IOBR is a comprehensive R package designed for immuno-oncology research, providing a one-stop solution for tumor microenvironment (TME) deconvolution, signature analysis, and integrated visualization. It integrates multiple state-of-the-art algorithms and curated gene sets to facilitate in-depth analysis of tumor immunity.
- 322+ published signature gene sets covering TME, metabolism, m6A, exosomes, microsatellite instability, tertiary lymphoid structure, and more
- Easy access to signature genes and source citations
- Flexible signature management and customization
Integrates 8 cutting-edge TME decoding methodologies:
CIBERSORT- Cell-type identification by estimating relative subsets of RNA transcriptsTIMER- Tumor Immune Estimation ResourcexCell- Digital portrayal of tissue cellular heterogeneityMCPcounter- Estimation of immune and stromal cell populationsESTIMATE- Inference of tumor purity and stromal/immune cell admixtureEPIC- Enumeration of cancer and immune cell typesIPS- Immunophenoscore calculationquanTIseq- Quantification of tumor-infiltrating immune cells
Three robust computational methods for signature scoring:
PCA- Principal Component Analysisz-score- Standardized expression scoringssGSEA- Single-sample Gene Set Enrichment Analysis
- Data preprocessing and normalization
- Batch effect correction
- Feature selection and dimensionality reduction
- Survival analysis and risk modeling
- Statistical testing and visualization
- Batch survival analysis plots
- Subgroup characteristic visualization
- Correlation heatmaps and scatter plots
- Forest plots for biomarker validation
- Customizable themes and color palettes
- R version 3.6.0 or higher
- Bioconductor version 3.10 or higher
# Install BiocManager if not already installed
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
# Install IOBR from GitHub
BiocManager::install("IOBR/IOBR")# Install remotes if not already installed
if (!requireNamespace("remotes", quietly = TRUE)) {
install.packages("remotes")
}
# Install IOBR using a mirror for faster download
remotes::install_git("https://ghfast.top/https://github.com/IOBR/IOBR")library(IOBR)# List available TME deconvolution methods
tme_deconvolution_methods
# Perform TME deconvolution using multiple methods
# Assuming you have an expression set object 'eset'
tme_result <- deconvo_tme(eset,
methods = c("cibersort", "timer", "xcell"),
output_format = "data.frame")# List available signature score calculation methods
signature_score_calculation_methods
# Calculate signature scores using ssGSEA
# Assuming you have an expression set object 'eset' and signature list 'sig_list'
sig_scores <- sigScore(eset,
signature = sig_list,
method = "ssgsea")For detailed tutorials and case studies, please refer to the IOBR Book, which provides comprehensive guidance on:
- Installation and setup
- Data preprocessing
- TME deconvolution
- Signature analysis
- Survival analysis
- Visualization techniques
Vignettes are available within the package and can be accessed using:
browseVignettes("IOBR")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"signature_score_calculation_methods
#> PCA ssGSEA z-score Integration
#> "pca" "ssgsea" "zscore" "integration"data("signature_collection")
# Number of available signatures
length(signature_collection)
#> [1] 323
head(signature_collection)
#> $CD_8_T_effector
#> [1] "CD8A" "GZMA" "GZMB" "IFNG" "CXCL9" "CXCL10" "PRF1" "TBX21"
#>
#> $DDR
#> [1] "UNG" "SMUG1" "MBD4" "OGG1" "MUTYH" "NTHL1" "MPG"
#> [8] "NEIL1" "NEIL2" "NEIL3" "APEX1" "APEX2" "LIG3" "XRCC1"
#> [15] "PNKP" "APLF" "PARP1" "PARP2" "PARP3" "MGMT" "ALKBH2"
#> [22] "ALKBH3" "TDP1" "TDP2" "MSH2" "MSH3" "MSH6" "MLH1"
#> [29] "PMS2" "MSH4" "MSH5" "MLH3" "PMS1" "XPC" "RAD23B"
#> [36] "CETN2" "RAD23A" "XPA" "DDB1" "DDB2" "RPA1" "RPA2"
#> [43] "RPA3" "ERCC3" "ERCC2" "GTF2H1" "GTF2H2" "GTF2H3" "GTF2H4"
#> [50] "GTF2H5" "CDK7" "CCNH" "MNAT1" "ERCC5" "ERCC1" "ERCC4"
#> [57] "LIG1" "ERCC8" "ERCC6" "UVSSA" "XAB2" "MMS19" "RAD51"
#> [64] "RAD51B" "RAD51D" "DMC1" "XRCC2" "XRCC3" "RAD52" "RAD54L"
#> [71] "RAD54B" "BRCA1" "SHFM1" "RAD50" "MRE11A" "NBN" "RBBP8"
#> [78] "MUS81" "EME1" "EME2" "GEN1" "FANCA" "FANCB" "FANCC"
#> [85] "BRCA2" "FANCD2" "FANCE" "FANCF" "FANCG" "FANCI" "BRIP1"
