This function will take the meta data from the product of
combineExpression() and generate a relational data frame to
be used for a chord diagram. Each cord will represent the number of
clone unique and shared across the multiple group.by variable.
If using the downstream circlize R package, please read and cite the
following manuscript.
If looking for more advance ways for circular visualizations, there
is a great cookbook
for the circlize package.
getCirclize(
sc.data,
cloneCall = "strict",
group.by = NULL,
proportion = FALSE,
include.self = TRUE
)The single-cell object after combineExpression().
Defines the clonal sequence grouping. Accepted values
are: gene (VDJC genes), nt (CDR3 nucleotide sequence), aa (CDR3 amino
acid sequence), or strict (VDJC + nt). A custom column header can also be used.
A column header in the metadata to group the analysis
by (e.g., "sample", "treatment"). If NULL, data will be analyzed by active
identity.
Calculate the relationship unique clones (proportion = FALSE) or normalized by proportion (proportion = TRUE)
Include counting the clones within a single group.by comparison
A data frame of shared clones between groups formatted for chordDiagram
# Getting the combined contigs
combined <- combineTCR(contig_list,
samples = c("P17B", "P17L", "P18B", "P18L",
"P19B","P19L", "P20B", "P20L"))
# Getting a sample of a Seurat object
scRep_example <- get(data("scRep_example"))
scRep_example <- combineExpression(combined,
scRep_example)
# Getting data frame output for Circlize
circles <- getCirclize(scRep_example,
group.by = "seurat_clusters")