A unified interface to immune deconvolution methods (CIBERSORT, EPIC, quanTIseq, TIMER, xCell, MCPcounter)
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immunedeconv - an R package for unified access to computational methods for estimating immune cell fractions from bulk RNA sequencing data.

travis license docs

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


For more detailed usage instructions, see the Documentation.

Available methods

method citation
quanTIseq Finotello, F., Mayer, C., Plattner, C., Laschober, G., Rieder, D., Hackl, H., … Trajanoski, Z. (2017). quanTIseq: quantifying immune contexture of human tumors. BioRxiv, 223180. https://doi.org/10.1101/223180
TIMER 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 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 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 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 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.


System requirements: linux and R >= 3.4.1. The package has been tested on CentOS Linux 7.5 with R 3.4.1.


The easiest way to retrieve this package and all its dependencies is to use Anaconda. Install 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
  1. Install the immunedeconv package
conda install -c grst,bioconda,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. Installation typically completes within 30 minutes, depending on how many dependency packages need to be compiled.

You need install the following non-CRAN dependencies. If you use a very recent version of devtools, it will also resolve these dependencies automatically.





Finally, install the immunedeconv package itself by running



If you use this package, please cite

Sturm, G., Finotello F. et al. "Comprehensive evaluation of cell-type quantification methods for immuno-oncology", bioRxiv, https://doi.org/10.1101/463828