birewire.rewire.bipartite.and.projections: Analysis and rewiring function processing a bipartite graphs...
In BiRewire: High-performing routines for the randomization of a bipartite graph (or a binary event matrix), undirected and directed signed graph preserving degree distribution (or marginal totals)

Description Usage Arguments Details Value Author(s) References Examples

This function performs the same analysis of birewire.analysis.bipartite but additionally it provides in output a rewired version of the two networks resulting from the natural projections of the initial graph, together with the corresponding Jaccard index trends.

1 2	birewire.rewire.bipartite.and.projections(graph,step=10,max.iter="n", accuracy=0.00005,verbose=TRUE,MAXITER_MUL=10)

`graph`	A bipartite graph g;
`max.iter`	"n" (default) the number of successful switching steps to be performed. If equal to "n" then this number is considered equal to the analytically derived lower bound N={e(1-d)}/{2} \ln{((e-de)/δ)} presented in Gobbi et al. (see References);
`step`	10 (default): the interval (in terms of switching steps) at which the Jaccard index between g and the its current rewired version is computed;
`accuracy`	0.00005 (default) is the desired level of accuracy reflecting the average distance between the Jaccard index at the N-th step and its analytically derived fixed point in terms of fracion of common edges;
`verbose`	TRUE (default) boolean value. If TRUE print a processing bar during the rewiring algorithm.
`MAXITER_MUL`	10 (default).Since N indicates the number of successful switching steps, in order to prevent a possible infinite loop the program stops anyway after MAXITER_MUL*max.iter iterations ;

See birewire.analysis.bipartite for details.

A list containing the three sequences of Jaccard index values (similarity_scores, similarity_scores.proj1, similarity_scores.proj2) for the three resulting graphs respectively (rewired, rewired.proj1, rewired.proj2). The first one is the rewired version of the initial graph g, while the second and the third one are rewired versions of its natural projections.

Andrea Gobbi
Maintainer: Andrea Gobbi <gobbi.andrea@mail.com>

Gobbi, A. and Iorio, F. and Dawson, K. J. and Wedge, D. C. and Tamborero, D. and Alexandrov, L. B. and Lopez-Bigas, N. and Garnett, M. J. and Jurman, G. and Saez-Rodriguez, J. (2014) Fast randomization of large genomic datasets while preserving alteration counts Bioinformatics 2014 30 (17): i617-i623 doi: 10.1093/bioinformatics/btu474.

Iorio, F. and and Bernardo-Faura, M. and Gobbi, A. and Cokelaer, T.and Jurman, G.and Saez-Rodriguez, J. (2016) Efficient randomization of biologicalnetworks while preserving functionalcharacterization of individual nodes Bioinformatics 2016 1 (17):542 doi: 10.1186/s12859-016-1402-1.

library(igraph)
library(BiRewire)
g <- simplify(graph.bipartite( rep(0:1,length=100),
c(c(1:100),seq(1,100,3),seq(1,100,7),100,seq(1,100,13),
seq(1,100,17),seq(1,100,19),seq(1,100,23),100)))
##gets the incidence matrix of g
 m<-as.matrix(get.incidence(graph=g))
## rewires g and its projections
result=birewire.rewire.bipartite.and.projections(g,step=10,max.iter="n",accuracy=0.00005)