R/AntibodyForests_communities.R
AntibodyForests_communities.RdPerforms community detection/clustering on the AntibodyForests sequence similarity networks. Annotates the resulting networks with a new igraph vertex attribute ('community') for downstream analysis or plotting. Can also add these annotations back to the VGM.
AntibodyForests_communities(
trees,
VGM,
community.algorithm,
graph.type,
which.bipartite,
features,
count.level,
additional.parameters
)AntibodyForests object/list of AntibodyForests objects - the resulting sequence similarity or minimum spanning tree networks from the AntibodyForests function
VGM object - for annotating the VGM object with the resulting clusters/communities.
string - denotes the community/clustering algorithm to be used. Several options are available: 'louvain', 'walktrap', 'edge_betweenness', 'fast_greedy', 'label_prop', 'leading_eigen', 'optimal', 'spinglass'.
string - the graph type available in the AntibodyForests object which will be used as the function input. Currently supported network/analysis types: 'tree' (for the minimum spanning trees or sequence similarity networks obtained from the main AntibodyForests function), 'heterogeneous' for the bipartite graphs obtained via AntibodyForests_heterogeneous, 'dynamic' for the dynamic networks obtained from AntibodyForests_dynamics.
string - whether to perform clustering on the cell layer of the bipartite/heterogeneous graph ('cells'), sequence layer ('sequences') or on both ('both').
vector of strings - features to be considered in the output bar plots (of feature counts per cluster). These features must be integrated when creating the initial AntibodyForests objects by using the node.features parameter.
string - whether to consider cells ('cells') or sequences ('sequences') when counting the unique feature values in the output bar plots. When counting by sequences/nodes, each unique node is assigned the feature value of the majority of its consituent cells.
named list - additional parameters to be considered in the clustering algorithm, as mentioned in the igraph documentation for the respective algorithms (e.g., additional.parameters = list(resolution = 0.25)).
a single AntibodyForests object or a nested list of AntibodyForests objects (depending on the input type) with community/cluster annotations as a vertex attribute. Additional bar plots of feature counts per resulting cluster are also displayed.
AntibodyForests, AntibodyForests_plot
if (FALSE) {
AntibodyForests_communities(trees = AntibodyForests_object,
VGM = NULL, community.algorithm = 'louvain',
graph.type = 'tree', features = 'seurat_clusters',
count.level = 'cells', additional.parameters = list(resolution = 0.25))
}