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.
🎯 Key Features
1. Extensive Signature Collection
- 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
2. Multi-algorithm TME Deconvolution
Integrates 8 cutting-edge TME decoding methodologies:
-
CIBERSORT- Cell-type identification by estimating relative subsets of RNA transcripts -
TIMER- Tumor Immune Estimation Resource -
xCell- Digital portrayal of tissue cellular heterogeneity -
MCPcounter- Estimation of immune and stromal cell populations -
ESTIMATE- Inference of tumor purity and stromal/immune cell admixture -
EPIC- Enumeration of cancer and immune cell types -
IPS- Immunophenoscore calculation -
quanTIseq- Quantification of tumor-infiltrating immune cells
3. Signature Score Calculation
Three robust computational methods for signature scoring:
-
PCA- Principal Component Analysis -
z-score- Standardized expression scoring -
ssGSEA- Single-sample Gene Set Enrichment Analysis
⚠️ Important Note
To better integrate IOBR into the bioinformatics community, we have recently made extensive changes to optimize the code and enhance documentation. However, these improvements may introduce code instability and inconsistency. If you need to reproduce previous analysis results, please install and use the previously tagged versions from https://github.com/IOBR/IOBR/tags.
📦 Installation
Prerequisites
- 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 from CRAN
# Install IOBR from GitHub
BiocManager::install("IOBR")Note: install with
dependencies = TRUEis recommended to ensure all required packages are installed for full functionality.
BiocManager::install("IOBR", dependencies = TRUE)
Install from GitHub
# Install IOBR from GitHub
BiocManager::install("IOBR/IOBR")For Chinese Users (Faster Download)
# 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")🚀 Quick Start
TME Deconvolution
# 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")Signature Score Calculation
# 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")📖 Documentation
IOBR Book
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
Package Vignettes
Vignettes are available within the package and can be accessed using:
browseVignettes("IOBR")🛠️ Available Methods
TME Deconvolution Methods
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
signature_score_calculation_methods
#> PCA ssGSEA z-score Integration
#> "pca" "ssgsea" "zscore" "integration"Signature Collection
signature_collection <- load_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"
signature_collection_citation <- load_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…
sig_group <- load_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"📊 Licenses and Citations
TME Deconvolution Methods
Signature Estimation Methods
| 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 |
📝 Citing IOBR
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
🤝 Contributing
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.
🐛 Reporting Bugs
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
📧 Contact
For questions or inquiries, please contact:
- Dr. Deqiang Zeng: [email protected]
- Dr. Yiran Fang: [email protected]
📄 License
IOBR is released under the GNU General Public License v3.0.