cluster_plot: Make a plot showing properties of the clustering

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

Description

The number of elements per cluster and the average distance between the medoid and other elements are plotted.

Usage

cluster_plot(cdb, return_plotlist = FALSE)

Arguments

cdb	A fine_clustering ContigCellDB object
return_plotlist	should a list of ggplot2 plots be returned. If FALSE, a cowplot composite is retuned.

Value

a cowplot composite or a list of plots.

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()
cluster_plot(ccdb_ex_small)

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