fine_clustering: Perform additional clustering of sequences within groups

In CellaRepertorium: Data structures, clustering and testing for single cell immune receptor repertoires (scRNAseq RepSeq/AIRR-seq)

Description

Perform additional clustering of sequences within groups

Usage

fine_clustering(
  ccdb,
  sequence_key,
  type,
  max_affinity = NULL,
  keep_clustering_details = FALSE,
  ...
)

Arguments

ccdb	A ContigCellDB() object
sequence_key	character naming column in contig_tbl with sequence
type	'AA' or 'DNA'
max_affinity	numeric naming the maximal affinity for the sparse affinity matrix that is constructed. Not currently used.
keep_clustering_details	logical – should output of fine_cluster_seqs be kept as a list column
...	Arguments passed on to fine_cluster_seqs big_memory_brute attempt to cluster more than 4000 sequences? Clustering is quadratic, so this will take a long time and might exhaust memory method one of 'substitutionMatrix' or 'levenshtein' substitution_matrix a character vector naming a substitution matrix available in Biostrings, or a substitution matrix itself

Value

ContigCellDB() object with updated contig_tbl and cluster_tbl

Examples

library(dplyr)
data(ccdb_ex)
ccdb_ex_small = ccdb_ex
ccdb_ex_small$cell_tbl = ccdb_ex_small$cell_tbl[1:200,]
ccdb_ex_small = cdhit_ccdb(ccdb_ex_small,
sequence_key = 'cdr3_nt', type = 'DNA', cluster_name = 'DNA97',
identity = .965, min_length = 12, G = 1)
ccdb_ex_small = fine_clustering(ccdb_ex_small, sequence_key = 'cdr3_nt', type = 'DNA')

# Canonicalize with the medoid contig is probably what is most common
ccdb_medoid = canonicalize_cluster(ccdb_ex_small)

# But there are other possibilities.
# To pass multiple "AND" filter arguments must use &
ccdb_umi = canonicalize_cluster(ccdb_ex_small,
contig_filter_args = chain == 'TRA' & length > 500, tie_break_keys = 'umis',
contig_fields = c('chain', 'length'))
ccdb_umi$cluster_tbl %>% dplyr::select(chain, length) %>% summary()

CellaRepertorium documentation built on Nov. 8, 2020, 7:48 p.m.