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.
• 2. IOBR integrates 8 published methodologies decoding tumor microenvironment (TME) contexture: CIBERSORT, TIMER, xCell, MCPcounter, ESITMATE, EPIC, IPS, quanTIseq;
• 3. IOBR adopts three computational methods to calculate the signature score, comprising PCA,z-score, and ssGSEA;
• 4. IOBR integrates multiple approaches for variable transition, visualization, batch survival analysis, feature selection, and statistical analysis.
• 5. IOBR also integrates methods for batch visualization of subgroup characteristics.

IOBR package workflow

IOBR 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.

IOBR logo

3.Availabie methods to decode TME contexture

tme_deconvolution_methods
#>         MCPcounter               EPIC              xCell          CIBERSORT 
#>       "mcpcounter"             "epic"            "xcell"        "cibersort" 
#> CIBERSORT Absolute                IPS           ESTIMATE                SVR 
#>    "cibersort_abs"              "ips"         "estimate"              "svr" 
#>               lsei              TIMER          quanTIseq 
#>             "lsei"            "timer"        "quantiseq"
# 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
#>           PCA        ssGSEA       z-score   Integration 
#>         "pca"      "ssgsea"      "zscore" "integration"
# 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),]
#> # A tibble: 20 × 6
#>    Signatures                         `Published year` Journal Title PMID  DOI  
#>    <chr>                                         <dbl> <chr>   <chr> <chr> <chr>
#>  1 CD_8_T_effector                                2018 Nature  TGFβ… 2944… 10.1…
#>  2 TMEscoreA_CIR                                  2019 Cancer… Tumo… 3084… 10.1…
#>  3 CD8_Rooney_et_al                               2015 Cell    Mole… 2559… 10.1…
#>  4 T_cell_inflamed_GEP_Ayers_et_al                2017 The Jo… IFN-… 2865… 10.1…
#>  5 MDSC_Wang_et_al                                2016 Cancce… Targ… 2670… 10.1…
#>  6 B_cells_Danaher_et_al                          2017 Journa… Gene… 2823… 10.1…
#>  7 Nature_metabolism_Hypoxia                      2019 Nature… Char… 3198… 10.1…
#>  8 Winter_hypoxia_signature                       2007 Cancer… Rela… 1740… 10.1…
#>  9 Hu_hypoxia_signature                           2019 Molecu… The … 3044… 10.1…
#> 10 MT_exosome                                     2019 Molecu… An E… 3147… 10.1…
#> 11 SR_exosome                                     2017 Scient… Gene… 2838… 10.1…
#> 12 MC_Review_Exosome1                             2016 Molcul… Diag… 2718… 10.1…
#> 13 CMLS_Review_Exosome                            2018 Cellul… Curr… 2873… 10.1…
#> 14 Positive_regulation_of_exosomal_s…             2020 Gene O… http… <NA>  <NA> 
#> 15 Molecular_Cancer_m6A                           2020 Molecu… m6A … 3216… 10.1…
#> 16 Ferroptosis                                    2020 IOBR    Cons… <NA>  <NA> 
#> 17 T_cell_accumulation_Peng_et_al                 2018 Nature… Sign… 3012… 10.1…
#> 18 Antigen_Processing_and_Presentati…             2020 Nature… Pan-… 3208… 10.1…
#> 19 CD8_T_cells_Bindea_et_al                       2013 Immuni… Spat… 2413… 10.1…
#> 20 ecm_myCAF                                      2020 Cancer… Sing… 3243… 10.1…

#signature groups
sig_group[1:3]
#> $tumor_signature
#>  [1] "CellCycle_Reg"                            
#>  [2] "Cell_cycle"                               
#>  [3] "DDR"                                      
#>  [4] "Mismatch_Repair"                          
#>  [5] "Histones"                                 
#>  [6] "Homologous_recombination"                 
#>  [7] "Nature_metabolism_Hypoxia"                
#>  [8] "Molecular_Cancer_m6A"                     
#>  [9] "MT_exosome"                               
#> [10] "Positive_regulation_of_exosomal_secretion"
#> [11] "Ferroptosis"                              
#> [12] "EV_Cell_2020"                             
#> 
#> $EMT
#> [1] "Pan_F_TBRs" "EMT1"       "EMT2"       "EMT3"       "WNT_target"
#> 
#> $io_biomarkers
#>  [1] "TMEscore_CIR"                    "TMEscoreA_CIR"                  
#>  [3] "TMEscoreB_CIR"                   "T_cell_inflamed_GEP_Ayers_et_al"
#>  [5] "CD_8_T_effector"                 "IPS_IPS"                        
#>  [7] "Immune_Checkpoint"               "Exhausted_CD8_Danaher_et_al"    
#>  [9] "Pan_F_TBRs"                      "Mismatch_Repair"                
#> [11] "APM"

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 [email protected] or [email protected]