birewire.visual.monitoring.dsg {BiRewire} | R Documentation |
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 Rtsne
). If display is set to TRUE the relative plot is displayed.
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)
data |
The initial dsg either in matrix or graph formulation 9see |
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 |
sequence.pos |
c(1,5,100,"n")(default) the sequence of step for wich generating a sampler (see |
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. |
See birewire.visual.monitoring.bipartite
for more details.
A list containing the list containing the distance matrices dist and the list containing the tsne results Rtsne.
Andrea Gobbi
Maintainer: Andrea Gobbi <gobbi.andrea@mail.com>
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
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) tsne = birewire.visual.monitoring.dsg(dsg,exact=TRUE,sequence.pos=c(1,2,"n",100), sequence.neg=c(1,2,"n",60),n.networks=50,perplexity=1)