immunedeconv - an R package for unified access to computational methods for estimating immune cell fractions from bulk RNA sequencing data.
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.
| 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 |
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.
-
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 -c grst -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.
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.
Bioconductor
source("https://bioconductor.org/biocLite.R")
biocLite("preprocessCore")
biocLite("Biobase")
biocLite("GSVA")
biocLite("sva")
biocLite("GSEABase")GitHub
library(devtools)
install_github('dviraran/xCell')
install_github('GfellerLab/EPIC')
install_github('ebecht/MCPcounter/Source')Finally, install the immunedeconv package itself by running
devtools::install_github('grst/immunedeconv')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