cutoff.risk: Calculation of suggested cut off for bayesian risk model
In hanasusak/cDriver: Find best driver candidate genes

Description Usage Arguments Value See Also Examples

cutoff.risk function runs Bayesian risk inference model n times, but with randomly generated gene names (probablity of gene beeing mutated is taken from background model)

cutoff.risk(sample.mutations, bcgr.prob, n = 100, fdr = 0.1,
  simulation.quantile = 0.5, genes = NULL, prior.sick = 0.0045,
  plot.save = FALSE, permutationResults.save = FALSE,
  Variant_Classification = NULL, Hugo_Symbol = NULL,
  Tumor_Sample_Barcode = NULL, CCF = NULL, Damage_score = NULL,
  mode = "MAX", epsilon = 0.05)

`sample.mutations`	data frame with SNVs and InDels in MAF like format. Columns (with exactly same names) which `sample.mutations` should have are: Variant_Classification column specifed by MAF format, used to distinguish between silent and nonsilent SNVs Hugo_Symbol column specifed by MAF format, which reports gene for each SNV. Tumor_Sample_Barcode column specifed by MAF format, reporting for each SNV in wich patient was found. CCF numeric column produce by `CCF` function. Damage_score numeric column with values between 0 and 1, where 1 means very damaging SNV/IndDel and 0 not damaging SNV/InDel
`bcgr.prob`	a numeric vector, same lenght as genes (should be same orderd also) which gives probability of gene having somatic mutation in healfy population. There are functions for obtaining this vector: `bcgr`, `bcgr.lawrence` and `bcgr.combine`.
`n`	a integer number indicating how many random genes mutations (by background probablity) tests will be done. Default is 100.
`fdr`	expected false discover rate. Value can be between 0 and 1, while closer to 0 less false discoveries will be allowed. Default value is 0.1 (10% of ranked genes before suggested cut off are expected to be false postives).
`simulation.quantile`	represent numeric value between 0 and 1 that will take for each ranking that qunantile from n permutations. Default value is 0.5 (median).
`genes`	vector of genes which were sequenced. They should be unique values of Hugo_Symbol column (with possibility of more additional genes which did not have any SNV/Indel. in given cohort). Default NULL.
`prior.sick`	a numeric value representing incidence of tumor in population. Set by default to 0.0045
`plot.save`	a boolean variable to indicate if plot should be saved
`permutationResults.save`	a boolean variable to indicate if n permutations results should be saved
`Variant_Classification`	(optional) integer/numeric value indicating column in `sample.mutations` which contain classification for SNV (Silent or not). Default is NULL value (in this case `sample.mutations` should already have this column)
`Hugo_Symbol`	(optional) integer/numeric value indicating column in `sample.mutations` having gene names for reported SNVs/Indels. Default is NULL value (in this case `sample.mutations` should already have this column)
`Tumor_Sample_Barcode`	(optional) integer/numeric value indicating column in `sample.mutations` which have sample ids for SNVs/Indels. Default is NULL value (in this case `sample.mutations` should already have this column)
`CCF`	(optional) integer/numeric value indicating column in `sample.mutations` which have cancer cell fraction information for SNVs/Indels. Default is NULL value (in this case `sample.mutations` should already have this column)
`Damage_score`	(optional) integer/numeric value indicating column in `sample.mutations` which contain damage score for SNVs/Indels. Default is NULL value (in this case `sample.mutations` should already have this column)
`mode`	a charechter value indicationg how to solve when in one gene sample pair there are multiple mutations. Options are SUM, MAX and ADVANCE
`epsilon`	a numeric value. If mode is ADVANCE, epsilone value will be threshold for CCF difference to decide if they are in same or different clone.

a integer value, where suggested cut off for ranking is.

CCF, bcgr, bcgr.lawrence, bcgr.combine and bayes.risk

# first calculate CCF
sample.genes.mutect <- CCF(sample.genes.mutect)
# then somatic background probability
bcgr.prob <- bcgr.combine(sample.genes.mutect)
# bayes risk model suggested cut off
suggested.cut.off <- cutoff.risk(sample.genes.mutect,  bcgr.prob, prior.sick = 0.00007) 
print(suggested.cut.off)