birewire.sampler.undirected: Efficient generation of a null model for a given undirected...

Description Usage Arguments Details Author(s) References

View source: R/BiRewire.R


The routine samples correctly from the null model of a given undirected graph creating a set of randomized version of the initial undirected graph.


birewire.sampler.undirected(adjacency,K,path,max.iter="n", accuracy=0.00005,



Adjacency matrix of the initial undirected graph. Since 3.6.0 this matrix can contain also NAs and the position of such entries will be preserved by the SA;


The number of networks that has to be generated;


The directory in which the routine stores the outputs;


"n" (default) see birewire.rewire.undirected for references


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;


TRUE (default). When TRUE a progression bar is printed during computation.


10 (default). If exact==TRUE in order to prevent a possible infinite loop the program stops anyway after MAXITER_MUL*max.iter iterations;


FALSE (default). If TRUE the program performs max.iter swithcing steps, otherwise the program will count also the not-performed swithcing steps;


TRUE (default). If FALSE the table is written as an R data.frame (long time and more space needed)


The routine creates, starting from the given path, different subfolders in order to have maximum 1000 files for folder . Moreover the incidence matrices are saved using write_stm_CLUTO (sparse matrices) that can be loaded using read_stm_CLUTO. The set is generated calling birewire.rewire.undirected on the last generated graph starting from the input graph.


Andrea Gobbi: <>


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.

Gobbi, A. and Jurman, G. (2013) Theoretical and algorithmic solutions for null models in network theory (Doctoral dissertation)

R. Milo, N. Kashtan, S. Itzkovitz, M. E. J. Newman, U. Alon (2003), On the uniform generation of random graphs with prescribed degree sequences, eprint arXiv:cond-mat/0312028

BiRewire documentation built on Nov. 8, 2020, 8:09 p.m.