repClonality function encompasses several methods to measure
clonal proportions in a given repertoire.
repClonality( .data, .method = c("clonal.prop", "homeo", "top", "rare"), .perc = 10, .clone.types = c(Rare = 1e-05, Small = 1e-04, Medium = 0.001, Large = 0.01, Hyperexpanded = 1), .head = c(10, 100, 1000, 3000, 10000, 30000, 1e+05), .bound = c(1, 3, 10, 30, 100) )
The data to be processed. Can be data.frame, data.table, or a list of these objects.
Every object must have columns in the immunarch compatible format. immunarch_data_format
Competent users may provide advanced data representations: DBI database connections, Apache Spark DataFrame from copy_to or a list of these objects. They are supported with the same limitations as basic objects.
Note: each connection must represent a separate repertoire.
A String with one of the following options:
A single numerical value ranging from 0 to 100.
A named numerical vector with the boundaries of the half-closed intervals that mark off clonal groups.
A numerical vector with ranges of the top clonotypes.
A numerical vector with ranges of abundance for the rare clonotypes in the dataset.
If input data is a single immune repertoire, then the function returns a numeric vector with clonality statistics.
Otherwise, it returns a numeric matrix with clonality statistics for all input repertoires.
Clonal proportion assessment is a different approach to estimate repertoire diversity. When visualised, it allows for thorough examination of immune repertoire structure and composition.
In its core this type of analysis is similar to the relative species abundance concept in ecology. Relative abundance is the percent composition of an organism of a particular kind relative to the total number of organisms in the area.
A stacked barplot of relative clonotype abundances can be therefore viewed as a non-parametric approach to comparing their underlying distributions.
# Load the data data(immdata) imm_pr <- repClonality(immdata$data, .method = "clonal.prop") vis(imm_pr)imm_top <- repClonality(immdata$data, .method = "top", .head = c(10, 100, 1000, 3000, 10000)) vis(imm_top)#>imm_rare <- repClonality(immdata$data, .method = "rare") vis(imm_rare)#>imm_hom <- repClonality(immdata$data, .method = "homeo") vis(imm_hom)