baysic.fit: Fits BaySIC BMR model

In BaySIC: Bayesian Analysis of Significantly Mutated Genes in Cancer

Description

Generates an MCMC model fit of the BaySIC BMR model

Usage

baysic.fit(dat.out, snv.cat, covar = NULL, excl.list = NULL, burn.in = 10000,n.samp = 25000, fn.jags = "baysic.jags", prior = NULL)

Arguments

dat.out	Output from baysic.data
snv.cat	a list of length C, where C is the number of sequence categories desired to be modeled (C≤q32). Each element of snv.cat should be a vector of character strings of trinucleotide motifs (e.g., c("ATA","ACA")) which define a group of motifs which are assumed to have the same background mutation rate.
covar	optional G \times Q matrix of gene-level covariate data, where G is the total number of genes and Q the number of covariates.
excl.list	optional vector of genes to be excluded from model fitting process. The format of excl.list can be either character or numeric, the former indicating the names of genes and the latter their order in ref.dat.
burn.in	an integer; represents the burn-in size to apply in the MCMC model fitting using JAGS. Defaults to 10,000
n.samp	an integer; represents the size of the MCMC posterior sample draw from the fitted model. Defaults to 25,000
fn.jags	a character string; corresponds to the file name and location of the JAGS model file to be written. Defaults to "baysic.jags" in the current working directory.
prior	optional vector of prior distribution specifications (as character strings). If is.null(prior)==FALSE, prior should be of length equal to all of the model parameters and formatted to follow the distributional notation of the JAGS model language. The order of the prior specification follows the format: SNV categories, any covariates (optional), indel λ parameter.

Value

Returns a list object with the following components:

fit.post	an mcmc object of the posterior draws of the BaySIC BMR model parameters
covar	covar object (if included in baysic.fit argument)
snv.cat	the snv.cat object in the original call
excl.list	excl.list object (if included in baysic.fit argument)

Author(s)

Nicholas B. Larson

See Also

baysic.data,baysic.test

Examples

## Not run: 
data(example.dat)
data(ccds.19)
baysic.dat.ex<-baysic.data(example.dat,ccds.19)
snv.cat.ex<-list()
snv.cat.ex[[1]]<-grep("[^T]C[^G]",colnames(ccds.19)[-c(1:2)])
snv.cat.ex[[2]]<-unique(c(grep("TC.",colnames(ccds.19)[-c(1:2)]),grep(".CG",colnames(ccds.19)[-c(1:2)])))
snv.cat.ex[[3]]<-grep(".T.",colnames(ccds.19)[-c(1:2)])
baysic.fit.ex<-baysic.fit(baysic.dat.ex,snv.cat.ex)

## End(Not run)

Example output

Loading required package: rjags
Loading required package: coda
Linked to JAGS 4.2.0
Loaded modules: basemod,bugs
Loading required package: fields
Loading required package: spam
Loading required package: dotCall64
Loading required package: grid
Spam version 2.1-1 (2017-07-02) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction 
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.

Attaching package: 'spam'

The following objects are masked from 'package:base':

    backsolve, forwardsolve

Loading required package: maps
Loading required package: poibin
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 4
   Unobserved stochastic nodes: 4
   Total graph size: 41

Initializing model

BaySIC documentation built on May 2, 2019, 10:29 a.m.