Profiling Cell Type Abundance and Expression in Bulk Tissues with CIBERSORTx

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...

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Bibliographic Details
Published inMethods in molecular biology (Clifton, N.J.) Vol. 2117; p. 135
Main Authors Steen, Chloé B, Liu, Chih Long, Alizadeh, Ash A, Newman, Aaron M
Format Journal Article
LanguageEnglish
Published United States 2020
Subjects
Case-Control Studies
Cell Line, Tumor
Cell Separation
Computational Biology - methods
Gene Expression Profiling - methods
Gene Expression Regulation, Neoplastic
High-Throughput Nucleotide Sequencing
Humans
Machine Learning
Neoplasms - genetics
Organ Specificity
Single-Cell Analysis
Cellular heterogeneity
Deconvolution
scRNA-seq
Tumor microenvironment
Gene expression
Digital cytometry
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Summary: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.
ISSN:1940-6029
DOI:10.1007/978-1-0716-0301-7_7