impute.lowess              package:aCGH              R Documentation

_I_m_p_u_t_i_n_g _l_o_g_2 _r_a_t_i_o_s

_D_e_s_c_r_i_p_t_i_o_n:

     Imputing log2 ratios

_U_s_a_g_e:

     impute.lowess(aCGH.obj, chrominfo = human.chrom.info.Jul03, maxChrom =
     23, smooth = 0.1)

_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.

  smooth: smoothing parameter for the lowess procedure

_D_e_t_a_i_l_s:

     There are two main reasons to impute data. One is that given that
     imputation is reasonable, one can increase the analytical power
     and improve results. Another, more practical, is that at the
     moment many widely used fuctions in R do not support missing
     values. While procedures such as kNN imputations is widely used
     for gene expression data, it is more powerful to take advantage of
     the genomic structure of the array CGH data and use a smoother.
     Note that we perform only one pass os smoothing. If there still
     remain missing values, they are imputed by the median on the
     chromosome or chromosomal arm where applicable,

_V_a_l_u_e:

     Computes and returns the imputed log2 ratio matrix of the aCGH
     object.

_S_e_e _A_l_s_o:

     'aCGH', 'impute.HMM'.

_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 

     log2.ratios.imputed(ex.acgh) <- impute.lowess(ex.acgh)

