Description Usage Arguments Details Value Author(s) References Examples

View source: R/omnibusTest-functions.R

Graph theory approach associated with a permutation test to evaluate whether the number of associations is unexpectedly large.

1 | ```
graphTheory(genename, interactome, perm)
``` |

`genename` |
A vector a gene names that are associated with a particular phenotype |

`interactome` |
A binary matrix composed of genes (rows) and biological
complexes (columns) (see package |

`perm` |
Numeric, number of permutation run |

We form two distinct graphs where the set of nodes are the proteins (genes) in the organism. In one graph we create edges between genes if the two genes are members of one, or more, protein complexes. In the second graph we create an edge between all genes that are associated to a particular phenotype. We then construct a third graph on the same node set, but where there is an edge in this graph only if there is an edge in both of the first to graphs. We count the number of edges in the third and test by permutation whether the number of edges is unexpectedly large.

The returned value is a list with components:

`edgeCount` |
Number of associations observed between the genes that are linked to a particular phenotype and the given interactome. |

`edgeSimul` |
Number of associations if the genes that are linked to a particular phenotype are randomly distributed across the given interactome |

`p.value` |
Returned p.value |

R. Gentleman and N. LeMeur

Balasubramanian, R., LaFramboise, T., Scholtens, D., Gentleman, R. (2004) A graph-theoretic approach to testing associations between disparate sources of functional genomics data.Bioinformatics,20(18),3353-3362.

1 2 3 | ```
data(ScISI)
data(essglist)
ans <- graphTheory(names(essglist), ScISI, perm=3)
``` |

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