Af_compare_within_repertoires: Function to compare tree topology of B cell lineages

In AntibodyForests: Delineating Inter- And Intra-Antibody Repertoire Evolution

Function to compare tree topology of B cell lineages

Description

Function to compare trees of clonotypes.

Usage

Af_compare_within_repertoires(
  input,
  min.nodes,
  distance.method,
  distance.metrics,
  clustering.method,
  visualization.methods,
  plot.label,
  text.size,
  point.size = 2,
  parallel,
  num.cores
)

Arguments

input	list - An AntibodyForests-object, output from Af_build()
min.nodes	integer - The minimum number of nodes in a tree to include in the comparison
distance.method	string - The method to calculate distance (default ...) 'none' : No distance metric, analyze similarity directly from distance.metrics 'euclidean' : 'jensen-shannon' : Jensen-Shannon distance between spectral density profiles of trees.
distance.metrics	string - If distance.method is "none" or "euclidean", these metrics will be used to calculate clusters and PCA/MDS dimensions and are used for plotting. (Default is mean.depth and nr.nodes) 'nr.nodes' : The total number of nodes 'nr.cells' : The total number of cells in this clonotype 'mean.depth' : Mean of the number of edges connecting each node to the germline 'mean.edge.length' : Mean of the edge lengths between each node and the germline 'group.depth' : Mean of the number of edges connecting each node per group (node.features of the AntibodyForests-object) to the germline. (default FALSE) 'sackin.index' : Sum of the number of nodes between each terminal node and the germline, normalized for the number of terminal nodes. 'spectral.density' : Metrics of the spectral density profiles (calculated with package RPANDA) peakedness : Tree balance asymmetry : Shallow or deep branching events principal eigenvalue : Phylogenetic diversity modalities : The number of different structures within the tree
clustering.method	string - Method to cluster trees (default none) 'none' : No clustering 'mediods' : Clustering based on the k-mediods method. The number of clusters is estimated based on the optimum average silhouette.
visualization.methods	string - The methods to analyze similarity (default PCA) 'PCA' : Scatterplot of the first two principal components. This is usefull when distance.method is "none". 'MDS' : Scatterplot of the first two dimensions using multidimensional scaling. Usefull for all distance methods 'heatmap' : A (clustered) heatmap of the distance between clonotypes. If distance.method is "none", euclidean distance will be calculated.
plot.label	boolean - Label clonotypes in the PCA/MDS plot (default FALSE)
text.size	integer - Size of the text in the plots (default 12)
point.size	integer - Size of the points in the plots (default 2)
parallel	If TRUE, the metric calculations are parallelized (default FALSE)
num.cores	Number of cores to be used when parallel = TRUE (Defaults to all available cores - 1)

Value

• list - Returns a distance matrix, clustering, and various plots based on visualization.methods

Examples

compare_repertoire <- Af_compare_within_repertoires(input = AntibodyForests::small_af,
                                     min.nodes = 8,
                                     distance.method = "euclidean",
                                     distance.metrics = c("mean.depth", "sackin.index"),
                                     clustering.method = "mediods",
                                     visualization.methods = "PCA")
#Plot the PCA clusters
compare_repertoire$plots$PCA_clusters

AntibodyForests documentation built on April 4, 2025, 4:45 a.m.