sig.net: Significant Network

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

Description

Significance of Bipartite networks

Usage

sig.net(data, g, Amatrix, num.permutation = 10, adjp.cutoff = 0.05,
  p.adjust.method = c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY",
  "fdr"), parallel = TRUE, multicores = NULL, verbose = T)

Arguments

data	n x m Adjancency matrix of seeds or dataframe of pairs.
g	igrah object of bipartite indcident adjacency matrix.
Amatrix	Affinity matrix computed from biNetWalk or uNetWalk.
num.permutation	number of permutation of Affinity matrix needed to performed.
adjp.cutoff	pvalue cutoff 0.05
p.adjust.method	Adjusting the pvalue by diiferent method.It uses method from the stats package.
parallel	Using parallization either True or False
multicores	If parallisation is set TRUE number of cores to perform parallel computaion.
verbose	For verbose output.

Examples

data(Enzyme)
A <- enzyme_ADJ
S1 = enzyme_Gsim
S2 = enzyme_Csim
g1 = graph.incidence(A)
Q = biNetwalk(g1,s1=S1,s2=S2,normalise="laplace", dataSeed=NULL, file=NULL,restart=0.8,verbose=T)
Z = sig.net(data=A,g=g1,Amatrix=Q,num.permutation=100,adjp.cutoff=0.01,p.adjust.method="BH",parallel=FALSE,verbose=T)

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