RNA序列
反褶积
电池类型
核糖核酸
计算生物学
计算机科学
基因表达
类型(生物学)
可靠性(半导体)
细胞
差速器(机械装置)
表达式(计算机科学)
基因
生物
算法
转录组
遗传学
物理
程序设计语言
热力学
功率(物理)
量子力学
生态学
作者
Wenjing Ma,Sumeet K. Sharma,Peng Jin,Shannon L. Gourley,Zhaohui Qin
摘要
Given most tissues are consist of abundant and diverse (sub-)cell types, an important yet unaddressed problem in bulk RNA-seq analysis is to identify at which (sub-)cell type(s) the differential expression occurs. Single-cell RNA-sequencing (scRNA-seq) technologies can answer the question, but they are often labor-intensive and cost-prohibitive. Here, we present LRcell, a computational method aiming to identify specific (sub-)cell type(s) that drives the changes observed in a bulk RNA-seq experiment. In addition, LRcell provides pre-embedded marker genes computed from putative scRNA-seq experiments as options to execute the analyses. We conduct a simulation study to demonstrate the effectiveness and reliability of LRcell. Using three different real datasets, we show that LRcell successfully identifies known cell types involved in psychiatric disorders. Applying LRcell to bulk RNA-seq results can produce a hypothesis on which (sub-)cell type(s) contributes to the differential expression. LRcell is complementary to cell type deconvolution methods.
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