imputeMissingValues         package:snapCGH         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:

     imputeMissingValues(seg, chrominfo = chrominfo.Mb, maxChrom =
     23, smooth = 0.1)

_A_r_g_u_m_e_n_t_s:

     seg: Object of class 'SegList'

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 of 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:

     'SegList'

