runBioHMM              package:snapCGH              R Documentation

_T_h_i_s _f_u_n_c_t_i_o_n _i_m_p_l_e_m_e_n_t_s _t_h_e _B_i_o_H_M_M

_D_e_s_c_r_i_p_t_i_o_n:

     This function reads in a dataset of log2 ratios and the
     corresponding clone and covariate information. It calculates a
     heterogeneous HMM when there are  1,2,3,4 or 5 underlying states
     and chooses betwen them using either the AIC or BIC. It then
     assigns clones using a modified version of the Viterbi algorithm.

_U_s_a_g_e:

     runBioHMM(input, useCloneDists = TRUE, covariates, criteria="AIC", delta=NA
     ,var.fixed=FALSE, epsilon = 1e-06, numiter = 30000)

_A_r_g_u_m_e_n_t_s:

   input: An object of class 'MAList'or 'SegList'

useCloneDists: Boolean stating whether the distance between clones
          should be incorportated into the HMM.  If false then the HMM
          becomes homogeneous.

covariates: This is a dataframe containing information about covariate
          factors. The first two columns should be Chrom (giving the 
          chromosome on which a clone is located) and Mb (giving the
          position of the chromosome along a particular chromosome in
          Megabases). The order should be the same as that described
          above with the following crucial difference. No covariate
          information about the first clone is used in the
          segmentation. Hence, for each chromosome, there should be one
          less row in the covariate dataframe than in the  datainfo
          dataframe corresponding to this missing chromosome. This is
          important if the transition matrix is to be calculated 
          correctly. 

criteria: Options are AIC or BIC depending upon which we want to use to
          distinguish between the number of states

   delta: A variable to be assigned if the BIC is used. 

var.fixed: Logical variable - TRUE if you want to tie the variance to
          be the same across all states. Defaults to FALSE

 epsilon: Stopping criterion for the optimization algorithm.

 numiter: Number of iterations to be used in the optimization alogithm.

_V_a_l_u_e:

     The model returns an object of class 'SegList'.

_A_u_t_h_o_r(_s):

     John Marioni and Mike Smith

_R_e_f_e_r_e_n_c_e_s:

     Marioni, J.C., Thorne, N.P., Tavar\'e, S., BioHMM: a heterogeneous
     Hidden Markov Model for segmenting array CGH data, submitted

