ccr.geneSummary | R Documentation |
This function collapses single-guide RNAs (sgRNAs) depletion log fold-changes (logFCs)
on a targeted gene basis, by averaging (using the ccr.geneMeanFCs
). In addition it computes also a logFC threshold T
such that when considering as significantly depleted all the genes with a depletion logFC < T
, the false discover rate
(FDR) of prior known non-essential genes is below a given threshold (specificed in input). Finally it calls
significantly depleted genes according to the computed threshold.\
The significant threshold is computed using the ccr.PrRc_Curve
function, employing a reference list of core-fitness essential genes and a list of non-essential genes, assembled from multiple RNAi studies used as classification template by the BAGEL algorithm to call gene depletion significance (included as the built-in objects BAGEL_essential
and BAGEL_nonEssential
) [1].
ccr.geneSummary(sgRNA_FCprofile,
libraryAnnotation,
FDRth=0.05)
sgRNA_FCprofile |
A named numerical vector containing the sgRNAs' log fold-changes, with names corresponding to sgRNAs identifiers. |
libraryAnnotation |
A data frame containing the sgRNA library annotation (with same format of |
FDRth |
The FDR threshold to consider in order to derive the significance threshold (FDR 5% by default) |
A data frame with gene symbols as row names and two columns: the first one indicating the gene depletion logFC and the second one including a boolean value specifying if the gene under consideration is significantly depleted at the indicated FDR level.
Francesco Iorio (francesco.iorio@fht.org)
[1] BAGEL: a computational framework for identifying essential genes from pooled library screens. Traver Hart and Jason Moffat. BMC Bioinformatics, 2016 vol. 17 p. 164.
KY_Library_v1.0
, ccr.geneMeanFCs
,
ccr.PrRc_Curve
, ccr.PrRc_Curve
,
BAGEL_essential
, BAGEL_nonEssential
## loading corrected sgRNAs log fold-changes and segment annotations for
## an example cell line (EPLC-272H)
data(EPLC.272HcorrectedFCs)
## loading sgRNA library annotation
data(KY_Library_v1.0)
## storing sgRNA log fold-changes in a named vector
FCs<-EPLC.272HcorrectedFCs$corrected_logFCs$avgFC
names(FCs)<-rownames(EPLC.272HcorrectedFCs$corrected_logFCs)
## computing gene level log fold-changes
geneFCs<-ccr.geneSummary(FCs,KY_Library_v1.0)
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