{"id":13773952,"url":"https://github.com/omnideconv/immunedeconv","last_synced_at":"2026-02-23T04:51:54.945Z","repository":{"id":38819951,"uuid":"126168168","full_name":"omnideconv/immunedeconv","owner":"omnideconv","description":"A unified interface to immune deconvolution methods (CIBERSORT, EPIC, quanTIseq, TIMER, xCell, MCPcounter) and mouse deconvolution 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fractions from bulk RNA sequencing data.**\n\n[![tests](https://github.com/omnideconv/immunedeconv/actions/workflows/test.yml/badge.svg)](https://github.com/omnideconv/immunedeconv/actions/workflows/test.yml)\n[![test-conda](https://github.com/omnideconv/immunedeconv/actions/workflows/conda.yml/badge.svg)](https://github.com/omnideconv/immunedeconv/actions/workflows/conda.yml)\n[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/r-immunedeconv/README.html)\n[![license](https://img.shields.io/badge/license-GPL2-blue.svg)](https://github.com/omnideconv/immunedeconv/blob/master/LICENSE.md)\n[![docs](https://img.shields.io/badge/docs-pkgdown-blue.svg)](https://omnideconv.github.io/immunedeconv)\n[![Codecov test coverage](https://codecov.io/gh/omnideconv/immunedeconv/branch/master/graph/badge.svg)](https://app.codecov.io/gh/omnideconv/immunedeconv?branch=master)\n\n## Basic usage\n\nDeconvolution of human data:\n\n```R\nimmunedeconv::deconvolute(gene_expression_matrix, \"quantiseq\")\n```\n\nDeconvolution of mouse data:\n\n```R\nimmunedeconv::deconvolute_mouse(gene_expression_matrix, \"mmcp_counter\")\n```\n\nwhere `gene_expression_matrix` is a matrix with genes in rows and samples in columns. The rownames must be\n[HGNC](https://www.genenames.org/) symbols for human data, or [MGI](http://www.informatics.jax.org/mgihome/nomen/) gene symbols for mouse data.\nThe colnames must be sample names. For human data, the method can be one of\n\n```\nquantiseq\ntimer\ncibersort\ncibersort_abs\nmcp_counter\nxcell\nepic\nabis\nconsensus_tme\nestimate\n```\n\nThe [ESTIMATE](https://bioinformatics.mdanderson.org/public-software/estimate/) algorithm, which computes a score for the tumoral, immune and stromal components and the fraction of tumor purity of a sample, has been implemented.\n\n```R\nimmunedeconv::deconvolute_estimate(gene_expression_matrix)\n```\n\nThe methods available for the deconvolution of mouse data are\n\n```\nmmcp_counter\nseqimmucc\ndcq\nbase\n```\n\nIn addition, human-based methods can be used to deconvolute mouse data through the conversion to orthologous gene names\n\n```r\ngene_expression_matrix \u003c- immunedeconv::mouse_genes_to_human(gene_expression_matrix)\nimmunedeconv::deconvolute(gene_expression_matrix, \"quantiseq\")\n```\n\nFinally, certain methods can be used with custom signatures, consisting of either a signature matrix or signature genes\nfor the cell types of interest. Since the information used to deconvolute the bulk is user-provided, these functions can be\nused for different tissues and organisms.\nThe functions may require different input data formats, related to the requirements of each method. Please refer to their documentation.\nThe available methods are\n\n```r\nbase:  deconvolute_base_custom()\ncibersort norm/abs:  deconvolute_cibersort_custom()\nepic: deconvolute_epic_custom()\nconsensus_tme: deconvolute_consensus_tme_custom()\n```\n\nFor more detailed usage instructions, see the Documentation:\n\n- [Getting started](https://omnideconv.org/immunedeconv/articles/immunedeconv.html).\n- [Detailed example](https://omnideconv.org/immunedeconv/articles/detailed_example.html).\n- [Detailed example - mouse](https://omnideconv.org/immunedeconv/articles/detailed_example_mouse.html).\n\n## Available methods, Licenses, Citations\n\nNote that, while _immunedeconv_ itself is free ([BSD](https://github.com/omnideconv/immunedeconv/blob/master/LICENSE)), 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.\n\n\u003e 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\n\n| method                                                                     | organism | license                                                                                                       | citation                                                                                                                                                                                                                                                                                                                                                                             |\n| -------------------------------------------------------------------------- | -------- | ------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| [quanTIseq](http://icbi.at/software/quantiseq/doc/index.html)              | human    | free ([BSD](https://github.com/omnideconv/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                                                                                       |\n| [TIMER](http://cistrome.org/TIMER/)                                        | human    | 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                                                                                                                                        |\n| [CIBERSORT](https://cibersort.stanford.edu/)                               | human    | 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                                                                                                                                          |\n| [MCPCounter](https://github.com/ebecht/MCPcounter)                         | human    | 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                                                                               |\n| [xCell](http://xcell.ucsf.edu/)                                            | human    | free ([GPL 3.0](https://github.com/dviraran/xCell/blob/master/DESCRIPTION))                                   | Aran, D., Hu, Z., \u0026 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                                                                                                                                                                                              |\n| [EPIC](https://gfellerlab.shinyapps.io/EPIC_1-1/)                          | human    | 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., \u0026 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                                                                                                                                                |\n| [ESTIMATE](https://gfellerlab.shinyapps.io/EPIC_1-1/)                      | human    | free ([GPL 