Fit a Hidden Markov Model to a ChIPseq sample.
Description
Fit a HMM to a ChIPseq sample to determine the modification state of genomic regions, e.g. call peaks in the sample.
Usage
1 2 3 4 5 6  callPeaksUnivariateAllChr(binned.data, input.data = NULL, eps = 0.01,
init = "standard", max.time = NULL, max.iter = NULL, num.trials = 1,
eps.try = NULL, num.threads = 1, read.cutoff = TRUE,
read.cutoff.quantile = 1, read.cutoff.absolute = 500, max.mean = Inf,
post.cutoff = 0.5, control = FALSE, keep.posteriors = FALSE,
keep.densities = FALSE, verbosity = 1)

Arguments
binned.data 
A 
input.data 
Input control for the experiment. A 
eps 
Convergence threshold for the BaumWelch algorithm. 
init 
One of the following initialization procedures:

max.time 
The maximum running time in seconds for the BaumWelch algorithm. If this time is reached, the BaumWelch will terminate after the current iteration finishes. The default 
max.iter 
The maximum number of iterations for the BaumWelch algorithm. The default 
num.trials 
The number of trials to run the HMM. Each time, the HMM is seeded with different random initial values. The HMM with the best likelihood is given as output. 
eps.try 
If code num.trials is set to greater than 1, 
num.threads 
Number of threads to use. Setting this to >1 may give increased performance. 
read.cutoff 
The default ( 
read.cutoff.quantile 
A quantile between 0 and 1. Should be near 1. Read counts above this quantile will be set to the read count specified by this quantile. Filtering very high read counts increases the performance of the BaumWelch fitting procedure. However, if your data contains very few peaks they might be filtered out. If option 
read.cutoff.absolute 
Read counts above this value will be set to the read count specified by this value. Filtering very high read counts increases the performance of the BaumWelch fitting procedure. However, if your data contains very few peaks they might be filtered out. If option 
max.mean 
If 
post.cutoff 
False discovery rate. codeNULL means that the state with maximum posterior probability will be chosen, irrespective of its absolute probability (default=codeNULL). 
control 
If set to 
keep.posteriors 
If set to 
keep.densities 
If set to 
verbosity 
Verbosity level for the fitting procedure. 0  No output, 1  Iterations are printed. 
Details
The Hidden Markov Model which is used to classify the bins uses 3 states: state 'zeroinflation' with a delta function as emission densitiy (only zero read counts), 'unmodified' and 'modified' with Negative Binomials as emission densities. A BaumWelch algorithm is employed to estimate the parameters of the distributions. Please refer to our manuscript at http://dx.doi.org/10.1101/038612 for a detailed description of the method.
Value
A uniHMM
object.
Author(s)
Aaron Taudt, Maria Coome Tatche
See Also
uniHMM
, callPeaksMultivariate