README.md
In IOBR/IOBR: Immune Oncology Biological Research
IOBR: Immuno-Oncology Biological Research
IOBR is an R package to perform comprehensive analysis of tumor
microenvironment and signatures for immuno-oncology.
1.Introduction
-
- 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.
-
- IOBR integrates 8 published methodologies decoding tumor
microenvironment (TME) contexture:
CIBERSORT, TIMER, xCell,
MCPcounter, ESITMATE, EPIC, IPS, quanTIseq;
-
- IOBR adopts three computational methods to calculate the signature
score, comprising
PCA,z-score, and ssGSEA;
-
- 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.
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 interlaken@smu.edu.cn or fyr_nate@163.com
IOBR/IOBR documentation built on April 4, 2024, 1:07 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
IOBR: Immuno-Oncology Biological Research
IOBR is an R package to perform comprehensive analysis of tumor microenvironment and signatures for immuno-oncology.
1.Introduction
-
- 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
-
- 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.
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 interlaken@smu.edu.cn or fyr_nate@163.com
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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