Package: ClusTCR2 Title: Identifying Similar T Cell Receptor Hyper-Variable Sequences with 'ClusTCR2' Version: 1.7.3.01 Date: 2024-05-15 Authors@R: c(person("Kerry A.", "Mullan", role = c("aut","cre"), email = "Kerry.Mullan@uantwerpen.be"), person("Sebastiaan", "Valkiers", role = c("aut","ctb")), person("Kris", "Laukens", role = c("aut","ctb")), person("Pieter", "Meysman", role = c("aut","ctb"), email = "")) Author: Kerry A. Mullan [aut, cre], Sebastiaan Valkiers [aut, ctb], Kris Laukens [aut, ctb], Pieter Meysman [aut, ctb] Description: 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) . Maintainer: Kerry A. Mullan License: GPL (>= 3) Encoding: UTF-8 RoxygenNote: 7.3.1 Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Config/testthat/edition: 3 Imports: DescTools, ggplot2, ggseqlogo, network, plyr, RColorBrewer, stringr, scales, sna, VLF biocViews: GeneTarget, SingleCell VignetteBuilder: knitr NeedsCompilation: no Packaged: 2026-07-02 05:41:50 UTC; root Repository: https://cran.r-universe.dev Date/Publication: 2024-05-17 02:36:54 UTC RemoteUrl: https://github.com/cran/ClusTCR2 RemoteRef: HEAD RemoteSha: 712bcc780c7b33ba862aea979219ba986a5f4b0b