mcl_cluster_large

Create the files for labeling the linked clusters from ClusTCR_list_to_matrix function

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>.

Kerry A. Mullan

ClusTCR2

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

Sebastiaan Valkiers

Kris Laukens

Pieter Meysman

mcl_cluster_large function

<dl><dt>my_file</dt>
<dd>Matrix file produce from ClusTCR</dd>
<dt>max.iter</dt>
<dd>Number of iterations to find the steady state of MCL.</dd>
<dt>inflation</dt>
<dd>numeric value</dd>
<dt>expansion</dt>
<dd>numeric value</dd></dl>

Arguments

Create the files for labeling the linked clusters from ClusTCR_list_to_matrix function — mcl_cluster_large

<dl>

<dt>my_file</dt>
<dd>Matrix file produce from ClusTCR</dd>


<dt>max.iter</dt>
<dd>Number of iterations to find the steady state of MCL.</dd>


<dt>inflation</dt>
<dd>numeric value</dd>


<dt>expansion</dt>
<dd>numeric value</dd>

</dl>

mcl_cluster_large: Create the files for labeling the linked clusters from ClusTCR_list_to_matrix function

Description

Usage

Value

Arguments