转录组
电池类型
细胞
丰度(生态学)
细胞生物学
计算生物学
核糖核酸
基因表达
化学
表达式(计算机科学)
生物
单细胞分析
仿形(计算机编程)
原位
细胞培养
基因表达谱
基因
遗传学
计算机科学
生态学
操作系统
作者
Chloé B. Steen,Chih Long Liu,Ash A. Alizadeh,Aaron M. Newman
出处
期刊:Methods in molecular biology
日期:2020-01-01
卷期号:: 135-157
被引量:226
标识
DOI:10.1007/978-1-0716-0301-7_7
摘要
CIBERSORTx is a suite of machine learning tools for the assessment of cellular abundance and cell type-specific gene expression patterns from bulk tissue transcriptome profiles. With this framework, single-cell or bulk-sorted RNA sequencing data can be used to learn molecular signatures of distinct cell types from a small collection of biospecimens. These signatures can then be repeatedly applied to characterize cellular heterogeneity from bulk tissue transcriptomes without physical cell isolation. In this chapter, we provide a detailed primer on CIBERSORTx and demonstrate its capabilities for high-throughput profiling of cell types and cellular states in normal and neoplastic tissues.
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