传学 地理 生态学 大地测量学
作者
Jiaxin Fan,Yafei Lyu,Qihuang Zhang,Xuran Wang,Mingyao Li,Rui Xiao
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:23 (6) 被引量:3
标识
DOI:10.1093/bib/bbac430
摘要

Abstract Cell-type composition of intact bulk tissues can vary across samples. Deciphering cell-type composition and its changes during disease progression is an important step toward understanding disease pathogenesis. To infer cell-type composition, existing cell-type deconvolution methods for bulk RNA sequencing (RNA-seq) data often require matched single-cell RNA-seq (scRNA-seq) data, generated from samples with similar clinical conditions, as reference. However, due to the difficulty of obtaining scRNA-seq data in diseased samples, only limited scRNA-seq data in matched disease conditions are available. Using scRNA-seq reference to deconvolve bulk RNA-seq data from samples with different disease conditions may lead to a biased estimation of cell-type proportions. To overcome this limitation, we propose an iterative estimation procedure, MuSiC2, which is an extension of MuSiC, to perform deconvolution analysis of bulk RNA-seq data generated from samples with multiple clinical conditions where at least one condition is different from that of the scRNA-seq reference. Extensive benchmark evaluations indicated that MuSiC2 improved the accuracy of cell-type proportion estimates of bulk RNA-seq samples under different conditions as compared with the traditional MuSiC deconvolution. MuSiC2 was applied to two bulk RNA-seq datasets for deconvolution analysis, including one from human pancreatic islets and the other from human retina. We show that MuSiC2 improves current deconvolution methods and provides more accurate cell-type proportion estimates when the bulk and single-cell reference differ in clinical conditions. We believe the condition-specific cell-type composition estimates from MuSiC2 will facilitate the downstream analysis and help identify cellular targets of human diseases.

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