random.walk: Function to implement Random Walk with Restart (RwR)...

In DTSEA: Drug Target Set Enrichment Analysis

Function to implement Random Walk with Restart (RwR) algorithm on the input graph

Description

Function random.walk is supposed to implement the original Random Walk with Restart (RwR) on the input graph. If the seeds (i.e., a set of starting nodes) are given, it intends to calculate the affinity score of all nodes in the graph to the seeds.

Usage

random.walk(
  network,
  p0,
  edge_weight = FALSE,
  gamma = 0.7,
  threshold = 1e-10,
  pt.post.processing = "log",
  pt.align = "median",
  verbose = FALSE
)

Arguments

network	The input graph object. It should be either an igraph object or an edge list matrix / data frame.
p0	The starting vector on time t0.
edge_weight	Logical to indicate whether the input graph contains weight information.
gamma	The restart probability used for RwR. The gamma takes the value from 0 to 1, controlling the probability that a node would go back to its starting node.
threshold	The threshold used for RwR. The threshold indicates the stabilization status, which is a stopping criterion of RwR.
pt.post.processing	The way to scale the pt vector. It can be 'none', 'zscore', and 'log'.
pt.align	The way to normalize the output pt vector. It can be mean to manually cut the up- and down-regulated genes, median to avoid the influence of the distribution shape, or none for no normalization.
verbose	Show the progress of the calculation.

Value

pt vector

Examples

library(DTSEA)

# Load the data
data("example_disease_list", package = "DTSEA")
data("example_drug_target_list", package = "DTSEA")
data("example_ppi", package = "DTSEA")

# Perform random walk
p0 <- calculate_p0(nodes = example_ppi, disease = example_disease_list)
pt <- random.walk(network = example_ppi, p0 = p0)

# Perform GSEA analysis
# ....

# If you have obtained the supplemental data, then you can do random walk
# with restart in the real data set

# supp_data <- get_data(c("graph", "disease_related", "example_ppi"))
# p0 <- calculate_p0(nodes = supp_data[["graph"]],
#                    disease = supp_data[["disease_related"]])
# pt <- random.walk(network = supp_data[["example_ppi"]],
#                   p0 = p0)

DTSEA documentation built on Nov. 10, 2022, 5:09 p.m.