build_network: Construct a PPI network from input genes and InnateDB's...

In travis-m-blimkie/networker: Reproducible PPI Network Creation and Visualization in R

Construct a PPI network from input genes and InnateDB's database

Description

Construct a PPI network from input genes and InnateDB's database

Usage

build_network(
  df,
  col,
  order,
  hub_measure = "betweenness",
  ppi_data = innatedb_exp,
  seed = 1
)

Arguments

df	Input data frame containing genes of interest
col	Column of input genes as Ensembl IDs (character)
order	Desired network order. Possible options are "zero" (default), "first," "min_simple," or "min_steiner."
hub_measure	Character denoting what measure should be used in determining which nodes to highlight as hubs when plotting the network. Options include "betweenness" (default), "degree", and "hubscore". These represent network statistics calculated by their respective tidygraph::centrality_x, functions, specifically degree, betweenness, and hub_score.
ppi_data	Data frame of PPI data; must contain rows of interactions as pairs of Ensembl gene IDs, with columns named "ensembl_gene_A" and "ensembl_gene_B". Defaults to pre-packaged InnateDB PPI data.
seed	Number used in call to set.seed() to allow for reproducible network generation

Details

The "min_steiner" method is implemented with the SteinerNet package.

The "hub_measure" argument determines how build_network assesses connectedness of nodes in the network, which will be used to highlight nodes when visualizing with plot_network. The options are "degree", "betweenness", or "hubscore". This last option uses the igraph implementation of the Kleinburg hub centrality score - details on this method can be found at ?igraph::hub_score.

Value

tidygraph object for plotting or further analysis

References

See https://cran.r-project.org/web/packages/SteinerNet/index.html for details on the Steiner network trimming.

See Also

https://www.github.com/travis-m-blimkie/networker

travis-m-blimkie/networker documentation built on June 3, 2023, 10:17 p.m.