Protocol to analyze immune cells in the tumor microenvironment by transcriptome using machine learning

Yunxi Liao; Ziyan Rao; Shaodong Huang; Dongyu Zhao

doi:10.1016/j.xpro.2023.102684

Protocol to analyze immune cells in the tumor microenvironment by transcriptome using machine learning

STAR Protoc. 2024 Mar 15;5(1):102684. doi: 10.1016/j.xpro.2023.102684. Epub 2024 Jan 14.

Authors

Yunxi Liao¹, Ziyan Rao¹, Shaodong Huang¹, Dongyu Zhao²

Affiliations

¹ Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing 100191, China.
² Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing 100191, China. Electronic address: zhaodongyu@bjmu.edu.cn.

Abstract

Immunotherapy is a promising strategy to treat cancer. Here, we present a protocol for analyzing the transcriptome-based phenotypic alterations and immune cell infiltration in the tumor microenvironment. We describe steps for integrating single-cell RNA sequencing (scRNA-seq) data, comparing phenotypes and origins of mononuclear phagocytes, inferring the differentiation trajectory and infiltration process, and identifying infiltration-associated genes using machine learning. We then detail procedures for exploring the impact of these genes in prognosis through the integrated microarray and bulk RNA-seq data to obtain potential drug targets. For complete details on the use and execution of this protocol, please refer to Liao et al.¹.

Keywords: Bioinformatics; Cancer; RNA-seq; Sequence Analysis; Single Cell.

MeSH terms

Cell Differentiation
Drug Delivery Systems
Machine Learning
Transcriptome* / genetics
Tumor Microenvironment* / genetics