Tutorial: integrative computational analysis of bulk RNA-sequencing data to characterize tumor immunity using RIMA

Area of Science:

• Oncology
• Bioinformatics
• Immunology

Background:

• RNA-sequencing (RNA-seq) is a cost-effective method for tumor molecular profiling and immune characterization.
• Numerous computational tools exist for analyzing gene expression data in cancer immunology.
• Analyzing large-scale RNA-seq data demands significant bioinformatics expertise and computational resources.

Purpose of the Study:

• To provide an overview of computational analysis for tumor immune characterization using bulk RNA-seq data.
• To introduce essential computational tools relevant to cancer immunology and immunotherapy.
• To present the RNA-seq IMmune Analysis (RIMA) pipeline for streamlined analysis and a user-friendly guide.

Main Methods:

• Overview of computational analysis strategies for bulk RNA-seq in tumor immunology.
• Introduction to diverse computational tools for evaluating expression signatures, immune infiltration, immune repertoire, immunotherapy response prediction, neoantigen detection, and microbiome quantification.
• Description of the RIMA pipeline, integrating multiple tools for efficient RNA-seq analysis.

Main Results:

• The RIMA pipeline integrates various computational tools for comprehensive tumor immune characterization.
• A user-friendly GitBook guide with demos is available for analyzing RNA-seq data at sample and cohort levels.
• The tutorial aims to lower the barrier for researchers in utilizing RNA-seq for cancer immunology studies.

Conclusions:

• Computational analysis of bulk RNA-seq data is crucial for understanding tumor immunity.
• The RIMA pipeline and accompanying guide facilitate accessible and efficient immune characterization from RNA-seq data.
• This resource empowers researchers to leverage RNA-seq for advancing cancer immunology and immunotherapy research.