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 
fit.out 
output from 
fdr.level 
numeric (\in(0,1)) defining FDR level for multiple assessment passed to 
fuzzy.cnt 
number of Monte Carlo iterations to use in approximating fuzzy FDR values passed to 
r 
Optional number of MCMC draws to thin to for Monte Carlo integration, such that 
subtype 
Optional N_s\times 2 dataframe that defines membership of cancer subtype(s), where N_s≤q N. The first column of 
PB.approx 
logical; if 
When is.list{ref.dat}
is TRUE
, BaySIC evaluates whether or not a gene is an SMG using the PoissonBinomial rather than the traditional binomial distribution. This accomodates subjectspecific 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 pvalues, and fuzzy rejection probabilities under FDR level 
fdr.level 
value of 
fuzzy.cnt 
value of 
subtype 
value of 
Nicholas B. Larson
1 2 3 4 5 6 7 8 9 10 11 12  ## 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)

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