ClusTCR2: 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) <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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