The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
immunarch — Fast and Seamless Exploration of Single-cell and Bulk T-cell/Antibody Immune Repertoires in Rimmunarch?immunarch code.immunarch with the latest methods. Let us know what you need!install.packages("immunarch") # Install the package
library(immunarch); data(immdata) # Load the package and the test dataset
repOverlap(immdata$data) %>% vis() # Compute and visualise the most important statistics:
geneUsage(immdata$data[[1]]) %>% vis() # public clonotypes, gene usage, sample diversity
repDiversity(immdata$data) %>% vis(.by = "Status", .meta = immdata$meta) # Group samplesimmunarch is brought to you by ImmunoMind — a UC Berkeley SkyDeck startup. ImmunoMind improves the design of adoptive T-cell therapies such as CAR-T by precisely identifying T-cell subpopulations and their immune profile. ImmunoMind’s tools are trusted by researchers from top pharma companies and universities, including 10X Genomics, Pfizer, Regeneron, UCSF, MIT, Stanford, John Hopkins School of Medicine and Vanderbilt University.
immunarch is an R package designed to analyse T-cell receptor (TCR) and B-cell receptor (BCR) repertoires, mainly tailored to medical scientists and bioinformaticians. The mission of immunarch is to make immune sequencing data analysis as effortless as possible and help you focus on research instead of coding.
Create a ticket with a bug or question on GitHub Issues to get help from the community and enrich it with your experience. If you need to send us sensitive data, feel free to contact us via support@immunomind.io.
In order to install immunarch execute the following command:
That’s it, you can start using immunarch now! See the Quick Start section below to dive into immune repertoire data analysis. If you run in any trouble during installation, take a look at the Installation Troubleshooting section.
Note: there are quite a lot of dependencies to install with the package because it installs all the widely-used packages for data analysis and visualisation. You got both the AIRR data analysis framework and the full Data Science package ecosystem with only one command, making immunarch the entry-point for single-cell & immune repertoire Data Science.
If the above command doesn’t work for any reason, try installing immunarch directly from its repository:
install.packages(c("devtools", "pkgload")) # skip this if you already installed these packages
devtools::install_github("immunomind/immunarch")
devtools::reload(pkgload::inst("immunarch"))Since releasing on CRAN is limited to one release per one or two months, you can install the latest pre-release version with all the bleeding edge and optimised features directly from the code repository. In order to install the latest pre-release version, you need to execute the following commands:
install.packages(c("devtools", "pkgload")) # skip this if you already installed these packages
devtools::install_github("immunomind/immunarch", ref="dev")
devtools::reload(pkgload::inst("immunarch"))You can find the list of releases of immunarch here: https://github.com/immunomind/immunarch/releases
Data agnostic. Fast and easy manipulation of immune repertoire data:
The package automatically detects the format of your files—no more guessing what format is that file, just pass them to the package;
Supports all popular TCR and BCR analysis and post-analysis formats, including single-cell data: ImmunoSEQ, IMGT, MiTCR, MiXCR, MiGEC, MigMap, VDJtools, tcR, AIRR, 10XGenomics, ArcherDX. More coming in the future;
Works on any data source you are comfortable with: R data frames, data tables from data.table, databases like MonetDB, Apache Spark data frames via sparklyr;
Tutorial is available here.
Beginner-friendly. Immune repertoire analysis made simple:
Most methods are incorporated in a couple of main functions with clear naming—no more remembering dozens and dozens of functions with obscure names. For details see link;
Repertoire overlap analysis (common indices including overlap coefficient, Jaccard index and Morisita’s overlap index). Tutorial is available here;
Gene usage estimation (correlation, Jensen-Shannon Divergence, clustering). Tutorial is available here;
Diversity evaluation (ecological diversity index, Gini index, inverse Simpson index, rarefaction analysis). Tutorial is available here;
Tracking of clonotypes across time points, widely used in vaccination and cancer immunology domains. Tutorial is available here;
K-mer distribution measures and statistics. Tutorial is available here;
Coming in the next releases: CDR3 amino acid physical and chemical properties assessment, mutation networks.
Seamless publication-ready plots with a built-in tool for visualisation manipulation:
The gist of the typical TCR or BCR data analysis workflow can be reduced to the next few lines of code.
immunarch data1) Load the package and the data
2) Calculate and visualise basic statistics
repExplore(immdata$data, "lens") %>% vis() # Visualise the length distribution of CDR3
repClonality(immdata$data, "homeo") %>% vis() # Visualise the relative abundance of clonotypes3) Explore and compare T-cell and B-cell repertoires
repOverlap(immdata$data) %>% vis() # Build the heatmap of public clonotypes shared between repertoires
geneUsage(immdata$data[[1]]) %>% vis() # Visualise the V-gene distribution for the first repertoire
repDiversity(immdata$data) %>% vis(.by = "Status", .meta = immdata$meta) # Visualise the Chao1 diversity of repertoires, grouped by the patient statuslibrary(immunarch) # Load the package into R
immdata <- repLoad("path/to/your/data") # Replace it with the path to your data. Immunarch automatically detects the file format.For advanced methods such as clonotype annotation, clonotype tracking, k-mer analysis and public repertoire analysis see “Tutorials”.
The mission of immunarch is to make bulk and single-cell immune repertoires analysis painless. All bug reports, documentation improvements, enhancements and ideas are appreciated. Just let us know via GitHub (preferably) or support@immunomind.io (in case of private data).
Bug reports must:
Aspiring to help the community build the ecosystem of scRNAseq & AIRR analysis tools? Found a bug? A typo? Would like to improve documentation, add a method or optimise an algorithm?
We are always open to contributions. There are two ways to contribute:
Create an issue here and describe what would you like to improve or discuss.
Create an issue or find one here, fork the repository and make a pull request with the bugfix or improvement.
ImmunoMind Team. (2019). immunarch: An R Package for Painless Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires. Zenodo. http://doi.org/10.5281/zenodo.3367200
BibTex:
@misc{immunomind_team_2019_3367200,
author = {{ImmunoMind Team}},
title = {{immunarch: An R Package for Painless Bioinformatics Analysis
of T-Cell and B-Cell Immune Repertoires}},
month = aug,
year = 2019,
doi = {10.5281/zenodo.3367200},
url = {https://doi.org/10.5281/zenodo.3367200}
}For EndNote citation import the immunarch-citation.xml file.
Preprint on BioArxiv is coming soon.
The package is freely distributed under the Apache-2.0 license. You can read more about it here.
For commercial or server use, please contact ImmunoMind via support@immunomind.io about solutions for biomarker data science of single-cell immune repertoires.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.