HDP.ranking: High Directed Preorder Ranking of MST

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/HDP.ranking.R

Description

Rank nodes in an object of class igraph (see package igraph for the definition of class igraph) containing a minimum spanning tree (MST) according to the High Directed Preorder traversal of the tree.

Usage

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HDP.ranking(object)

Arguments

object

object of class igraph that consists of a minimum spanning tree.

Details

Rank nodes in an object of class igraph (see package igraph) containing a minimum spanning tree (MST). The MST is rooted at a node with the largest geodesic distance and the rest of the nodes are ranked according to the high directed preorder (HDP) traversal of the tree (Friedman and Rafsky 1979).

Value

Numeric vector giving the node ranks according to HDP traversal of the MST.

Note

This function does not work properly if there is any node in the MST with more than 26 links. However, this situation is almost impossible for a dataset composed of a few hundreds or less of samples.

Author(s)

Yasir Rahmatallah and Galina Glazko

References

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.

See Also

radial.ranking, KStest, MDtest.

Examples

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## 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)
HDP.ranks <- HDP.ranking(MST)

GSAR documentation built on May 2, 2018, 2:35 a.m.