Profiling Cell Type Abundance and Expression in Bulk Tissues with CIBERSORTx

Chloé B Steen; Chih Long Liu; Ash A Alizadeh; Aaron M Newman

doi:10.1007/978-1-0716-0301-7_7

Profiling Cell Type Abundance and Expression in Bulk Tissues with CIBERSORTx

Methods Mol Biol. 2020:2117:135-157. doi: 10.1007/978-1-0716-0301-7_7.

Authors

Chloé B Steen¹, Chih Long Liu^{1

2}, Ash A Alizadeh^{3

4

5

6}, Aaron M Newman^{7

8}

Affiliations

¹ Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
² Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
³ Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA. arasha@stanford.edu.
⁴ Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. arasha@stanford.edu.
⁵ Center for Cancer Systems Biology, Stanford University, Stanford, CA, USA. arasha@stanford.edu.
⁶ Division of Hematology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA. arasha@stanford.edu.
⁷ Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. amnewman@stanford.edu.
⁸ Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. amnewman@stanford.edu.

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.

Keywords: Cellular heterogeneity; Deconvolution; Digital cytometry; Gene expression; Tumor microenvironment; scRNA-seq.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

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

Abstract

Publication types

MeSH terms

Grants and funding