Description Usage Arguments Value Author(s) Examples

View source: R/GenePvalsFromEset.R

This is a wrapper function that enables direct generation of target-level p-values from a crispr screen.

1 2 3 | ```
ct.generateResults(fit, annotation, RRAalphaCutoff = 0.1,
permutations = 1000, contrast.term = NULL, scoring = c("combined",
"pvalue", "fc"), permutation.seed = NULL)
``` |

`fit` |
An object of class |

`annotation` |
An annotation file for the experiment. gRNAs are annotated by
row, and must minimally contain columns |

`RRAalphaCutoff` |
A cutoff to use when defining gRNAs with significantly altered abundance during the RRAa aggregation step, which may be specified
as a single numeric value on the unit interval or as a logical vector. When supplied as a logical vector (of length equal to Note that this function uses directional tests to identify enriched or depleted targets, and when RRAalphaCutoff is provided as a logical vector, only one of these hypotheses is implicitly specified; this means that enrichment and depletion cannot be . |

`permutations` |
The number of permutations to use during the RRAa aggregation step. |

`contrast.term` |
If a fit object with multiple coefficients is passed in, a string indiating the coefficient of interest. |

`scoring` |
The gRNA ranking method to use in RRAa aggregation. May take one of three values: |

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

A dataframe containing gRNA-level and target-level statistics. In addition to the information present in the supplied annotation object, the returned object indicates P-values and Q-values for the depletion and enrichment of each gRNA and associated target, the median log2 fold change estimate among all gRNAs associated with the target, and Rho statistics that are calculated internally by the RRAa algorithm that may be useful in ranking targets that are considered significant at a given alpha or false discovery threshold.

A 'resultsDF' formatted dataframe containing gene-level statistics.

Russell Bainer

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
data('fit')
data('ann')
output <- ct.generateResults(fit, ann, permutations = 10)
head(output)
p = seq(0, 1, length.out=20)
fc = seq(-3, 3, length.out=20)
fc[2] = NA
fc[3] = -20
stats = data.frame(
Depletion.P=p,
Enrichment.P=rev(p),
fc=fc
)
ct.applyAlpha(stats,scoring="combined")
``` |

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