impute.HMM               package:aCGH               R Documentation

_I_m_p_u_t_i_n_g _l_o_g_2 _r_a_t_i_o_s _u_s_i_n_g _H_M_M

_D_e_s_c_r_i_p_t_i_o_n:

     Imputing log2 ratios using the output of the HMM segmenttation

_U_s_a_g_e:

     impute.HMM(aCGH.obj, chrominfo = human.chrom.info.Jul03, maxChrom =
     23, use.BIC = TRUE)

_A_r_g_u_m_e_n_t_s:

aCGH.obj: Object of class aCGH.

chrominfo: a chromosomal information associated with the mapping of the
          data

maxChrom: Highest chromosome to impute.

 use.BIC: logical parameter; if true impute missing values based on the
          Hidden Markov Model selected using Bayesian Information
          Criterion impute missing data, otherwise use AIC.

_D_e_t_a_i_l_s:

     See details in 'aCGH' discussion.

_V_a_l_u_e:

     Computes and returns the imputed log2 ratio matrix of the aCGH
     object using the output of the Hidden Markov Model segmentation
     done by invoking 'find.hmm.states' function.

_S_e_e _A_l_s_o:

     'aCGH', 'find.hmm.states', 'impute.lowess'.

_E_x_a_m_p_l_e_s:

     datadir <- system.file(package = "aCGH")
     datadir <- paste(datadir, "/examples", sep="")

     clones.info <-
           read.table(file = file.path(datadir, "clones.info.ex.txt"),
                      header = TRUE, sep = "\t", quote="", comment.char="")
     log2.ratios <-
           read.table(file = file.path(datadir, "log2.ratios.ex.txt"),
                      header = TRUE, sep = "\t", quote="", comment.char="")
     ex.acgh <- create.aCGH(log2.ratios, clones.info)

     ## Imputing the log2 ratios 

     hmm(ex.acgh) <- find.hmm.states(ex.acgh, aic = TRUE, delta = 1.5)
     log2.ratios.imputed(ex.acgh) <- impute.HMM(ex.acgh)

