Description Usage Arguments Value See Also Examples
bayes.driver
function performs by runing Bayesian driver inference model where each observed mutation in gene is taken as proof for being true driver.
1 2 3 4 5 |
sample.mutations |
data frame with SNVs and InDels in MAF like format.
Columns (with exactly same names) which
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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: |
genes |
a 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.driver |
a numeric value representing prior probability that random gene is dirver.
Default is set to |
gene.mut.driver |
a numeric value or named vector representing likelihood that gene is mutated if it is knowen to be driver. Gene does not need to be mutated if it is driver, as cancers are heterogenious. Default is set to NULL and driver.genes are considered as drivers. |
driver.genes |
a character vector of genes which are considered as drivers for this cancer. If NULL then used set is |
Variant_Classification |
(optional) integer/numeric value indicating column in |
Hugo_Symbol |
(optional) integer/numeric value indicating column in |
Tumor_Sample_Barcode |
(optional) integer/numeric value indicating column in |
CCF |
(optional) integer/numeric value indicating column in |
Damage_score |
(optional) integer/numeric value indicating column in |
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 data frame with ranked genes by posteriory probability of gene beeing true driver. Additional columns with usefull info are contained in data frame.
bcgr
, bcgr.lawrence
and bcgr.combine
for obtaining bcgr.prob variable.
1 2 3 4 5 6 7 | # first calculate CCF
sample.genes.mutect <- CCF(sample.genes.mutect)
# then somatic background probability
bcgr.prob <- bcgr.combine(sample.genes.mutect)
# bayes risk model
driver.genes <- bayes.driver(sample.genes.mutect, driver = 0.001, gene.mut.driver=1/50)
head(driver.genes)
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