Immune Oncology Biological Research R package IOBR version 2.2.0 Provides six modules for tumor microenvironment (TME) analysis based on multi-omics data. These modules cover data preprocessing, TME estimation, TME infiltrating patterns, cellular interactions, genome and TME interaction, and visualization for TME relevant features, as well as modelling based on key features. It integrates multiple microenvironmental analysis algorithms and signature estimation methods, simplifying the analysis and downstream visualization of the TME. In addition to providing a quick and easy way to construct gene signatures from single-cell RNA-seq data, it also provides a way to construct a reference matrix for TME deconvolution from single-cell RNA-seq data. The analysis pipeline and feature visualization are user-friendly and provide a comprehensive description of the complex TME, offering insights into tumour-immune interactions (Zeng D, et al. (2024) <doi:10.1016/j.crmeth.2024.100910>. Fang Y, et al. (2025) <doi:10.1002/mdr2.70001>). Software Depends: R (>= 3.6.0) Imports: cli, dplyr, ggplot2, glmnet, GSVA, methods, purrr, rlang, stringr, survival, survminer, tibble, tidyr Suggests: BiocParallel, biomaRt, circlize, clusterProfiler, ComplexHeatmap, corrplot, DESeq2, doParallel, DOSE, e1071, easier, enrichplot, factoextra, FactoMineR, foreach, ggdensity, ggpp, ggpubr, ggsci, gridExtra, Hmisc, knitr, limma, limSolve, maftools, MASS, Matrix, msigdbr, NbClust, org.Hs.eg.db, org.Mm.eg.db, patchwork, PMCMRplus, pracma, preprocessCore, prettydoc, pROC, psych, RColorBrewer, reshape2, rmarkdown, ROCR, sampling, scales, Seurat, SeuratObject, sva, testthat (>= 3.0.0), tidyHeatmap, timeROC, webr, WGCNA Shixiang Wang <w_shixiang@163.com> Comprehensive R Archive Network (CRAN) Dongqiang Zeng [aut], Yiran Fang [aut], Shixiang Wang [aut, cre] (ORCID: <https://orcid.org/0000-0001-9855-7357>), Qingcong Luo [aut], Hongqian Qian [aut] GPL-3 2026-04-22 application/tgz https://CRAN.R-project.org/package=IOBR doi:10.32614/CRAN.package.IOBR