ct.seas {gCrisprTools} | R Documentation

## Geneset Enrichment within a CRISPR screen using 'sparrow'

### Description

This function is a wrapper for the 'sparrow::seas()' function, which identifies differentially enriched/depleted ontological categories within the hits identified by a pooled screening experiment, given a provided 'GenseSetDb()' object and a list of results objects created by 'ct.generateResults()'. By default testing is performed using 'fgsea' and a hypergeometric test ('sparrow::ora()'), and results are returned as a 'SparrowResult' object.

This function will attempt to coerce them into inputs appropriate for the above analyses via 'ct.seasPrep()', after checking the relevant parameters within the provided 'GeneSetDb'. This is generally easier than going through the individual steps yourself, especially when the user is minimally postprocessing the contrast results in question.

Sometimes, it can be useful to directly indicate the set of targets to be included in an enrichment analysis (e.g., if you wish to expand or contract the set of active targets based on signal validation or other secondary information about the experiment). To accomodate this use case, users may include a logical column in the provided result object(s) indicating which elements should be included among the positive signals exposed to the test.

Note that many pooled libraries specifically target biased sets of genes, often focusing on genes involved in a particular pathway or encoding proteins with a shared biological property. Consequently, the enrichment results returned by this function represent the disproportionate enrichment or depletion of targets annotated to pathways *within the context of the screen*, and may or may not be informative of the underlying biology in question. This means that pathways not targeted by a library will obviously never be enriched a positive target set regardless of their biological relevance, and pathways enriched within a focused library screen are similarly expected to partially reflect the composition of the library and other confounding issues (e.g., number of targets within a pathway). Analysts should therefore use this function with care. For example, it might be unsurprising to detect pathways related to histone modification within a screen employing a crispr library primarily targeting epigenetic regulators.

### Usage

    ct.seas(dflist, gdb, as.dfs = FALSE, active = "replicated", ...)

### Arguments

`dflist` | A result object created by 'ct.generateResults()', or a named list containing many of them; will be passed as a list to 'ct.seasPrep()' with the associated '...' arguments.
`gdb` | A 'GenseSetDb' object containing annotations for the targets specified in 'result'.
`as.dfs` | Logical indicating whether to return the various contrast statistics as in-register lists of data.frames to facilitate comparisons.
`active` | Name of a column in the supplied result(s) that should be used to indicate active/selected targets.
`...` | Additional arguments to pass to 'ct.seasPrep()' or 'sparrow::seas()'.

### Value

A named list of 'SparrowResults' objects.

### Author(s)

Steve Lianoglou for seas; Russell Bainer for GeneSetDb processing and wrapping functions.

### Examples

    data('resultsDF')
    gdb <- sparrow::getMSigGeneSetDb(collection = 'h', species = 'human', id.type = 'entrez')
    ct.seas(list('longer' = resultsDF, 'shorter' = resultsDF[1:10000,]), gdb)

[Package gCrisprTools version 2.0.0 Index]