immunedeconv - 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
epicFor 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.
Installation
System requirements: R >= 3.4.1. Only linux is officially supported, but Mac/Windows should work, too.
Conda
The easiest way to retrieve this package and all its dependencies is to use Anaconda. Install typically completes within minutes.
Download Miniconda, if you don't have a conda installation already.
(Optional) create and activate an environment for deconvolution:
conda create -n deconvolution
conda activate deconvolution- Install the
immunedeconvpackage
conda install --override -c grst -c bioconda -c conda-forge/label/cf201901 r-immunedeconvNote: due to a recent conda compiler update, immunedeconv needs
to be installed using the cf201901 label of conda-forge. I'm working on making it work with the default version...
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.
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("grst/immunedeconv")Citation
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