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

View source: R/post_analysis.R

Permutes the rank of genes processed by `GO_analyse()`

to compute
a P-value for each ontology

1 2 3 | ```
pValue_GO(
result, N=1000, ranked.by=result$rank.by, rank.by='P',
FUN.GO=result$FUN.GO)
``` |

`result` |
The output of |

`N` |
The number of permutation desired. Default to 1000. |

`ranked.by` |
Either of 'rank' or 'score'. The metric used to compare whether GO terms
in the randomised data arelative to their original value. Default to the
current ordering method of the |

`rank.by` |
Either of 'rank', 'score', or 'p.val'. The metric used to order the GO terms after computing of the P-value. Default to 'p.val'. |

`FUN.GO` |
Function to summarise the score and rank of all feature associated with
each gene ontology. Logically, default is the function used in the call
to |

This function is relatively lengthy. Its procedure can be divided in several steps: (1) assemble a copy of all genes used in the original scoring step, then for each of the N permutations, (2) permute the gene labels, (3) aggregate the rank – or score – of all genes belonging to each GO term, (4) decide whether each GO term is ranked – or scored –better or worst than originally, (5) return a p-value based on the number of permutations where each GO term was better ranked – or scored – than originally.

A list formatted identically to the results of the analysis, with an added 'p.val' column in the GO slot, and re-ordered by the chosen metric.

Kevin Rue-Albrecht

Method `GO_analyse`

.

1 2 3 4 5 6 7 8 9 | ```
## Not run:
# Time-consuming examples:
# Limited here to N=1 for time constraint
# Recommended N=1000 (or at least 100).
# Run the analysis on factor "Treatment" including all samples
AlvMac.Pvals <- pValue_GO(result, N=1, ranked.by=result$rank.by, rank.by='P')
## End(Not run)
``` |

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