pValue_GO {GOexpress} | R Documentation

## Compute p-values for ontologies by randomising gene labels.

### Description

Permutes the rank of genes processed by `GO_analyse()` to compute a P-value for each ontology

### Usage

    pValue_GO(
        result, N=1000, ranked.by=result$rank.by, rank.by='P',
        FUN.GO=result$FUN.GO)

### Arguments

`result` | The output of `GO_analyse()` or a subset of it obtained from `subset_scores()`.
`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 `result` object.
`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 `GO_analyse`. If using "lambda-like" (anonymous) functions, these must take a list of numeric values as an input, and return a single numeric value as an output.

### Details

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.

### Value

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.

### Author(s)

Kevin Rue-Albrecht

### See Also

Method `GO_analyse`.

### Examples

    ## 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)

[Package GOexpress version 1.18.0 Index]