This is a wrapper function implementing the RRAalpha p-value aggregation algorithm. Takes in a set of gRNA rank scores (formatted as a single-column numeric matrix with row.names indicating the guide names) and a list object of gRNA annotations (names are the gene targets, and each element of the list contains a vector of the corresponding guide names). The rank scores are converted to gene-level statistics that are thenm transformed into empirical p-values by permutation.

1 2 | ```
ct.RRAaPvals(p, g.key, alpha, permute, multicore = TRUE, core.perm = 100,
permutation.seed = NULL)
``` |

`p` |
A single column matrix of ranking scores, with row.names indicating the gRNA labels |

`g.key` |
An annotation data frame of gRNAs, minimally containing a factorized "geneSymbol" column indicating the target names. This is typically generated by calling the |

`alpha` |
The alpha cutoff parameter, corresponding to the P-value threshold or fold change proportion at which gRNAs should no longer be considered to be
differentially expressed. Alternatively, this can be provided as a logical vector of the same length as the number of rows in |

`permute` |
Number of permutations to be used during empirical p-value estimation. In a multicore context the exact number of permutations may vary somewhat to accomodate the corresponding system archetecture but should be close to the specified permutation number. |

`multicore` |
Logical indicating whether to use multiple cores to calculate p-values. |

`core.perm` |
Maximum number of permutations to run on each core (only relevant when |

`permutation.seed` |
numeric seed for permutation reproducibility.
Default: |

A named list of target-level empirical P-values.

Russell Bainer

1 2 3 4 | ```
data('fit')
data('ann')
genePvals <- ct.RRAaPvals(fit$p.value, ann, alpha = 0.1, permute = 100, multicore = FALSE)
genePvals <- ct.RRAaPvals(fit$p.value, ann, alpha = 0.1, permute = 100, multicore = TRUE, core.perm = 10)
``` |

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