Description Usage Arguments Details Value Examples

Evaluates a set of gene modules (e.g., the output from
`predictModules`

) by checking them for enrichment of gene
annotations.

1 | ```
checkModules(x, a, p = 0.05, return.p.matrix = FALSE)
``` |

`x` |
a binary matrix indicating module membership (genes in rows, modules in columns) |

`a` |
a binary gene annotation matrix (genes in rows, annotations in columns) |

`p` |
FDR-adjusted p-value below which to consider an annotation significant |

`return.p.matrix` |
logical value indicating whether the full p-value matrix should be returned in the output |

This function evaluates a set of predicted gene modules by testing whether
each module is enriched for any gene annotations. Enrichment p-values are calculated using
hypergeometric statistics (see `phyper`

). If x is not a binary matrix,
module membership is determined using `partition`

with default
paramaters. The FDR-correction (see `p.adjust`

) is applied to all
p-values prior to determining signficance.

A list with the following elements:

- num.annotations
Number of annotations tested (number of columns in a)

- num.modules
Number of modules tested (number of columns in x)

- anns.signif
Number of annotations that were significant in at least one module

- mods.signif
Number of modules that were significant for at least one annotation

- p.matrix
The module-by-annotation p-value matrix (if return.p.matrix == TRUE)

1 2 3 4 5 6 7 | ```
x = matrix(rnorm(100), 10, 10)
a = matrix(sample(c(0,1), size = 100, replace = TRUE), 10, 10)
m = predictModules(x)$S
rownames(a) = rownames(m) = as.character(1:10)
m.p = partition(m, t = 2)
m.p2 = splitMatrix(m.p)
checkModules(m.p2, a)
``` |

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