# Suite of Functions for Pooled Crispr Screen QC and Analysis

## Documentation for package ‘gCrisprTools’ version 1.16.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

gCrisprTools-package | gCrisprTools
aln | Precalculated alignment statistics of a crispr screen
ann | Annotation file for a mouse Crispr library
ct.alignmentChart | View a Barchart Summarizing Alignment Statistics for a Crispr Screen
ct.applyAlpha | Apply RRA 'alpha' cutoff to RRAalpha input
ct.buildSE | Package Screen Data into a 'SummarizedExperiment' Object
ct.CAT | Compare Two CRISPR Screens via a CAT plot
ct.DirectionalTests | Compute Directional P-values from eBayes Output
ct.filterReads | Remove low-abundance elements from an ExpressionSet object
ct.GCbias | Visualization of gRNA GC Content Trends
ct.generateResults | Calculate results of a crispr screen from a contrast
ct.GREATdb | Update a gene-centric msdb object for GREAT-style enrichment analysis using a specified CRISPR annotation.
ct.gRNARankByReplicate | Visualization of Ranked gRNA Abundances by Replicate
ct.guideCDF | View CDFs of the ranked gRNAs or Targets present in a crispr screen
ct.inputCheck | Check compatibility of a sample key with a supplied object
ct.makeContrastReport | Generate a Contrast report from a pooled CRISPR screen
ct.makeQCReport | Generate a QC report from a pooled CRISPR screen
ct.makeReport | Generate a full experimental report from a pooled CRISPR screen
ct.makeRhoNull | Make null distribution for RRAalpha tests

ct.multiGSEA

Geneset Enrichment within a CRISPR screen using multiGSEA This function identifies differentially enriched/depleted ontological categories within the hits of a CRISPR screen given a provided 'GenseSetDb()' and a results 'data.frame' created by 'ct.generateResults()'. Testing is performed using a Hypergeometric test, and results are returned as a 'MultiGSEAResult' object defined in the 'multiGSEA' package. Note that the '@logFC' slot in the returned object will contain the median gRNA lfc across all associated guides, which in some cases may have dubious interpretive value. This method used overrepresentation analysis, derived from 'limma::kegga()', and incorporates the number of gRNAs associated with each Target (inferred from the 'geneSymbol' column of the 'resultsDF') as the bias vector (because standard aggregation methods should be underpowered for targets with few guides). Setting 'unbiased' = 'TRUE' suppresses this behavior, which is identical to a hypergeometric test.

ct.normalizeBySlope | Normalize sample abundance estimates by the slope of the values in the central range
ct.normalizeFactoredQuantiles | Apply Factored Quantile Normalization to gRNA counts
ct.normalizeFQ | Factored Quantile Normalization
ct.normalizeGuides | Normalize an ExpressionSet Containing a Crispr Screen
ct.normalizeMedians | Normalize sample abundance estimates by median gRNA counts
ct.normalizeNTC | Normalize sample abundance estimates by the median values of nontargeting control guides
ct.normalizeSpline | Normalize sample abundance estimates by a spline fit to the nontargeting controls
ct.PantherPathwayEnrichment | Run a (limited) Pathway Enrichment Analysis on the results of a Crispr experiment.
ct.PRC | Generate a Precision-Recall Curve from a CRISPR screen
ct.prepareAnnotation | Check and optionally subset an annotation file for use in a Crispr Screen
ct.rawCountDensities | Visualization of Raw gRNA Count Densities
ct.resultCheck | Determine whether a supplied object contains the results of a Pooled Screen
ct.ROC | Generate a Receiver-Operator Characteristic (ROC) Curve from a CRISPR screen
ct.signalSummary | Generate a Figure Summarizing Overall Signal for One or More Targets
ct.stackGuides | View a stacked representation of the most variable targets or individual guides within an experiment, as a percentage of the total aligned reads
ct.targetSetEnrichment | Test Whether a Specified Target Set is Enriched Within a Pooled Screen
ct.topTargets | Display the log2 fold change estimates and associated standard deviations of the guides targeting the top candidates in a crispr screen
ct.viewControls | View nontargeting guides within an experiment
ct.viewGuides | Generate a Plot of individual gRNA Pair Data in a Crispr Screen
es | ExpressionSet of count data from a Crispr screen with strong selection
essential.genes | Artificial list of 'essential' genes in the example Crispr screen included for plotting purposes
fit | Precalculated contrast fit from a Crispr screen
resultsDF | Precalculated gene-level summary of a crispr screen