caseControlSig: Identify signatures with significantly higher mutation burden...
In sjdlabgroup/MutSigTools: Mutational signature analysis

Description Usage Arguments Details Value See Also Examples

Identify signatures with significantly higher mutation burden in case samples over control samples.

1	caseControlSig(contextfreq.cases, contextfreq.controls, signatures.ref=signatures.cosmic, threshold=0.05, adjust="fdr")

`contextfreq.cases`	A data frame of class contextfreq containing mutation frequency in tri-nucleotide contexts in case samples
`contextfreq.controls`	A data frame of class contextfreq containing mutation frequency in tri-nucleotide contexts in control samples
`signatures.ref`	An object of class mutsig comprising the set of signatures. (signatures.nature2013 or signatures.cosmic or signatures.cosmic.2019 ), Default: 'signatures.cosmic'
`threshold`	Threshold for uncorrected percentile score. Default: 0.05
`adjust`	Method for p-value correction for multiple testing. Options are as provided in the function p.adjust. The default method is the "FDR" method.

Identifes signatures with significantly higher mutation burden in case samples campared to control samples using the Wilcoxon Rank sum test. This is suitable when the samples are homogeneous within respective groups i.e., case and control, and signatures significantly over-represented in cases relative to the controls are of interest.

An object providing the following information:

n.case: Number of case samples.
n.control: Number of control samples.
case.weights: A data frame containing estimated weights of known mutation signatures in the case samples.
control.weights: A data frame containing estimated weights of known mutation signatures in the control samples.
p.value: Indicates statistical significance of higher mutation burden of signatures in cases over controls.
adjust.p.value: P-values adjusted for multiple testing correction.

signatures.cosmic, confidenceSig for robust signatures and enrichSig for enriched signatures in individual case samples.
To generate contextfreq object from snv dataframe use processSNV and vcfToSNV.

data(contextfreq.cases_test)
data(contextfreq.controls_test)
signif_signatures_obj=caseControlSig(contextfreq.cases=contextfreq.cases_test, 
contextfreq.controls=contextfreq.controls_test, signatures.ref=signatures.cosmic, threshold=0.05, adjust="fdr")