random.walk {DTSEA} | R Documentation |
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 |
threshold |
The threshold used for RwR. The |
pt.post.processing |
The way to scale the |
pt.align |
The way to normalize the output |
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)