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Diffuse seed genes in a tied manner over a network

tied_diffusion.Rd

This function takes two sets of diffusion vector and leverages tied diffusion to identify an intersection of influential nodes. If the network is undirected, the method will run two personalised page rank diffusions based on the diffusion vectors and generate the score aggregation

Usage

tied_diffusion(
  object,
  diffusion_vector_1,
  diffusion_vector_2,
  summarisation = c("max", "mean", "harmonic_sum"),
  score_aggregation = c("min", "max", "mean"),
  .verbose = FALSE
)

Arguments

object

NetworkDiffusions object. The underlying class NetworkDiffusions().

diffusion_vector_1

Named numeric. The first named vector with values to use for the reset parameter in the personalised page-rank diffusion. Names should represent node names of the graph.

diffusion_vector_2

Named numeric. The second named vector with values to use for the reset parameter in the personalised page-rank diffusion. Names should represent node names of the graph.

summarisation

String. If there are duplicated names in the diffusion_vector how to summarise these.

score_aggregation

String. How to summarise the tied scores.

.verbose

Boolean. Controls verbosity of the function.

Value

The class with added diffusion score based on a two sets of seed genes. Additionally, the seed genes are stored in the class.