| Title: | Statistically Validated Networks |
|---|---|
| Description: | Determines networks of significant synchronization between the discrete states of nodes; see Tumminello et al <doi:10.1371/journal.pone.0017994>. |
| Authors: | Damien Challet |
| Maintainer: | Damien Challet <[email protected]> |
| License: | GPL (>= 2.0) |
| Version: | 1.0.1 |
| Built: | 2024-10-25 03:31:08 UTC |
| Source: | https://github.com/cran/SVN |
Statistically validated networks are built from the states of nodes and from their curious (statistically speaking) synchronization: two nodes are linked if their states are anomalously synchronized. The associated null hypothesis is that the states of nodes are Poissonian processes, i.e., independent from each other.
The main function SVN_links expects a matrix/data.frame/data.table of states, the line number being the index and while a given column corresponds to a given node, and outputs the results network in an igraph format. A Multiple Hypothesis Testing correction is applied since one performs several (rather, many) tests. It is then up to the user to use some network clustering technique if needed. For the convenience of the user, the function SVN_clusters applies the infoMap or Louvain method to the output of SVN_links and returns the resulting clustering. For small enough timeseries, it may be useful to use the options(svn.memoise=TRUE) so as to avoid expensive calls to the phyper function.
Maintainer: Damen Challet <[email protected]>
Tumminello, M., Micciche, S., Lillo, F., Piilo, J., & Mantegna, R. N. (2011). Statistically validated networks in bipartite complex systems. PloS one, 6(3), e17994.
igraph
x=sample(c(1,0),1000,replace = TRUE) # random vector of 0s and 1s xx=x; x[1]=1-x[1] # one modifies just one element. x and xx are very similar x_rev=rev(x) # x and x_rev are not y=sample(c(1,0),1000,replace = TRUE) # y is another random vector of 0s and 1s M=cbind(x,xx,x_rev,y) # builds the matrix of states mylinks=SVN_links(M) print(mylinks) # one can compute clusters as well myclusters=SVN_clusters(mylinks)x=sample(c(1,0),1000,replace = TRUE) # random vector of 0s and 1s xx=x; x[1]=1-x[1] # one modifies just one element. x and xx are very similar x_rev=rev(x) # x and x_rev are not y=sample(c(1,0),1000,replace = TRUE) # y is another random vector of 0s and 1s M=cbind(x,xx,x_rev,y) # builds the matrix of states mylinks=SVN_links(M) print(mylinks) # one can compute clusters as well myclusters=SVN_clusters(mylinks)
Find clusters in Statistically Validated Networks
SVN_clusters(links, cluster.method = "infomap")SVN_clusters(links, cluster.method = "infomap")
links |
an object obtained from the SVN_links function |
cluster.method |
a string, either "infomap" or "louvain", that selects the clustering method |
an igraph communities object
M=matrix(rbinom(200,size = 1,0.5),nrow=50) mylinks=SVN_links(M) # no links, then print(mylinks) # another example x=c(1,0,0,0,0,0,1,1,1,1,0,0) xx=c(1,1,0,0,0,0,1,1,1,1,0,0) xrev=rev(x) w=sample(x,length(x)) M=rbind(x,xx,xrev,w) mylinks=SVN_links(M) ## if one wants clusters as well myclusters=SVN_clusters(mylinks)M=matrix(rbinom(200,size = 1,0.5),nrow=50) mylinks=SVN_links(M) # no links, then print(mylinks) # another example x=c(1,0,0,0,0,0,1,1,1,1,0,0) xx=c(1,1,0,0,0,0,1,1,1,1,0,0) xrev=rev(x) w=sample(x,length(x)) M=rbind(x,xx,xrev,w) mylinks=SVN_links(M) ## if one wants clusters as well myclusters=SVN_clusters(mylinks)
Statistical validated networks
SVN_links(states_vs_t, alpha = 0.01, MHT.correction = "bonferroni", exclude.states = NULL, states.pair.types = "all", alternative = "overexpression")SVN_links(states_vs_t, alpha = 0.01, MHT.correction = "bonferroni", exclude.states = NULL, states.pair.types = "all", alternative = "overexpression")
states_vs_t |
a matrix with time in lines and individual states in columns |
alpha |
the family-wise error rate in the case of Bonferroni multiple-hypothesis correction, or the false discovery rate in the case of the FDR multiple hypothesis correction |
MHT.correction |
the type of multiple hypothesis correction |
exclude.states |
a vector of states to remove from states_vs_t |
states.pair.types |
accepted value: "all", "same", "different": selects which kind of state pairs are tested: if there are two states e.g. (1,2), "all" allows (1,1), (2,2) and (1,2); "same" allows (1,1) and (2,2), while "different" allows (1,2) |
alternative |
a string either equal to "overexpression" or "underexpression" |
a data.table object of the node pairs significantly correlated at the alpha level. The columns are: link_id: a unique identifier for links; i and j are the node names; si and sj are their states; pv is the value associated to the link; threshold is the Multiple Hypothesis Testing-adjusted p-value threshold (only links with pv<=threshold are kept)
x=sample(c(1,0),1000,replace = TRUE) xx=x; x[1]=1-x[1] # one modifies just one element x_rev=rev(x) y=sample(c(1,0),1000,replace = TRUE) M=cbind(x,xx,x_rev,y) mylinks=SVN_links(M) print(mylinks) # one can compute clusters as well myclusters=SVN_clusters(mylinks)x=sample(c(1,0),1000,replace = TRUE) xx=x; x[1]=1-x[1] # one modifies just one element x_rev=rev(x) y=sample(c(1,0),1000,replace = TRUE) M=cbind(x,xx,x_rev,y) mylinks=SVN_links(M) print(mylinks) # one can compute clusters as well myclusters=SVN_clusters(mylinks)