Specify Node-level Network Properties to Compute

Create a vector specifying node-level network properties to compute. Intended for use with buildRepSeqNetwork() or addNodeNetworkStats.

node_stat_settings() is a deprecated equivalent of chooseNodeStats().

Usage

chooseNodeStats(
  degree = TRUE,
  cluster_id = FALSE,
  transitivity = TRUE,
  closeness = FALSE,
  centrality_by_closeness = FALSE,
  eigen_centrality = TRUE,
  centrality_by_eigen = TRUE,
  betweenness = TRUE,
  centrality_by_betweenness = TRUE,
  authority_score = TRUE,
  coreness = TRUE,
  page_rank = TRUE,
  all_stats = FALSE
)

exclusiveNodeStats(
  degree = FALSE,
  cluster_id = FALSE,
  transitivity = FALSE,
  closeness = FALSE,
  centrality_by_closeness = FALSE,
  eigen_centrality = FALSE,
  centrality_by_eigen = FALSE,
  betweenness = FALSE,
  centrality_by_betweenness = FALSE,
  authority_score = FALSE,
  coreness = FALSE,
  page_rank = FALSE
)

Arguments

degree: Logical. Whether to compute network degree.
cluster_id: Logical. Whether to perform cluster analysis and record the cluster membership of each node. See addClusterMembership().
transitivity: Logical. Whether to compute node-level network transitivity using transitivity() with type = "local". The local transitivity of a node is the the number of triangles connected to the node relative to the number of triples centered on that node.
closeness: Logical. Whether to compute network closeness using closeness().
centrality_by_closeness: Logical. Whether to compute network centrality by closeness. The values are the entries of the res element of the list returned by centr_clo().
eigen_centrality: Logical. Whether to compute the eigenvector centrality scores of node network positions. The scores are the entries of the vector element of the list returned by eigen_centrality() with weights = NA. The centrality scores correspond to the values of the first eigenvector of the adjacency matrix for the cluster graph.
centrality_by_eigen: Logical. Whether to compute node-level network centrality scores based on eigenvector centrality scores. The scores are the entries of the vector element of the list returned by centr_eigen().
betweenness: Logical. Whether to compute network betweenness using betweenness().
centrality_by_betweenness: Logical. Whether to compute network centrality scores by betweenness. The scores are the entires of the res element of the list returned by centr_betw().
authority_score: Logical. Whether to compute the authority score using authority_score().
coreness: Logical. Whether to compute network coreness using coreness().
page_rank: Logical. Whether to compute page rank. The page rank values are the entries of the vector element of the list returned by page_rank().
all_stats: Logical. If TRUE, all other argument values are overridden and set to TRUE.

Details

These functions return a vector that can be passed to the stats_to_include argument of addNodeStats() (or buildRepSeqNetwork(), if node_stats = TRUE) in order to specify which node-level network properties to compute.

chooseNodeStats and exclusiveNodeStats each have default argument values suited to a different use case, in order to reduce the number of argument values that must be set manually.

chooseNodeStats has most arguments TRUE by default. It is best suited for including a majority of the available properties. It can be called with all_stats = TRUE to set all values to TRUE.

exclusiveNodeStats has all of its arguments set to FALSE by default. It is best suited for including only a few properties.

Value

A named logical vector with one entry for each of the function's arguments (except for all_stats). Each entry has the same name as the corresponding argument, and its value matches the argument's value.

References

Hai Yang, Jason Cham, Brian Neal, Zenghua Fan, Tao He and Li Zhang. (2023). NAIR: Network Analysis of Immune Repertoire. Frontiers in Immunology, vol. 14. doi: 10.3389/fimmu.2023.1181825

Webpage for the NAIR package

Author

Brian Neal (Brian.Neal@ucsf.edu)

Examples

set.seed(42)
toy_data <- simulateToyData()

net <- generateNetworkObjects(
  toy_data, "CloneSeq"
)

# Add default set of node properties
net <- addNodeStats(net)

# Modify default set of node properties
net <- addNodeStats(
  net,
  stats_to_include =
    chooseNodeStats(
      closeness = TRUE,
      page_rank = FALSE
    )
)

# Add only the spepcified node properties
net <- addNodeStats(
  net,
  stats_to_include =
    exclusiveNodeStats(
      degree = TRUE,
      transitivity = TRUE
    )
)

# Add all node-level network properties
net <- addNodeStats(
  net,
  stats_to_include = "all"
)