radial.ranking {GSAR} | R Documentation |
Rank vertices in an object of class igraph
(see package
igraph
for the definition of class igraph
) that consists
of a minimum spanning tree (MST) or the union of multiple MSTs radially
such that vertices with higher depth and distance from the centroid are
given higher ranks.
radial.ranking(object)
object |
object of class |
Rank nodes in an object of class igraph
(see package
igraph
) that consists of a minimum spanning tree (MST) or
the union of multiple MSTs radially. The MST is rooted at the node of
smallest geodesic distance (centroid) and nodes with largest depths
from the root are assigned higher ranks. Hence, ranks are increasing
radially from the root of the MST (Friedman and Rafsky 1979).
Numeric vector giving the radial node ranks in the MST or union of MSTs.
Yasir Rahmatallah and Galina Glazko
Rahmatallah Y., Emmert-Streib F. and Glazko G. (2012) Gene set analysis for self-contained tests: complex null and specific alternative hypotheses. Bioinformatics 28, 3073–3080.
Friedman J. and Rafsky L. (1979) Multivariate generalization of the Wald-Wolfowitz and Smirnov two-sample tests. Ann. Stat. 7, 697–717.
HDP.ranking
, RKStest
, RMDtest
.
## generate random data using normal distribution ## generate 20 features in 20 samples object <- matrix(rnorm(400),20,20) objt <- aperm(object, c(2,1)) ## calculate the weight matrix Wmat <- as.matrix(dist(objt, method = "euclidean", diag = TRUE, upper = TRUE, p = 2)) ## create a weighted undirectional graph from the weight matrix gr <- graph_from_adjacency_matrix(Wmat, weighted = TRUE, mode = "undirected") ## find the minimum spanning tree MST <- mst(gr) radial.ranks <- radial.ranking(MST)