mrf: Fitting a one-dimensional Markov random field mixture model...
In enRich: Analysis of Multiple ChIP-Seq Data

Description Usage Arguments Value Author(s) References See Also

mrf uses an MCMC algorithm to fit a one-dimensional Markov random field model for the latent binding profile from ChIP-seq data. The emission distribution of the enriched state (signal) can be either Poisson or Negative Binomial (NB), while the emission distribution of the non-enriched state (background) can be either a Zero-inflated Poisson (ZIP) or a Zero-inflated Negative Binomial (ZINB).

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mrf(data, method=NULL, exp.label = NULL, Niterations=10000, Nburnin=5000,  
     Poisprior=c(5, 1, 0.5, 1), NBprior=c(5, 1, 1, 1, 0.5, 1, 1, 1), 
     PoisNBprior=c(5,1,1,1, 0.5,1), var.NB=c(0.1, 0.1, 0.1, 0.1), parallel=TRUE)

`data`	A list, whose first argument is a n x 3 matrix with information on the bins. The three columns should contain "Chromosome", "Start" and "Stop" information. The second argument contains the counts of a single ChIP-seq experiment. This is a n x 1 matrix, where n is the number of bins.
`method`	A character variable. Can be "Poisson", "PoisNB" or "NB" and it refers to the densities of the mixture distribution. "Poisson" means that a ZIP distribution is used for the background (with parameters pi and mean lambda_B) and a Poisson distribution for the signal (with parameter lambda_S); "PoisNB" means that a ZIP distribution is used for the bacground (with parameter pi and lambda_B) and a NB distribution for the signal (with mean mu_S and overdispersion phi_S); "NB" means that a ZINB distribution is used for the background (with parameters pi, mu_B and phi_B) and a NB distribution for the signal (with mean mu_S and overdispersion phi_S).
`exp.label`	A charater vector, giving a label for experiment.
`Niterations`	An integer value, giving the number of MCMC iteration steps. Default value is 10000.
`Nburnin`	An integer value, giving the number of burn-in steps. Default value is 5000.
`Poisprior`	The gamma priors for the parameter lambda in the Poisson-Poisson mixture: the first two elements are the priors for signal and the second two are priors for background. Default values are (5,1, 0.5, 1).
`NBprior`	The gamma priors for the mean mu and overdispersion parameter phi in the NB-NB mixture: the first two elements are the priors for mu_S for the signal; the third and fourth elements are priors for phi_S; the fifth and sixth elements are priors for mu_B for the background and the seventh and eighth are priors for phi_B. Default values are (5, 1, 1, 1, 0.5, 1, 1, 1).
`PoisNBprior`	The gamma priors for lambda_B and mu_S, phi_S in Poisson-NB mixture, the first two are priors for mu_S, the third and the fourth are priors for phi_S, the fifth and the sixth are priors for lambda_B. Default values are (5, 1,1,1, 0.5, 1).
`var.NB`	The variances used in the Metropolis-Hastling algorithm for estimating (mu_S, phi_S, mu_B, phi_B) for NB mixture or for estimating (mu_S, phi_S) for PoisNB mixture. Default values are (0.1, 0.1, 0.1, 0.1) or (0.1, 0.1) for NB and PoisNB respectively.
`parallel`	A logical variable. If TRUE and the experiment has more than one chromosome, then the individual chromosomes will be processed in parallel, using the `clusterApplyLB` function in package `parallel`. Default value is TRUE.

`data`	The data provided as input.
`parameters`	The estimates of parameters which are the mean of samples of parameters.
`parameters.sample`	The samples matrix drawing from the posterior distributions of the parameters. The samples are collected one from every ten steps right after burn-in step. The column names for the matrix are (q_1, q_0, lambda_S, pi, lambda_B) if method="Poisson" or (q_1, q_0, mu_S, phi_S, pi, mu_B, phi_B) if method ="NB" or (q_1, q_0, mu_S, phi_S, pi, lambda_B) if method="PoisNB", where q_1 and q_0 are the transition probabilities that the current bin is enriched given the previous bin is enriched or not enriched, respectively.
`PP`	The posterior probabilities that bins are enriched.
`method`	The method used for the analysis.
`acrate.NB`	The acceptance rate of Metropolis-Hastling method.