#> [92] "FANCL" "FANCM" "PALB2" "RAD51C" "XRCC6" "XRCC5" "PRKDC"
#> [99] "LIG4" "XRCC4" "DCLRE1C" "NHEJ1" "NUDT1" "DUT" "RRM2B"
#> [106] "POLB" "POLG" "POLD1" "POLE" "PCNA" "REV3L" "MAD2L2"
#> [113] "POLH" "POLI" "POLQ" "POLK" "POLL" "POLM" "POLN"
#> [120] "FEN1" "FAN1" "TREX1" "EXO1" "APTX" "ENDOV" "UBE2A"
#> [127] "UBE2B" "RAD18" "SHPRH" "HLTF" "RNF168" "SPRTN" "RNF8"
#> [134] "RNF4" "UBE2V2" "UBE2N" "H2AFX" "CHAF1A" "SETMAR" "BLM"
#> [141] "WRN" "RECQL4" "ATM" "DCLRE1A" "DCLRE1B" "RPA4" "PRPF19"
#> [148] "RECQL" "RECQL5" "HELQ" "RDM1" "ATR" "ATRIP" "MDC1"
#> [155] "RAD1" "RAD9A" "HUS1" "RAD17" "CHEK1" "CHEK2" "TP53"
#> [162] "TP53BP1" "RIF1" "TOPBP1" "CLK2" "PER1"
#>
#> $APM
#> [1] "B2M" "HLA-A" "HLA-B" "HLA-C" "TAP1" "TAP2"
#>
#> $Immune_Checkpoint
#> [1] "CD274" "PDCD1LG2" "CTLA4" "PDCD1" "LAG3" "HAVCR2" "TIGIT"
#>
#> $CellCycle_Reg
#> [1] "ATM" "CDKN1A" "CDKN2A" "MDM2" "TP53" "CCND1" "RB1" "CCNE1"
#> [9] "FBXW7" "E2F3"
#>
#> $Pan_F_TBRs
#> [1] "ACTA2" "ACTG2" "ADAM12" "ADAM19" "CNN1" "COL4A1"
#> [7] "CTGF" "CTPS1" "FAM101B" "FSTL3" "HSPB1" "IGFBP3"
#> [13] "PXDC1" "SEMA7A" "SH3PXD2A" "TAGLN" "TGFBI" "TNS1"
#> [19] "TPM1"
data("signature_collection_citation")
head(signature_collection_citation)
#> # A tibble: 6 × 6
#> Signatures `Published year` Journal Title PMID DOI
#> <chr> <dbl> <chr> <chr> <chr> <chr>
#> 1 CD_8_T_effector 2018 Nature TGFβ attenuates tumour… 2944… 10.1…
#> 2 DDR 2018 Nature TGFβ attenuates tumour… 2944… 10.1…
#> 3 APM 2018 Nature TGFβ attenuates tumour… 2944… 10.1…
#> 4 Immune_Checkpoint 2018 Nature TGFβ attenuates tumour… 2944… 10.1…
#> 5 CellCycle_Reg 2018 Nature TGFβ attenuates tumour… 2944… 10.1…
#> 6 Pan_F_TBRs 2018 Nature TGFβ attenuates tumour… 2944… 10.1…
data("sig_group")
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"| Method | License | Citation |
|---|---|---|
| CIBERSORT | Free for non-commercial use only | Newman, A. M., et al. (2015). Nature Methods, 12(5), 453–457. https://doi.org/10.1038/nmeth.3337 |
| ESTIMATE | Free (GPL2.0) | Vegesna R, et al. (2013). Nature Communications, 4, 2612. http://doi.org/10.1038/ncomms3612 |
| quanTIseq | Free (BSD) | Finotello, F., et al. (2019). Genome Medicine, 11(1), 34. https://doi.org/10.1186/s13073-019-0638-6 |
| TIMER | Free (GPL 2.0) | Li, B., et al. (2016). Genome Biology, 17(1), 174. https://doi.org/10.1186/s13059-016-1028-7 |
| IPS | Free (BSD) | Charoentong P, et al. (2017). Cell Reports, 18, 248-262. https://doi.org/10.1016/j.celrep.2016.12.019 |
| MCPCounter | Free (GPL 3.0) | Becht, E., et al. (2016). Genome Biology, 17(1), 218. https://doi.org/10.1186/s13059-016-1070-5 |
| xCell | Free (GPL 3.0) | Aran, D., et al. (2017). 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., et al. (2017). eLife, 6, e26476. https://doi.org/10.7554/eLife.26476 |
| Method | License | Citation |
|---|---|---|
| GSVA | Free (GPL (>= 2)) | Hänzelmann S, et al. (2013). BMC Bioinformatics, 14, 7. https://doi.org/10.1186/1471-2105-14-7 |
If you use IOBR in your research, please cite both the IOBR package and the specific methods you employ.
-
Zeng DQ, Fang YR, …, Liao WJ. Enhancing Immuno-Oncology Investigations Through Multidimensional Decoding of Tumour Microenvironment with IOBR 2.0, Cell Reports Methods, 2024 https://doi.org/10.1016/j.crmeth.2024.100910
-
Fang YR, …, Liao WJ, Zeng DQ, Systematic Investigation of Tumor Microenvironment and Antitumor Immunity With IOBR, Med Research, 2025 https://onlinelibrary.wiley.com/doi/epdf/10.1002/mdr2.70001
We welcome contributions to IOBR! If you’re interested in contributing, please:
- Fork the repository
- Create a feature branch
- Make your changes
- Submit a pull request
Please ensure your code follows the project’s coding standards and includes appropriate documentation and tests.
If you encounter any bugs or issues, please report them to the GitHub issues page. When reporting bugs, please include:
- A clear and descriptive title
- A detailed description of the issue
- Minimal reproducible example (if possible)
- Your R and IOBR versions
- Any error messages or warnings
For questions or inquiries, please contact:
- Dr. Deqiang Zeng: interlaken@smu.edu.cn
- Dr. Yunru Fang: fyr_nate@163.com
IOBR is released under the GNU General Public License v3.0.