clonalCluster: Clustering adaptive receptor sequences by edit distance

In ncborcherding/scRepertoire: A toolkit for single-cell immune receptor profiling

Clustering adaptive receptor sequences by edit distance

Description

This function uses edit distances of either the nucleotide or amino acid sequences of the CDR3 and V genes to cluster similar TCR/BCRs together. As a default, the function takes the input from combineTCR, combineBCR or combineExpression and amends a cluster to the data frame or meta data. If exportGraph is set to TRUE, the function returns an igraph object of the connected sequences.

Usage

clonalCluster(
  input.data,
  chain = "TRB",
  sequence = "aa",
  samples = NULL,
  threshold = 0.85,
  group.by = NULL,
  exportGraph = FALSE
)

Arguments

input.data	The product of combineTCR, combineBCR or combineExpression.
chain	Indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL".
sequence	Clustering based on either "aa" or "nt".
samples	The specific samples to isolate for visualization.
threshold	The normalized edit distance to consider. The higher the number the more similarity of sequence will be used for clustering.
group.by	The column header used for to group contigs. If (NULL), clusters will be calculated across samples.
exportGraph	Return an igraph object of connected sequences (TRUE) or the amended input with a new cluster-based variable (FALSE).

Value

Either amended input with edit-distanced clusters added or igraph object of connect sequences

Examples

# Getting the combined contigs
combined <- combineTCR(contig_list, 
                        samples = c("P17B", "P17L", "P18B", "P18L", 
                                    "P19B","P19L", "P20B", "P20L"))

sub_combined <- clonalCluster(combined[c(1,2)], 
                              chain = "TRA", 
                              sequence = "aa")

ncborcherding/scRepertoire documentation built on April 27, 2024, 8:34 p.m.