find.hmm.states: Determines states of the clones
In aCGH: Classes and functions for Array Comparative Genomic Hybridization data

Description Usage Arguments Details Value WARNING Author(s) References See Also Examples

This function runs unsupervised HMM algorithm and produces the essentual state information which is used for the subsequent structure determination.

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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, ...)

`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.

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: δ BIC(γ) = \log RSS(γ) + q_{γ}δ\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

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 = state

column 2 = smoothed value for a clone

column 3 = probability of being in a state

column 4 = predicted value of a state

column 5 = dispersion

column 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

When algortihm fails to fit an HMM for a given number of states on a chromosome, it prints a warning.

Jane Fridlyand

Application of Hidden Markov Models to the analysis of the array CGH data, Fridlyand et.al., JMVA, 2004

aCGH

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)