tropicalberto/immunedeconv

A unified interface to immune deconvolution methods (CIBERSORT, EPIC, quanTIseq, TIMER, xCell, MCPcounter)

an R package for unified access to computational methods for estimating immune cell fractions from bulk RNA sequencing data.

Basic usage

immunedeconv::deconvolute(gene_expression_matrix, "quantiseq")

where gene_expression_matrix is a matrix with genes in rows and samples in columns. The rownames must be HGNC symbols and the colnames must be sample names. The method can be one of

quantiseq
timer
cibersort
cibersort_abs
mcp_counter
xcell
epic

For more detailed usage instructions, see the Documentation:

• Getting started.
• Detailed example.

Available methods, Licenses, Citations

Note that, while immunedeconv itself is free (BSD), you may need to obtain a license to use the individual methods. See the table below for more information. If you use this package in your work, please cite both our package and the method(s) you are using.

Sturm, G., Finotello, F., Petitprez, F., Zhang, J. D., Baumbach, J., Fridman, W. H., ..., List, M., Aneichyk, T. (2019). Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology. Bioinformatics, 35(14), i436-i445. https://doi.org/10.1093/bioinformatics/btz363

method	license	citation
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
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
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

Comparison of the methods

For a benchmark comparison of the methods, please see our publication. If you would like to benchmark additional methods, please see our benchmark pipeline.

Installation

System requirements: R >= 3.5. Only linux is officially supported, but Mac/Windows should work, too.

Bioconda (Linux/MacOS only)

The easiest way to retrieve this package and all its dependencies is to use Anaconda. The installation typically completes within minutes.

1. Download Miniconda, if you don't have a conda installation already.

2. (Optional) create and activate an environment for deconvolution:

conda create -n deconvolution
conda activate deconvolution

3. Install the immunedeconv package

conda install -c bioconda -c conda-forge r-immunedeconv

conda will automatically install the package and all dependencies. You can then open an R instance within the environment and use the package.

Standard R Package

We highly recommend using conda, as it will avoid incompatibilities between different package versions. That being said, you can also install immunedeconv as a regular R package in your default R installation. The installation typically completes within 30 minutes, depending on how many dependency packages need to be compiled.

The easiest way to do so is to use the remotes package, which will automatically download all CRAN, Bioconductor and GitHub dependencies:

install.packages("remotes")
remotes::install_github("icbi-lab/immunedeconv")