使用 CIBERSORTx 分析批量组织中的细胞类型丰度和表达。
Profiling Cell Type Abundance and Expression in Bulk Tissues with CIBERSORTx.
机构信息
Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
出版信息
Methods Mol Biol. 2020;2117:135-157. doi: 10.1007/978-1-0716-0301-7_7.
Abstract
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.
摘要
CIBERSORTx 是一套用于从组织转录组图谱中评估细胞丰度和细胞类型特异性基因表达模式的机器学习工具。使用这个框架，可以使用单细胞或批量分选的 RNA 测序数据，从小样本生物标本中学习不同细胞类型的分子特征。然后，可以将这些特征重复应用于对无物理细胞分离的组织转录组进行细胞异质性分析。在本章中，我们提供了 CIBERSORTx 的详细指南，并展示了其在正常和肿瘤组织中进行高通量细胞类型和细胞状态分析的能力。
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