Chapter 1 Introduction

This is a tutorial about integrative computing analysis of tumor immunity using bulk RNA-sequencing (RNA-seq) data. We will focus on inferring immune infiltration, immune repertoire, immune response and neoantigen prediction from a gene expression profile.
We developed a RNA-seq immune analysis pipeline named RIMA that is available at https://github.com/liulab-dfci/RIMA/.

Tumor RNA-seq has become an important technique for molecular profiling and immune characterization of tumors. RNA-seq Immune Analysis (RIMA) performs integrative computational modeling of the tumor microenvironment from bulk tumor RNA-seq data, which has the potential to offer essential insights to cancer immunology and immune-oncology studies.

Flowchat of RIMA pipeline

Figure 1.1: Flowchat of RIMA pipeline

The pre-processing module includes four main procedures:
  • Read mapping
  • Quality control
  • Gene quantification
  • Batch effect removal
The downstream analysis includes seven modules:
  • Differential expression analysis
  • Immune repertoire inference
  • Immune infiltration estimation
  • Immunotherapy response prediction
  • Gene fusion
  • Microbiome characterization
  • Neoantigen detection

Available Tools Checklist

Methods Description
—PREPROCESSING—
STAR Spliced Transcript Alignment to a Reference
Salmon Gene Quantification
RSeQC High Throughput Sequence Data Evaluation
batch_removal Remove Batch Effects Using Limma
—DIFFERENTIAL EXPRESSION—
DESeq2 Gene Differential Expression Analysis
GSEA Gene Set Enrichment Analysis
ssGSEA Single-sample GSEA
—IMMUNE REPERTOIRE—
TRUST4 TCR and BCR Sequence Analysis
—IMMUNE INFILTRATION—
ImmuneDeconv Cell Components Estimation
—IMMNUE RESPONSE—
MSIsensor2 Microsatellite Instability (MSI) Detection
TIDEpy T cell dysfunction and exclusion prediction
—FUSION—
STAR-Fusion Identify the fusion gene pairs
—MICROBIOME—
Centrifuge Bacterial Abundance Detection
—NEO-ANTIGEN—
arcasHLA HLA Class I and Class II Genotyping