Identifying Similar T Cell Receptor Hyper-Variable Sequences with 'ClusTCR2'

Enhancing T cell receptor (TCR) sequence analysis, 'ClusTCR2', based on 'ClusTCR' python program, leverages Hamming distance to compare the complement-determining region three (CDR3) sequences for sequence similarity, variable gene (V gene) and length. The second step employs the Markov Cluster Algorithm to identify clusters within an undirected graph, providing a summary of amino acid motifs and matrix for generating network plots. Tailored for single-cell RNA-seq data with integrated TCR-seq information, 'ClusTCR2' is integrated into the Single Cell TCR and Expression Grouped Ontologies (STEGO) R application or 'STEGO.R'. See the two publications for more details. Sebastiaan Valkiers, Max Van Houcke, Kris Laukens, Pieter Meysman (2021) , Kerry A. Mullan, My Ha, Sebastiaan Valkiers, Nicky de Vrij, Benson Ogunjimi, Kris Laukens, Pieter Meysman (2023) .


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("ClusTCR2")

1.7.3.01 by Kerry A. Mullan, 2 years ago


Browse source code at https://github.com/cran/ClusTCR2


Authors: Kerry A. Mullan [aut, cre] , Sebastiaan Valkiers [aut, ctb] , Kris Laukens [aut, ctb] , Pieter Meysman [aut, ctb]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports DescTools, ggplot2, ggseqlogo, network, plyr, RColorBrewer, stringr, scales, sna, VLF

Suggests knitr, rmarkdown, testthat


See at CRAN