biNetwalk: Bipartite Random Walk

Description Usage Arguments References Examples

Description

Randomm walk with restart on Bipartite networks

Usage

1
2
biNetwalk(g1, s1, s2, normalise = c("laplace", "none", "chen"),
  dataSeed = NULL, restart = 0.8, verbose = T, weight = FALSE)

Arguments

g1

Bipartite graph igraph object.

s1

Accepts a matrix object of similarity scores for targets.

s2

Accepts a matrix object similarity scores for compounds.

normalise

Normalisation of matrix using laplacian, Chen, None(the transition matrix will be column normalized)

dataSeed

seeds file

restart

restart value

weight

if we want to use a weighted network . Options are either TRUE or FALSE.

References

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
## Not run: 
data(Enzyme)
A <- enzyme_ADJ
S2 = enzyme_Csim
S1 = enzyme_Gsim
g1 = graph.incidence(A)
M3 <- biNetwalk(g1,s1=S1,s2=S2,normalise="laplace", dataSeed=NULL,restart=0.8,
                parallel=FALSE, verbose=T,weight=FALSE)
dataF<- read.csv("seedFile.csv",header=FALSE)
Mat <- biNetwalk(g1,s1=S1,s2=S2,normalise="laplace", dataSeed=dataF,restart=0.8,
                 parallel=TRUE,verbose=T,weight=FALSE)

## End(Not run)

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