immunarch is an R package designed to analyse TCR and BCR (immunoglobulin) repertoires, which constitute a large amount of data. The mission of immunarch is to make immune sequencing data analysis as effortless as possible---and help you focus on research instead of coding. Follow us on Twitter or Telegram for news and updates.
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Fast and easy manipulation of immune repertoire data:
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The package automatically detects the format of your files---no more guessing what format is that file, just pass them to the package;
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Supports all popular TCR and BCR analysis and post-analysis formats: ImmunoSEQ, IMGT, MiTCR, MiXCR, MiGEC, MigMap, VDJtools, tcR, AIRR, 10XGenomics. More coming in the future;
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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;
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Tutorial is available here.
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Immune repertoire analysis made simple:
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Most methods are incorporated in a couple of main functions with clear naming---no more remembering tens and tens of functions with obscure names. For details see link;
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Repertoire overlap analysis (common indices including overlap coefficient, Jaccard index and Morisita's overlap index). Tutorial is available here;
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Gene usage estimation (correlation, Jensen-Shannon Divergence, clustering). Tutorial is available here;
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Diversity evaluation (ecological diversity index, Gini index, inverse Simpson index, rarefaction analysis). Tutorial is available here;
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Coming in the next releases: CDR3 amino acid physical and chemical properties assessment, Kmer distribution measures and statistics, mutation networks, tracking clonotypes across time points.
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Publication-ready plots with a built-in tool for visualisation manipulation:
immunarch is an R package designed to analyse TCR and BCR (immunoglobulin) repertoires, which constitute a large amount of data. The mission of immunarch is to make immune sequencing data analysis as effortless as possible---and help you focus on research instead of coding.
You can find the list of releases of immunarch here: https://github.com/immunomind/immunarch/releases
In order to install immunarch, you need to download it first. If you want to download the latest version, you need to download the package file, available here https://github.com/immunomind/immunarch/releases/download/latest/immunarch.tar.gz
Note that You should not un-archive it!
After downloading the file, you need to install a number of packages with R commands listed below, and run the newly installed devtools package to install immunarch locally. Upon completion the dependencies will have been already downloaded and installed.
install.packages("devtools", dependencies = T)
devtools::install_local("path/to/your/folder/with/immunarch.tar.gz", dependencies=T)That's it, you can start using immunarch now!
If you run in any trouble, try the following steps:
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Check your R version. Run
versioncommand in the console to get your R versions. If the R version is below 3.4.0 (for example,R version 3.1.0), try updating your R version to the latest one. -
Check if your packages are outdated and update them. In RStudio you can run the "Update" button on top of the package list. In R console you can run the
old.packages()command to view a list of outdated packages. -
If you are on Mac and have issues like old packages can't be updated, or error messages such as
ld: warning: directory not found for optionorld: library not found for -lgfortran, this link will help you to fix the issue. -
If you are working under Linux and have issues with igraph library or have Fortran errors, see this link
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If troubles still persist, message us on support@immunomind.io or create an issue in https://github.com/immunomind/immunarch/issues with the code that represents the issue and the output you get in the console.
Importing data into R is fairly simple. The gist of the typical TCR or BCR explorational data analysis workflow can be reduced to the next few lines of code:
# Load the data to the package
immdata = repLoad("path/to/your/folder/with/repertoires")
# If you folder contains metadata.txt file, immdata will have two elements:
# - immdata$data with a list of parsed repertoires
# - immdata$meta with the metadata file
# Compute and visualise overlap statistics
ov = repOverlap(immdata$data)
vis(ov)
# Cluster samples using K-means algorithm applied to the number of overlapped clonotypes
# and visualise the results
ov.kmeans = repOverlapAnalysis(ov, .method = "kmeans")
vis(ov.kmeans)
# Compute and visualise gene usage with samples, grouped by their disease status
gu = geneUsage(immdata$data)
vis(gu, .by="Status", .meta=immdata$meta)
# Compute Jensen-Shannon divergence among gene distributions of samples,
# cluster samples using the hierarchical clustering and visualise the results
gu.clust = geneUsageAnalysis(gu, .method = "js+hclust")
vis(gu.clust)
# Compare diversity of repertoires and visualise samples, grouped by two parameters
div = repDiversity(immdata$data, .method = "chao1")
vis(div, .by=c("Status", "Treatment"), .meta=immdata$meta)
# Manipulate the visualisation of diversity estimates to make the plot publication-ready
div.plot = vis(div, .by=c("Status", "Treatment"), .meta=immdata$meta)
fixVis(div.plot)If you want to test the package without parsing any data, you can load a small test dataset provided along with the package. Load the data with the following command:
data(immdata)The package is freely distributed under the AGPL v3 license. You can read more about it here.
Additionally, we provide an annual subscription that includes next services:
- 100 hours of consultations on the TCR & BCR repertoire analysis (contact us to purchase more);
- Priority email and call support;
- Package modifications and feature implementations are issued promptly;
- Setup a cloud or cluster installation of immunarch, including the development of cloud immunarch-based software;
- Use immunarch team expertise in your projects;
- If you need a GPL-compatible license other than AGPL v3, contact us.
Contact us at support@immunomind.io for more information.