baysic.test: BaySIC Evaluation of SMGs
In BaySIC: Bayesian Analysis of Significantly Mutated Genes in Cancer

Description Usage Arguments Details Value Author(s) Examples

Evaluates genes for SMGs using Bayesian posterior predictive methods

1	baysic.test(dat.out, fit.out, fdr.level = 0.15, fuzzy.cnt = 10000, r = NULL,subtype = NULL, PB.approx = FALSE)

`dat.out`	output from `baysic.data`
`fit.out`	output from `baysic.fit` which utilized `dat.out`
`fdr.level`	numeric (\in(0,1)) defining FDR level for multiple assessment passed to `fuzzy.FDR.approx`. Defaults to 0.15
`fuzzy.cnt`	number of Monte Carlo iterations to use in approximating fuzzy FDR values passed to `fuzzy.FDR.approx`. Defaults to 10000.
`r`	Optional number of MCMC draws to thin to for Monte Carlo integration, such that `r`<R, where R is the total number of MCMC draws.
`subtype`	Optional N_s\times 2 dataframe that defines membership of cancer subtype(s), where N_s≤q N. The first column of `subtype` should consist of subject ids (same as in `dat`) and the second the corresponding subtype membership. When `subtype` is provided, `baysic.test` will also generate analysis results for subtype-specific analyses.
`PB.approx`	logical; if `TRUE`, the Refined Normal Approximation (RNA) of the Poisson-Binomial distribution is used when `ref.dat` is a `list`. Defaults to `FALSE`.

When is.list{ref.dat} is TRUE, BaySIC evaluates whether or not a gene is an SMG using the Poisson-Binomial rather than the traditional binomial distribution. This accomodates subject-specific mutation rates given varying sequence content. When N is relatively large (e.g., N≥q50) it is recommended that optional arguments r and PB.approx be considered to alleviate computational burden.

Returns a list object with the following components:

`test.res`	a matrix with G rows containing the SMG analysis results from BaySIC. This includes the gene, the posterior predictive p-values, and fuzzy rejection probabilities under FDR level `fdr.level`. It will also contain results for any subtype analyses if `subtype` is specified.
`fdr.level`	value of `fdr.level` used
`fuzzy.cnt`	value of `fuzzy.cnt` used
`subtype`	value of `subtype`, if supplied

Nicholas B. Larson

## 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)
baysic.test.ex<-baysic.test(baysic.dat.ex,baysic.fit.ex)

## End(Not run)