Description Usage Arguments Author(s) See Also Examples
This function is used to implement MICC model and get the output files
1 | MICCoutput(data, outfilename, params.init = NULL, reltol = 1e-05, abstol = 0.001, step = 200, restart = 5, MinConfident = 5)
|
data |
Input matrix of PET clusters |
outfilename |
Output filename |
params.init |
Initialized paramters, see "MICCMainLearn" function for more details |
reltol |
Relative tolerance, default value: 1e-5 |
abstol |
Absolute tolerance, default value: 1e-5 |
step |
Max number of steps before convergence, default value: 200 |
restart |
Times to restart before convergence, default value: 5 |
MinConfident |
Minimal number of PET-count to classify true interaction PET clusters when initializing the paramters, default value: 5 |
Chao He
InputMatrixFormatted
,
MICCMainLearn
,
MICC_1.0-package
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | library(MICC)
## Import data
data(TestData)
# implement the model
MICCoutput( TestData, "./TestData.txt" )
## The function is currently defined as
function (data, outfilename, params.init = NULL, reltol = 1e-05,
abstol = 0.001, step = 200, restart = 5, MinConfident = 5)
{
data_formatted <- InputMatrixFormatted(data)
Par <- MICCMainLearn(data_formatted, params.init = params.init,
reltol = reltol, abstol = abstol, step = step, restart = restart,
MinConfident = MinConfident)
params <- Par$params
PostProb <- Par$PostProb
fdr <- FDRcompute(data, params, PostProb[, 1])
output.colnames <- c("chr.", "start", "end", "chr.", "start",
"end", "cAB", "cA", "cB", "-log10(1-PostProb)", "fdr")
y <- cbind(x, -log10(1 - PostProb[, 1]), fdr)
colnames(y) <- output.colnames
write.table(y, file = outfilename, sep = "\t", row.names = F,
quote = F)
}
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