Usage
eventTiming (x, m, history, totalCopy,
method = c("fullMLE","partialMLE", "Bayes"),
type = c("gain", "CNLOH"), seqError = 0, bootstrapCI = NULL,
B = if (method =="Bayes") 10000 else 500, CILevel = 0.95, normCont = 0,
verbose = TRUE, returnAssignments = FALSE, coverageCutoff = 1,
minMutations = 10, init = NULL, maxiter = 100, tol= 1e-04,
mutationId = 1:length(x),...)
Arguments
x
vector. the number of reads/fragments containing the variant m
vector. the number of reads/fragments covering the location with the variant (the coverage) history
a matrix, based on the history of the region (see Details) totalCopy
integer. the total number of copies of the tumor DNA for this region method
what estimation method to use, one of ``fullMLE",``partialMLE",``Bayes"
type
type of region, either a gain or a CNLOH region seqError
Probability of sequencing error
bootstrapCI
type of bootstrap confidence interval to calculate, one of ``parametric", ``nonparametric". If NULL, then the confidence interval is not calculated B
number of bootstrap samples to take (or simulations from the posterior for Bayesian estimation) CILevel
At what level the confidence intervals should be calculated.
normCont
the proportion of normal contamination, between 0 and 1. verbose
logical. Whether to give additional warnings as the program is running. returnAssignments
logical. Whether to return the probabilistic assignments of mutations to allele frequencies generated by the EM algorithm. Also returns the x,m values for those that pass the filter. coverageCutoff
minimum value for m[i]; any entries with m[i] minMutations
minimum number of mutations required.
init
initial value of multinomial parameter q passed to estimateQ. maxiter
maximum number of iterations in calculation q. tol
tolerance in the convergence of q mutationId
identification values of the mutations (vector of the same length as x and m). Default is indexing values, 1:length(x). Used when returnAssignments=TRUE so that the assignments of the mutations to allele frequencies can be linked with the original mutations if there has been filtering in eventTiming, e.g. due to depth of coverage.
...
Arguments passed to internal fitting function for Bayesian Method. For example, `alpha' gives the Dirichlet prior of the bayesian estimate (default=1), `tdf' gives the number of degrees of freedom for the t proposal density used in the bayesian estimate (default=4), `bayesApproxMethod' gives the method for calculating the approximate distribution (default is ``sir"; ``inv" is for K=1 when the problem is 1-dimensional and one can easily grid and get the approximate posterior density and cdf).