ClusTCR2: Identifying Similar T Cell Receptor Hyper-Variable Sequenceswith '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) <doi:10.1093/bioinformatics/btab446>, Kerry A. Mullan, My Ha, Sebastiaan Valkiers, Nicky de Vrij, Benson Ogunjimi, Kris Laukens, Pieter Meysman (2023) <doi:10.1101/2023.09.27.559702>.
| Version: | 1.7.3.01 |
| Imports: | DescTools,ggplot2,ggseqlogo,network,plyr,RColorBrewer,stringr,scales,sna,VLF |
| Suggests: | knitr,rmarkdown,testthat (≥ 3.0.0) |
| Published: | 2024-05-16 |
| DOI: | 10.32614/CRAN.package.ClusTCR2 |
| Author: | Kerry A. Mullan [aut, cre], Sebastiaan Valkiers [aut, ctb], Kris Laukens [aut, ctb], Pieter Meysman [aut, ctb] |
| Maintainer: | Kerry A. Mullan <Kerry.Mullan at uantwerpen.be> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| CRAN checks: | ClusTCR2 results |
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