ccr.correctCounts: Correction of sgRNA treatment counts for gene independent...

In francescojm/CRISPRcleanR: Unsupervised correction of gene independent cell responses to CRISPR-cas9 targeting

Correction of sgRNA treatment counts for gene independent responses to CRISPR-Cas9 targeting

Description

This function applies an inverse transformation (described in [1]) to CRISPRcleanR corrected sgRNAs' log fold changes and produces in output normalised corrected sgRNA counts (across treatments and control replicates), suitable for gene depletion/enrichment statistical testing via mean-variance modeling (for example through MAGeCK [2]*). *MAGeCK should be executed excluding initial normalisation, as the corrected sgRNA counts outputted by this function are already normalised.

Usage

ccr.correctCounts(CL,normalised_counts,
                  correctedFCs_and_segments,
                  libraryAnnotation,
                  minTargetedGenes=3,
                  OutDir='./',
                  ncontrols=1)

Arguments

CL	A string specifying the name of the experiment. This will be used to compose names of files and folde where results will be saved.
normalised_counts	A data frame containing normalised sgRNAs' read counts, which can be computed using the ccr.NormfoldChanges function from raw sgRNAs' counts.
correctedFCs_and_segments	sgRNAs log fold changes corrected for gene independent responses, generated with the function ccr.GWclean.
libraryAnnotation	A data frame containing the sgRNAs' genome-wide annotations with at least a named row for each of the sgRNAs included in the foldchanges data frame provided in input. The following columns/headers should be present in this data frame (additional columns will be ignored): GENES: string vector containing the HGNC symbols of the genes targeted by the sgRNA under consideration; EXONE: string vector containing the gene exon targeted by the sgRNA under consideration (these should include the prefix "ex" followed by the exone number); CHRM: string vector the chromosome of the gene targeted by the sgRNA under consideration (X and Y chromosome should be specified as "X" and "Y"); STRAND: string vector containing the strand targeted by the sgRNA under consideration ("+" or "-"); STARTpop: numeric vector containing the genomic coordinate of the starting position of the region targeted by the sgRNA under consideration; ENDpos: numeric vector containing the genomic coordinate of the ending position of the region targeted by the sgRNA under consideration;
minTargetedGenes	Minimanl number of different genes targeted by sgRNAs in a biased segment in order for the corresponding counts to be corrected (default = 3).
OutDir	Path of the folder where results and plots will be saved.
ncontrols	A numerical value indicating the number of control replicates (therefore columns to be considered as controls in the normalised counts).

Value

A data frame with one entry per sgRNA and individual columns for the control/treatment samples included in the normalised count data object specified by the normalised_counts parameter, and containing sgRNA counts corrected for gene independent responses to CRISPR-Cas9 targeting and median-ratio normalised.

Author(s)

Francesco Iorio ([e-mail])

References

[1] Iorio F, Behan FM, Goncalves E, Bhosle SG, Chen E, Shepherd R, Beaver C, Ansari R, Pooley R, Wilkinson P, Harper S, Butler AP, Stronach EA, Saez-Rodriguez J, Yusa K, Garnett MJ. Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting. BMC Genomics. 2018 Aug 13;19(1):604. doi: 10.1186/s12864-018-4989-y.

[2] Li, W., Xu, H., Xiao, T., Cong, L., Love, M. I., Zhang, F., et al. (2014). MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biology, 15(12), 554.

See Also

ccr.NormfoldChanges, ccr.GWclean

Examples

## Not run: 
## Loading sgRNA library annotation file
data(KY_Library_v1.0)

## Deriving the path of the file with the example dataset,
## from the mutagenesis of the EPLC-272H colorectal cancer cell line
fn<-paste(system.file('extdata', package = 'CRISPRcleanR'),
                      '/EPLC-272H_counts.tsv',sep='')

## Loading, median-normalizing and computing fold-changes for the example dataset
normANDfcs<-ccr.NormfoldChanges(fn,min_reads=30,
                                EXPname='EPLC-272H',
                                libraryAnnotation = KY_Library_v1.0)

## Genome-sorting of the fold changes
gwSortedFCs<-ccr.logFCs2chromPos(normANDfcs$logFCs,KY_Library_v1.0)

## Identifying and correcting biased sgRNAs' fold changes
correctedFCs<-ccr.GWclean(gwSortedFCs,display=FALSE,label='EPLC-272H')

## correcting individual sgRNA treatment counts

correctedCounts<-ccr.correctCounts('EPLC-272H',normANDfcs$norm_counts,
                  correctedFCs,
                  KY_Library_v1.0,
                  minTargetedGenes=3,
                  OutDir='./')

head(correctedCounts)

## End(Not run)

francescojm/CRISPRcleanR documentation built on April 30, 2023, 5:41 a.m.