find.hmm.states
Determines states of the clones
This function runs unsupervised HMM algorithm and produces the essentual state information which is used for the subsequent structure determination.
- Keywords
- models
Usage
hmm.run.func(dat, datainfo = clones.info, vr = 0.01, maxiter = 100, aic = TRUE, bic = TRUE, delta = NA, eps = 0.01)
find.hmm.states(aCGH.obj, ...)Arguments
- aCGH.obj
-
object of class
aCGH. - dat
- dataframe with clones in the rows and samples in the columns
- datainfo
- dataframe containing the clones information that is used to map each clone of the array to a position on the genome. Has to contain columns with names Clone/Chrom/kb containing clone names, chromosomal assignment and kb positions respectively
- vr
- Initial experimental variance
- maxiter
- Maximum number of iterations
- aic
- TRUE or FALSE variable indicating whether or nor AIC criterion should be used for model selection (see DETAILS)
- bic
- TRUE or FALSE variable indicating whether or nor BIC criterion should be used for model selection (see DETAILS)
- delta
- numeric vector of penalty factors to use with BIC criterion. If BIC is true, delta=1 is always calculated (see DETAILS)
- eps
- parameter controlling the convergence of the EM algorithm.
- ...
- All the parameters that can be passed to find.hmm.states except dat and datainfo.
Details
One or more model selection criterion is used to determine number of states on each chromosomes. If several are specified, then a separate matrix is produced for each criterion used. Delta is a fudge factor in BIC criterion: $\delta BIC(\gamma) = \log RSS(\gamma) + q_{\gamma}\delta\log n/n.$ Note that delta = NA leads to conventional BIC. (Broman KW, Speed TP (2002) A model selection approach for the identification of quantitative trait loci in experimental crosses (with discussion). J Roy Stat Soc B 64:641-656, 731-775 )
find.hmm.states(aCGH.obj, ...) uses aCGH object instead of log2 ratios matrix dat. Equivalent representation (assuming normally distributed residuals) is to write -loglik(gamma) = n/2*log(RSS)(gamma) and then bic= -loglik+log(n)*k*delta/2 and aic = -loglik+2*k/2
Value
-
Two lists of lists are returned. Each list contains information on the
states with each of the specified model selection criteria. E.g., if
AIC = T, BIC = T and delta = c(1.5), then each list will contain
three lists corresponding to AIC, BIC(1) and BIC(1.5) as the 1st,2nd
and 3rd lists repsectively. If AIC is used, it always comes first
followed by BIC and then deltaBIC in the order of delta vector.
- states.hmm
- Each of the sublists contains 2+ 6*n columns where the first two columns contain chromosome and kb positions for each clone in the dataset supplied followed up by 6 columns for each sample where n = number of samples.column 1 = statecolumn 2 = smoothed value for a clonecolumn 3 = probability of being in a statecolumn 4 = predicted value of a statecolumn 5 = dispersioncolumn 6 = observed value
- nstates.hmm
- Each of the sublists contains a matrix with each row corresponding to a chromosome and each column to a sample. The entries indicate how many different states were identified for a given sample on a given chromosome
WARNING
When algortihm fails to fit an HMM for a given number of states on a chromosome, it prints a warning.
References
Application of Hidden Markov Models to the analysis of the array CGH data, Fridlyand et.al., JMVA, 2004
See Also
Examples
datadir <- system.file("examples", package = "aCGH")
latest.mapping.file <-
file.path(datadir, "human.clones.info.Jul03.txt")
ex.acgh <-
aCGH.read.Sprocs(dir(path = datadir,pattern = "sproc",
full.names = TRUE), latest.mapping.file,
chrom.remove.threshold = 23)
ex.acgh
data(colorectal)
#in the interests of time, we comment the actual hmm-finding function out.
#hmm(ex.acgh) <- find.hmm.states(ex.acgh, aic = TRUE, delta = 1.5)
summary(ex.acgh)