shortestPath {GOstats} | R Documentation

## Shortest Path Analysis

### Description

The shortest path analysis was proposed by Zhou et. al. The basic computation is to find the shortest path in a supplied graph between two LocusLink IDs. Zhou et al claim that other genes annotated along that path are likely to have the same GO annotation as the two end points.

### Usage

    shortestPath(g, GOnode)

### Arguments

`g` | An instance of the `graph` class.
`GOnode` | A length one character vector specifying the GO node of interest.

### Details

The algorithm implemented here is quite simple. All LocusLink identifiers that are annotated at the GO node of interest are obtained. Those that are found as nodes in the graph are retained and used for the computation. For every pair of nodes at the GO term the shortest path between them is computed using `sp.between` from the RBGL package.

There is a presumption that the graph is `undirected`. This restriction could probably be lifted if there was some reason for it - a patch would be gratefully accepted.

### Value

The return values is a list with the following components:

`shortestpaths` | A list of the ouput from `sp.between`. The names are the names of the nodes used as the two endpoints
`nodesUsed` | A vector of the LocusLink IDs that were both found at the GO term of interest and were nodes in the supplied graph, `g`. These were used to compute the shortest paths.
`nodesNotUsed` | A vector of LocusLink IDs that were annotated at the GO term, but were not found in the graph `g`.

### Author(s)

R. Gentleman

### References

Transitive functional annotation by shortest-path analysis of gene expression data, by X. Zhou and M-C J. Kao and W. H. Wong, PNAS, 2002

### See Also

`sp.between`

### Examples

    library(GO)
    library(RBGL)

    tst <- unique(unlist(mget(c("GO:0005778", "GO:0005779",
                                "GO:0030060"), GOGO2LL)))
    set.seed(123)
    v1 <- randomGraph(tst, 1:10, .3)

    a1 <- shortestPath(v1, "GO:0005779")

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