In IOBR/IOBR: Immune Oncology Biological Research

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IOBR: Immuno-Oncology Biological Research

IOBR is an R package to perform comprehensive analysis of tumor microenvironment and signatures for immuno-oncology.

1.Introduction

• 1. IOBR collects 322 published signature gene sets, involving tumor microenvironment, tumor metabolism, m6A, exosomes, microsatellite instability, and tertiary lymphoid structure. Running the function signature_collection_citation to attain the source papers. The function signature_collection returns the detail signature genes of all given signatures.
• 1. IOBR integrates 8 published methodologies decoding tumor microenvironment (TME) contexture: CIBERSORT, TIMER, xCell, MCPcounter, ESITMATE, EPIC, IPS, quanTIseq;
• 1. IOBR adopts three computational methods to calculate the signature score, comprising PCA,z-score, and ssGSEA;
• 1. IOBR integrates multiple approaches for variable transition, visualization, batch survival analysis, feature selection, and statistical analysis.
• 1. IOBR also integrates methods for batch visualization of subgroup characteristics.

IOBR package workflow

2.Installation

It is essential that you have R 3.6.3 or above already installed on your computer or server. IOBR utilizes many other R packages that are currently available from CRAN, Bioconductor and GitHub. Before installing IOBR, please install all dependencies by executing the following command in R console:

The dependencies includs tibble, survival, survminer, limma, limSolve, GSVA, e1071, preprocessCore, ggplot2 and ggpubr.

# options("repos"= c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
# options(BioC_mirror="http://mirrors.tuna.tsinghua.edu.cn/bioconductor/")
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")

depens<-c('tibble', 'survival', 'survminer', 'limma', "DESeq2","devtools", 'limSolve', 'GSVA', 'e1071', 'preprocessCore', 
          "devtools", "tidyHeatmap", "caret", "glmnet", "ppcor",  "timeROC", "pracma", "factoextra", 
          "FactoMineR", "WGCNA", "patchwork", 'ggplot2', "biomaRt", 'ggpubr', 'ComplexHeatmap')
for(i in 1:length(depens)){
  depen<-depens[i]
  if (!requireNamespace(depen, quietly = TRUE))  BiocManager::install(depen,update = FALSE)
}

The package is not yet on CRAN or Bioconductor. You can install it from Github:

if (!requireNamespace("IOBR", quietly = TRUE))
  devtools::install_github("IOBR/IOBR")

Library R packages

library(IOBR) 

3.Manual

IOBR pipeline diagram below outlines the data processing flow of this package, and detailed guidance of how to use IOBR could be found in the IOBR book.

3.Availabie methods to decode TME contexture

tme_deconvolution_methods
# Return available parameter options of TME deconvolution.

If you use this package in your work, please cite both our package and the method(s) you are using.

Licenses of the deconvolution methods

| method | license | citation | |--------|---------|----------| | CIBERSORT | free for non-commerical use only | Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., … Alizadeh, A. A. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature Methods, 12(5), 453–457. https://doi.org/10.1038/nmeth.3337 | | ESTIMATE | free (GPL2.0) | Vegesna R, Kim H, Torres-Garcia W, ..., Verhaak R. (2013). Inferring tumour purity and stromal and immune cell admixture from expression data. Nature Communications 4, 2612. http://doi.org/10.1038/ncomms3612 | | quanTIseq | free (BSD) | Finotello, F., Mayer, C., Plattner, C., Laschober, G., Rieder, D., Hackl, H., ..., Sopper, S. (2019). Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome medicine, 11(1), 34. https://doi.org/10.1186/s13073-019-0638-6 | | TIMER | free (GPL 2.0) | Li, B., Severson, E., Pignon, J.-C., Zhao, H., Li, T., Novak, J., … Liu, X. S. (2016). Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biology, 17(1), 174. https://doi.org/10.1186/s13059-016-1028-7 | | IPS | free (BSD) | P. Charoentong et al., Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Reports 18, 248-262 (2017). https://doi.org/10.1016/j.celrep.2016.12.019 | | MCPCounter | free (GPL 3.0) | Becht, E., Giraldo, N. A., Lacroix, L., Buttard, B., Elarouci, N., Petitprez, F., … de Reyniès, A. (2016). Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biology, 17(1), 218. https://doi.org/10.1186/s13059-016-1070-5 | | xCell | free (GPL 3.0) | Aran, D., Hu, Z., & Butte, A. J. (2017). xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biology, 18(1), 220. https://doi.org/10.1186/s13059-017-1349-1 | | EPIC | free for non-commercial use only (Academic License) | Racle, J., de Jonge, K., Baumgaertner, P., Speiser, D. E., & Gfeller, D. (2017). Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. ELife, 6, e26476. https://doi.org/10.7554/eLife.26476 |

4.Availabie methods to estimate signatures

signature_score_calculation_methods
# Return available parameter options of signature estimation.

Licenses of the signature-esitmation method

| method | license | citation | |--------|---------|----------| | GSVA | free (GPL (>= 2)) | Hänzelmann S, Castelo R, Guinney J (2013). “GSVA: gene set variation analysis for microarray and RNA-Seq data.” BMC Bioinformatics, 14, 7. doi: 10.1186/1471-2105-14-7, http://www.biomedcentral.com/1471-2105/14/7 |

5.Signature collection

#References of collected signatures
signature_collection_citation[!duplicated(signature_collection_citation$Journal),]

#signature groups
sig_group[1:3]

References

Zeng D, Fang Y, …, Liao W (2024) IOBR2: Multidimensional Decoding of Tumor Microenvironment for Immuno-Oncology Research. bioRxiv, 2024.01.13.575484

Zeng D, Ye Z, Shen R, Yu G, Wu J, Xiong Y,..., Liao W (2021) IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures. Frontiers in Immunology. 12:687975. doi: 10.3389/fimmu.2021.687975

Reporting bugs

Please report bugs to the Github issues page

E-mail any questions to [e-mail] or [e-mail]

IOBR/IOBR documentation built on May 5, 2024, 2:34 p.m.