enrichSig: Identify enrichment for signatures in individual samples...
In sjdlabgroup/MutSigTools: Mutational signature analysis

Description Usage Arguments Details Value See Also Examples

Determine over-represented mutation signatures in individual case sample(s) relative to that in the panel of control samples. This is suitable when case samples are heterogeneous, and only some of them might have excess of certain mutation signatures of interest relative to the control population..

1	enrichSig(contextfreq.cases, contextfreq.controls, signatures.ref=signatures.cosmic, threshold=0.05)

`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

Determine over-represented mutation signatures in individual case sample(s), highlighting those that are significantly enriched. The extent of enrichment is indicated using a percentile score, with low scores indicating high enrichment for specific mutation signatures in a case sample relative to that in the panel of control samples.

An object of class enrichSig.obj 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.
case.percentile: Percentile scores corresponding to the extent of enrichment of known mutation signatures in case sample(s) relative to that in the control samples.

signatures.cosmic, confidenceSig for robust signatures and caseControlSig to identify signatures with significantly higher mutation burden in case samples over control samples.
To generate contextfreq object from snv dataframe use processSNV and vcfToSNV.

data(contextfreq.cases_test)  # load case data
data(contextfreq.controls_test) # load control data
enrich_obj=enrichSig(contextfreq.case=contextfreq.cases_test, contextfreq.controls=contextfreq.controls_test, 
signatures.ref=signatures.cosmic, threshold=0.05)