radial.ranking {GSAR} | R Documentation

## Radial Ranking of MST

### Description

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.

### Usage

    radial.ranking(object)

### Arguments

`object` | object of class `igraph` that consists of a minimum spanning tree or the union of multiple spanning trees.

### Details

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).

### Value

Numeric vector giving the radial node ranks in the MST or union of MSTs.

### 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

`HDP.ranking`, `RKStest`, `RMDtest`.

### Examples

    ## 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)

[Package GSAR version 1.21.0 Index]