Follow CRAN Downloads_all Downloads_week Issues CI DOI Visitors Downloads_all Downloads_week

immunarch — Fast and Seamless Exploration of Single-cell and Bulk T-cell/Antibody Immune Repertoires in R

Why immunarch?

Lightning-fast Start

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 samples

From Berkeley with devotion

immunarch is brought to you by ImmunoMind — a UC Berkeley SkyDeck startup. ImmunoMind Data Science tools for single-cell and immunomics exploration and biomarker discovery 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.


Stay connected!

  <input type="text" required name="_replyto" style="width:50%">
  <span class="highlight" style="width:50%; height:30%"></span>
  <span class="bar" style="width:50%"></span>
  <label>Leave your email and get the latest immunarch news</label>
  <button class="bttn-fill bttn-md bttn-danger" type="submit">Connect!</button>

Table of Contents


immunarch is an R package designed to analyse T-cell receptor (TCR) and B-cell receptor (BCR) repertoires, aimed at 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 help the community help you and enrich it with your experience. If you need to send us a sensitive data, feel free to contact us via


Latest release on CRAN

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 with 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.

Latest release on GitHub

If the above command doesn’t work for any reason, try installing immunarch directly from its repository:

install.packages("devtools") # skip this if you already installed devtools

Latest pre-release on GitHub

Since releasing on CRAN is limited to one release per one-two months, you can install the latest pre-release version with bleeding edge features and optimisations directly from the code repository. In order to install the latest pre-release version, you need to execute only two commands:

install.packages("devtools") # skip this if you already installed devtools
devtools::install_github("immunomind/immunarch", ref="dev")

You can find the list of releases of immunarch here:


  1. Fast and easy manipulation of immune repertoire data:

  2. Immune repertoire analysis made simple:

  3. Publication-ready plots with a built-in tool for visualisation manipulation:

Quick start

The gist of the typical TCR or BCR data analysis workflow can be reduced to the next few lines of code.

Use immunarch data

1) Load the package and the data

library(immunarch)  # Load the package into R
data(immdata)  # Load the test dataset

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 clonotypes

3) 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 status

Use your own data

library(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.

Advanced methods

For advanced methods such as clonotype annotation, clonotype tracking, kmer analysis and public repertoire analysis see “Tutorials”.

Bugs and Issues

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 (in case of private data).

Bug reports must:

  1. Include a short, self-contained R snippet reproducing the problem.
  2. Add a minimal data sample for us to reproduce the problem. In case of sensitive data you can send it to instead of GitHub issues.
  3. Explain why the current behavior is wrong/not desired and what you expect instead.
  4. If the issue is about visualisations, please attach a picture to the issue. In other case we wouldn’t be able to reproduce the bug and fix it.

Help the community

Have an aspiration to help the community build the ecosystem of scRNAseq & AIRR analysis tools? Found a bug? A typo? Would like to improve a documentation, add a method or optimise an algorithm?

We are always open to contributions. There are two ways to contribute:

  1. Create an issue here and describe what would you like to improve or discuss.

  2. 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.


  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          = {}

For EndNote citation import the immunarch-citation.xml file.

Preprint on BioArxiv is coming soon.


The package is freely distributed under the AGPL v3 license. You can read more about it here.

For commercial or server use, please contact ImmunoMind via about solutions for biomarker data science of single-cell immune repertoires.