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

View source: R/radial.ranking.R

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.

1 | ```
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`

.

1 2 3 4 5 6 7 8 9 10 11 | ```
## 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)
``` |

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