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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures",
out.width = "100%"
)
```
# 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
### 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', 'sva', 'limma', "DESeq2","devtools",
'limSolve', 'GSVA', 'e1071', 'preprocessCore', 'ggplot2', "biomaRt",
'ggpubr', "devtools", "tidyHeatmap", "caret", "glmnet", "ppcor", "timeROC","pracma")
for(i in 1:length(depens)){
depen<-depens[i]
if (!requireNamespace(depen, quietly = TRUE))
BiocManager::install(depen)
}
if (!requireNamespace("EPIC", quietly = TRUE))
devtools::install_github("GfellerLab/EPIC", ref="master")
if (!requireNamespace("MCPcounter", quietly = TRUE))
devtools::install_github("ebecht/MCPcounter",ref="master", subdir="Source")
if (!requireNamespace("estimate", quietly = TRUE)){
rforge <- "http://r-forge.r-project.org"
install.packages("estimate", repos=rforge, dependencies=TRUE)
}
```
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",ref="master")
```
Library R packages
```{r,message = FALSE, warning=FALSE}
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 [GitHub vignette](https://github.com/IOBR/IOBR/tree/master/vignettes) or [HTML vignette](https://iobr.github.io/IOBR/IOBR-VIGNETTE.html).
## 3.Availabie methods to decode TME contexture
```{r}
tme_deconvolution_methods
# 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
# 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),]
#signature groups
sig_group[1:3]
```
References
---------
DQ Zeng, ZL Ye, RF Shen, GC Yu, …, WJ Liao*, IOBR: Multi-omics Immuno-Oncology Biological Research to decode tumor microenvironment and signatures. bioRxiv, 2020.2012.2014.422647 (2020).[doi: 10.1101/2020.12.14.422647](https://www.biorxiv.org/content/10.1101/2020.12.14.422647v1)
Reporting bugs
---------
Please report bugs to the [Github issues page](https://github.com/IOBR/IOBR/issues)
E-mail any questions to [email protected]