IOBR is an R package to perform comprehensive analysis of tumor microenvironment and signatures for immuno-oncology.
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- 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_citationto attain the source papers. The functionsignature_collectionreturns the detail signature genes of all given signatures.
- IOBR collects 322 published signature gene sets, involving tumor
microenvironment, tumor metabolism, m6A, exosomes, microsatellite
instability, and tertiary lymphoid structure. Running the function
-
- IOBR integrates 8 published methodologies decoding tumor
microenvironment (TME) contexture:
CIBERSORT,TIMER,xCell,MCPcounter,ESITMATE,EPIC,IPS,quanTIseq;
- IOBR integrates 8 published methodologies decoding tumor
microenvironment (TME) contexture:
-
- IOBR adopts three computational methods to calculate the signature
score, comprising
PCA,z-score, andssGSEA;
- IOBR adopts three computational methods to calculate the signature
score, comprising
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- IOBR integrates multiple approaches for variable transition, visualization, batch survival analysis, feature selection, and statistical analysis.
-
- IOBR also integrates methods for batch visualization of subgroup characteristics.
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) 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.
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.
| 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 |
signature_score_calculation_methods
#> PCA ssGSEA z-score Integration
#> "pca" "ssgsea" "zscore" "integration"
# Return available parameter options of signature estimation.| 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 |
#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"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
Please report bugs to the Github issues page
E-mail any questions to [email protected] or [email protected]