birewire.visual.monitoring.dsg: Visual monitoring of the Markov chain underlying the SA for...

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

This function generates a cascade-sampling from the model at different switching steps given in sequence. For each step the routine computes the pairwise Jaccard distance (1-JI) among the samples and perfroms, on the resulting matix, a dimentional scaling reduction (using tsne). If display is set to TRUE the relative plot is displayed.

Usage

birewire.visual.monitoring.dsg(data,accuracy=0.00005,verbose=FALSE,MAXITER_MUL=10,exact=FALSE,n.networks=100,perplexity=15,
  sequence.pos=c(1,5,100,"n"),
  sequence.neg=c(1,5,100,"n"),ncol=2,nrow=length(sequence.pos)/ncol,display=TRUE)

Arguments

data	The initial dsg either in matrix or graph formulation 9see birewire.induced.bipartite.
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). When TRUE a progression bar is printed during computation.
MAXITER_MUL	10 (default). If exact==TRUE in order to prevent a possible infinite loop the program stops anyway after MAXITER_MUL*max.iter iterations;
exact	FALSE (default). If TRUE the program performs max.iter swithcing steps, otherwise the program will count also the not-performed swithcing steps;
n.networks	100 (default): the number of network generated for each step defined in sequence ;
perplexity	15 (default): the value of perplexity passed to the function tsne;
sequence.pos	c(1,5,100,"n")(default) the sequence of step for wich generating a sampler (seebirewire.sampler.dsg) for the positive part of data
sequence.neg	same as sequence.pos but for the negative part
ncol	2 (default). The number of column in the plot;
nrow	length(sequence)/ncol (default). The number of row in the plot;
display	TRUE (default). If TRUE the result of tsne is displayed.

Details

See birewire.visual.monitoring.bipartite for more details.

Value

A list containing the list containing the distance matrices dist and the list containing the tsne results tsne.

Author(s)

Andrea Gobbi
Maintainer: Andrea Gobbi <[e-mail]>

References

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 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.

Jaccard, P. (1901), Étude comparative de la distribution florale dans une portion des Alpes et des Jura, Bulletin de la Société Vaudoise des Sciences Naturelles 37: 547–579.

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

Van der Maaten, L.J.P. and Hinton, G.E. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579-2605, 2008

Examples

library(BiRewire)
data(test_dsg)
##bigger dsg
test_dsg_2=test_dsg
test_dsg_2[,1]=paste(test_dsg_2[,1],"_",sep="")
test_dsg_2[,3]=paste(test_dsg_2[,3],"_",sep="")

dsg <-  birewire.induced.bipartite(rbind(test_dsg,test_dsg_2),sparse=FALSE)

 a=birewire.visual.monitoring.dsg(dsg,exact=TRUE,sequence.pos=c(1,2,"n",100),
				sequence.neg=c(1,2,"n",60),n.networks=50)

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