bayes.risk: Bayesina risk inference model.

In hanasusak/cDriver: Find best driver candidate genes

Description

bayes.risk function performs by runing Bayesian risk inference model when priors are set by user and liklihood is calculated from given data of SNVs/InDels.

Usage

bayes.risk(sample.mutations, bcgr.prob, genes = NULL, prior.sick = 0.0045,
  Variant_Classification = NULL, Hugo_Symbol = NULL,
  Tumor_Sample_Barcode = NULL, CCF = NULL, Damage_score = NULL,
  mode = "MAX", epsilon = 0.05)

Arguments

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.
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 .
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.

Value

a data frame with ranked genes by posteriory probability of gene beeing risk factor for developing tumor. Additional columns with usefull info are contained in data frame.

See Also

bcgr, bcgr.lawrence and bcgr.combine for obtaining bcgr.prob variable.

Examples

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

hanasusak/cDriver documentation built on May 17, 2019, 2:27 p.m.