uNetwalk: Perform Random walk on a Unipartite Network

In abhik1368/netpredictor: Algorithms to search for missing link in a graph.

Description

Peforms random walk with restart with preferred seed sets. If seed sets are not given then the adjacencny matrix is taken as the input as the input seed sets. THe restart parameter controls the random walk probability . This can be changed default is set to 0.8. Normalization of the matrix can be done by row,column,laplacian. For faster computation Parallalization is implemented with multicores. Parallization is done using foreach package.

Usage

uNetwalk(ig, normalise = c("row", "column", "laplacian", "none"),
  dataSeed = NULL, restart = 0.8, parallel = TRUE, multicores = NULL,
  verbose = T)

Arguments

ig	igraph object
normalise	normalise method
dataSeed	vector or dataframe
restart	restart probability parameter
parallel	to execute in parallel either TRUE or FALSE
multicores	Number of cores to be used when running in parallel
verbose	Verbose output

References

• Kohler S, et al. Walking the Interactome for Prioritization of Candidate Disease Genes. American Journal of Human Genetics. 2008;82:949–958.

• Can, T., Camoglu, O., and Singh, A.K. (2005). Analysis of protein-protein interaction networks using random walks. In BIOKDD '05: Proceedings of the 5th international workshop on Bioinformatics (New York, USA: Association for Computing Machinery). 61–68

Examples

# generate a random graph according to the ER model
library(igraph)
library(netpredictor)
g1 <- upgrade_graph(erdos.renyi.game(100, 1/100))
V(g1)$name <- seq(1,100,1)
## Computing RWR
pM <- uNetwalk(g1,normalise="laplacian", restart=0.75, parallel=FALSE)
## Settin the seed nodes.
d1 <- c(1,0,1,0,1)
d2 <- c(0,0,1,0,1)
dataSeed <- data.frame(d1,d2)
rownames(dataSeed) <- 1:5
pM <- uNetwalk(g1, normalise="laplacian", dataSeed=dataSeed, restart=0.8,
               parallel=FALSE,multicores=NULL, verbose=T)

abhik1368/netpredictor documentation built on May 10, 2019, 4:09 a.m.