2.0](https://bioinformatics.mdanderson.org/public-software/estimate/))                             | Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., Carter, S. L., Getz, G., Stemke-Hale, K., Mills, G. B., \u0026 Verhaak, R. G. (2013). Inferring tumour purity and stromal and immune cell admixture from expression data. Nature communications, 4, 2612. https://doi.org/10.1038/ncomms3612 |\n| [ABIS](https://giannimonaco.shinyapps.io/ABIS/)                            | human    | free ([GPL 2.0](https://github.com/giannimonaco/ABIS))                                                        | Monaco, G., Lee, B., Xu, W., Mustafah, S., Hwang, Y. Y., ..., Larbi, A. (2019). RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types. Cell reports, 26(6), 1627–1640.e7. https://doi.org/10.1016/j.celrep.2019.01.041                                                                                                             |\n| [ConsensusTME](https://olliecast.shinyapps.io/Deconvolution_Benchmarking/) | human    | free ([GPL 3.0](https://github.com/cansysbio/ConsensusTME/blob/master/LICENSE.md))                            | Jiménez-Sánchez, A., Cast, O., \u0026 Miller, M. L. (2019). Comprehensive Benchmarking and Integration of Tumor Microenvironment Cell Estimation Methods. Cancer research, 79(24), 6238–6246. https://doi.org/10.1158/0008-5472.CAN-18-3560                                                                                                                                               |\n| [mMCPCounter](https://github.com/cit-bioinfo/mMCP-counter)                 | mouse    | free ([GPL 3.0](https://github.com/cit-bioinfo/mMCP-counter/blob/master/LICENSE.md))                          | Petitprez, F., Levy, S., Sun, C. M., Meylan, M., ..., de Reyniès, A. (2020). The murine Microenvironment Cell Population counter method to estimate abundance of tissue-infiltrating immune and stromal cell populations in murine samples using gene expression. Genome medicine, 12(1), 86. https://doi.org/10.1186/s13073-020-00783-w                                             |\n| [seqImmuCC](218.4.234.74:3200/immune/)                                     | mouse    | free for non-commerical use only                                                                              | Chen, Z., Quan, L., Huang, A., Zhao, Q., Yuan, Y., Yuan, X., ..., Wu, A. (2018). seq-ImmuCC: Cell-Centric View of Tissue Transcriptome Measuring Cellular Compositions of Immune Microenvironment From Mouse RNA-Seq Data. Frontiers in immunology, 9, 1286. https://doi.org/10.3389/fimmu.2018.01286                                                                                |\n| [DCQ](http://dcq.tau.ac.il/)                                               | mouse    | free ([GPL 2.0](https://cran.r-project.org/web/packages/ComICS/index.html))                                   | Altboum, Z., Steuerman, Y., David, E., Barnett-Itzhaki, Z., Valadarsky, L., ..., Amit, I. (2014). Digital cell quantification identifies global immune cell dynamics during influenza infection. Molecular systems biology, 10(2), 720. https://doi.org/10.1002/msb.134947                                                                                                           |\n| BASE                                                                       | mouse    | free                                                                                                          | Varn, F. S., Andrews, E. H., Mullins, D. W., \u0026 Cheng, C. (2016). Integrative analysis of breast cancer reveals prognostic haematopoietic activity and patient-specific immune response profiles. Nature communications, 7, 10248. https://doi.org/10.1038/ncomms10248                                                                                                                |\n\n### Comparison of the methods\n\nFor a benchmark comparison of the human-based methods, please see our [publication](https://doi.org/10.1101/463828).\nIf you would like to benchmark additional methods, please see our [benchmark\npipeline](https://github.com/omnideconv/immune_deconvolution_benchmark).\n\n## Installation\n\nSystem requirements: R \u003e= 4.1. Only linux is officially supported, but Mac/Windows should work, too.\n\n### Bioconda (Linux/MacOS only)\n\nThe easiest way to retrieve this package and all its dependencies is to use [Anaconda](https://conda.io/miniconda.html).\nThe installation typically completes within minutes.\n\n1. Download [Miniconda](https://conda.io/miniconda.html), if you don't have a conda installation already.\n\n2. (Optional) create and activate an environment for deconvolution:\n\n```\nconda create -n deconvolution\nconda activate deconvolution\n```\n\n3. Install the `immunedeconv` package\n\n```\nconda install -c bioconda -c conda-forge r-immunedeconv\n```\n\n`conda` will automatically install the package and all dependencies.\nYou can then open an `R` instance within the environment and use the package.\n\n### Standard R Package\n\nWe highly recommend using `conda`, as it will avoid incompatibilities between\ndifferent package versions. That being said, you can also install `immunedeconv`\nas a regular R package in your default R installation. The installation typically completes within 30 minutes, depending\non how many dependency packages need to be compiled.\n\nThe easiest way to do so is to use the `remotes` package, which will automatically download all CRAN, Bioconductor and GitHub dependencies:\n\n```R\ninstall.packages(\"remotes\")\nremotes::install_github(\"omnideconv/immunedeconv\")\n```\n\n## Credits\n\nThis package was originally developed by [Gregor Sturm](https://github.com/grst) in 2018 at [Pieris Pharmaceuticals GmbH](https://www.pieris.com/) in collaboration with [Markus List](https://biomedical-big-data.de/), [Tatsiana Aneichyk](https://www.independentdatalab.com/team), and [Francesca Finotello](https://computationalbiomedicinegroup.github.io/). Gregor Sturm continued to support this package while at [ICBI (Med Uni Innsbruck)](https://icbi.at). In 2022, this repository moved to the [omnideconv](https://omnideconv.org) organization, a joint effort of the [List Lab](https://biomedical-big-data.de/) and [Finotello Lab](https://computationalbiomedicinegroup.github.io/) dedicated to improve accessibility of deconvolution methods. At this point [Lorenzo Merotto](https://github.com/LorenzoMerotto) became primary maintainer of the immunedeconv package.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fomnideconv%2Fimmunedeconv","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fomnideconv%2Fimmunedeconv","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fomnideconv%2Fimmunedeconv/lists"}