# IOBR

**Repository Path**: joyeric_admin_admin/IOBR

## Basic Information

- **Project Name**: IOBR
- **Description**: 1. IOBR整合了10种已发表的大家比较认可的肿瘤微环境解析方法：CIBERSORT (fraction, absolute), TIMER, xCell, MCP-counter, ESTIMATE, EPIC, IPS, quanTIseq, lsei; 

2. IOBR收集了255个已经发表的signatures，包括肿瘤微环境相关的，代谢相关的，m6A, 外泌体，铁凋亡和错配修复等通路
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-05-22
- **Last Updated**: 2023-10-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


<!-- README.md is generated from README.Rmd. Please edit that file -->

# 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 255 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

<figure>
<img src="./man/figures/IOBR-Workflow.png" alt="IOBR logo" />
<figcaption aria-hidden="true">IOBR logo</figcaption>
</figure>

### 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`.

``` r
# 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:

``` r
if (!requireNamespace("IOBR", quietly = TRUE))
  devtools::install_github("IOBR/IOBR")
#> Warning: 程辑包'tidyHeatmap'是用R版本4.2.3 来建造的
```

Library R packages

``` r
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](https://iobr.github.io/book/).

<figure>
<img src="./man/figures/IOBR-Package.png" alt="IOBR logo" />
<figcaption aria-hidden="true">IOBR logo</figcaption>
</figure>

## 3.Availabie methods to decode TME contexture

``` r
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](https://cibersort.stanford.edu/)                                | 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](https://bioinformatics.mdanderson.org/public-software/estimate/) | free ([GPL2.0](https://bioinformatics.mdanderson.org/estimate/))                                              | 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](http://icbi.at/software/quantiseq/doc/index.html)               | free ([BSD](https://github.com/icbi-lab/immunedeconv/blob/master/LICENSE.md))                                 | 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](http://cistrome.org/TIMER/)                                         | free ([GPL 2.0](http://cistrome.org/TIMER/download.html))                                                     | 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](https://github.com/icbi-lab/Immunophenogram)                          | free ([BSD](https://github.com/icbi-lab/Immunophenogram/blob/master/LICENSE))                                 | 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](https://github.com/ebecht/MCPcounter)                          | free ([GPL 3.0](https://github.com/ebecht/MCPcounter/blob/master/Source/License))                             | 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](http://xcell.ucsf.edu/)                                             | free ([GPL 3.0](https://github.com/dviraran/xCell/blob/master/DESCRIPTION))                                   | 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](https://gfellerlab.shinyapps.io/EPIC_1-1/)                           | free for non-commercial use only ([Academic License](https://github.com/GfellerLab/EPIC/blob/master/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

``` r
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](http://www.bioconductor.org/packages/release/bioc/html/GSVA.html) | free ([GPL (\>= 2)](https://github.com/rcastelo/GSVA)) | 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

``` r
#References of collected signatures
signature_collection_citation[!duplicated(signature_collection_citation$Journal),]
#> # A tibble: 19 × 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 … <NA>  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…

#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, 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](https://www.frontiersin.org/articles/10.3389/fimmu.2021.687975/full)

## Reporting bugs

Please report bugs to the [Github issues
page](https://github.com/IOBR/IOBR/issues)

E-mail any questions to <dongqiangzeng0808@gmail.com>