biomvRhsmm {biomvRCNS} | R Documentation

## Estimating the most likely state sequence using Hidden Semi Markov Model

### Description

The batch function of building Hidden Semi Markov Model (HSMM) to estimate the most likely state sequences for multiple input data series.

### Usage

    biomvRhsmm(x, maxk=NULL, maxbp=NULL, J=3, xPos=NULL, xRange=NULL, usePos='start', emis.type='norm', com.emis=FALSE, xAnno=NULL, soj.type='gamma', q.alpha=0.05, r.var=0.75, useMC=TRUE, cMethod='F-B', maxit=1, maxgap=Inf, tol=1e-06, grp=NULL, cluster.m=NULL, avg.m='median', prior.m='cluster', trim=0, na.rm=TRUE)

### Arguments

`x` | input data matrix, or a `GRanges` object with input stored in the meta `DataFrame`, assume ordered.
`maxk` | maximum length of stay for the sojourn distribution
`maxbp` | maximum length of stay in bp for the sojourn distribution, given positional information specified in `xPos` / `xRange`
`J` | number of states
`xPos` | a vector of positions for each `x` row
`xRange` | a `IRanges`/`GRanges` object, same length as `x` rows
`usePos` | character value to indicate whether the 'start', 'end' or 'mid' point position should be used to estimate the sojourn distribution
`emis.type` | type of the emission distribution, only the following types are supported: 'norm', 'mvnorm', 'pois', 'nbinom', 'mvt', 't'
`com.emis` | whether to set a common emission prior across different seqnames. if TRUE, the emission will not be updated during individual runs.
`xAnno` | a optional `TxDb` / `GRanges` / `GRangesList` / `list` object used in `sojournAnno` to infer parameters for the sojourn distribution
`soj.type` | type of the sojourn distribution, only the following types are supported: 'nonpara', 'gamma', 'pois', 'nbinom'
`q.alpha` | a quantile factor controlling the estimated prior for the mean of the emission of each states, `seq(from=q.alpha, to=1-q.alpha, length.out=J)`
`r.var` | a ratio factor controlling the estimated prior for the variance / covariance structure of each states. A value larger than 1 tend to allow larger variation in extreme states; a value smaller than 1 will decrease the probability of having extreme state
`useMC` | TRUE if `mclapply` should be used to speed up the calculation, use `options(mc.cores=n)` to set number of parallel processes
`cMethod` | C algorithm used for the most likely state sequence, 'F-B' or 'Viterbi'
`maxit` | max iteration of the EM run with Forward-Backward algorithm
`maxgap` | max distance between neighbouring feature to consider a split
`tol` | tolerance level of the likelihood change to terminate the EM run
`grp` | vector of group assignment for each sample, with a length the same as columns in the data matrix, samples within each group would be processed simultaneously if a multivariate emission distribution is available
`cluster.m` | clustering method for prior grouping, possible values are 'ward','single','complete','average','mcquitty','median','centroid'
`avg.m` | method to calculate average value for each segment, 'median' or 'mean' possibly trimmed
`prior.m` | method to select emission prior for each state, 'quantile' uses different levels of quantile; the 'cluster' method uses clara function from cluster
`trim` | the fraction (0 to 0.5) of observations to be trimmed from each end of x before the mean is computed. Values of trim outside that range are taken as the nearest endpoint.
`na.rm` | `TRUE` if `NA` value should be ignored

### Details

This is the batch function of building Hidden Semi Markov Model (HSMM) to estimating the most likely state sequences for multiple input data series. The function will sequentially process each region identified by the distinctive `seqnames` in `x` or in `xRange` if available, or assuming all data from the same region. A second layer of stratification is introduced by the argument `grp`, which could be used to reflect experimental design. The assumption is that profiles from the same group could be considered homogeneous, thus processed together if `emis.type` is compatible (currently only with 'mvnorm'). Argument for the sojourn density will be initialized as flat prior or estimated from other data before calling the work horse function `hsmmRun`. Then for each batch run results will be combined and eventually a `biomvRCNS-class` object will be returned. See the vignette for more details and examples.

### Value

A `biomvRCNS-class` object:

``x`:` | Object of class `"GRanges"`, with range information either from real positional data or just indices, with input data matrix stored in the meta columns. Additional meta columns for the estimated states and associated probabilities for each sample or group will also be appended following the input data matrix.
``res`:` | Object of class `"GRanges"` , each range represent one continuous segment identified, with sample name slot 'SAMPLE', estimated state slot 'STATE' and segment mean slot 'MEAN' stored in the meta columns
``param`:` | Object of class `"list"`, list of all parameters used in the model run, plus the re-estimated emission and sojourn parameters.

### Author(s)

Yang Du

### References

Guedon, Y. (2003). Estimating hidden semi-Markov chains from discrete sequences. Journal of Computational and Graphical Statistics, 12(3), 604-639.

### See Also

`biomvRseg`

### Examples

    	data(coriell)
    	xgr<-GRanges(seqnames=paste('chr', coriell[,2], sep=''), IRanges(start=coriell[,3], width=1, names=coriell[,1]))
    	values(xgr)<-DataFrame(coriell[,4:5], row.names=NULL)
    	xgr<-sort(xgr)
    	reshsmm<-biomvRhsmm(x=xgr, maxbp=4E4, J=3, soj.type='gamma', emis.type='norm', grp=c(1,2))

    	## access model parameters
    	reshsmm@param$soj.par
    	reshsmm@param$emis.par

    	## states assigned and associated probabilities
    	mcols(reshsmm@x)[,-(1:2)]

[Package biomvRCNS version 1.32.0 Index]