Description Usage Arguments Details Value Author(s) References See Also Examples
Find the union of the first and second minimum spanning trees for protein-protein interaction (PPI) networks.
1 | findMST2.PPI(object, return.MST2only=TRUE)
|
object |
an object of class |
return.MST2only |
logical. If |
This function produces the union of the first and second minimum
spanning trees (MSTs) as an igraph
object (check package
igraph
for details). It can as well return the first and
second minimum spanning trees when return.MST2only
is FALSE
.
For the graph G(V,E) where V is the set of vertices and E is the set of edges, the first MST is defined as the acyclic subset T_{1} \subseteq E that connects all vertices in V and whose total length ∑_{i,j \in T_{1}} d(v_{i},v_{j}) is minimal (Rahmatallah et. al. 2014). The second MST is defined as the MST of the reduced graph G(V,E-T_{1}). The union of the first and second MSTs is denoted as MST2.
It was shown in Zybailov et. al. 2016 that MST2 can be informative as a graphical visualization tool in deciphering the properties of protein-protein interaction (PPI) networks by highlighting the minimum set of essential interactions among proteins. Most influential proteins with many interactions tend to occupy central position and have relatively high connectivity degree in the MST2 because the shortest paths connecting the vertices of the first and second MSTs tend to pass through the verteces corresponding to these proteins. In contrast, proteins with few interactions most likely occupy non-central positions in the MST2 and have a degree of 2.
If return.MST2only=TRUE
(default), function findMST2.PPI
returns an object of class igraph
representing the MST2. If
return.MST2only=FALSE
, function findMST2.PPI
returns a list
of length 3 with the following components:
MST2 |
an object of class |
first.mst |
an object of class |
second.mst |
an object of class |
Yasir Rahmatallah and Galina Glazko
Zybailov B., Byrd A., Glazko G., Rahmatallah Y. and Raney K. (2016) Protein-protein interaction analysis for functional characterization of helicases. Methods, 108, 56–64.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ## generate a random undirected graph with power-law
## distribution degree where minimum degree is 4 and
## maximum degree is 100
set.seed(123)
degs <- sample(c(4:100), 100, replace=TRUE, prob=c(4:100)^-2)
if(floor(sum(degs)/2) != (sum(degs)/2)) degs[1] <- degs[1] + 1
randomGraph <- sample_degseq(degs, method="vl")
## find MST2 of the random graph and highlight vertices
## with degree greater than 10 with red color
mst2.ppi <- findMST2.PPI(object=randomGraph, return.MST2only=TRUE)
degs <- degree(mst2.ppi)
ind <- which(degs > 10)
V(mst2.ppi)$color <- "yellow"
V(mst2.ppi)$color[ind] <- "red"
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.