normalize              package:CGHcall              R Documentation

_N_o_r_m_a_l_i_z_a_t_i_o_n _a_n_d _c_e_l_l_u_l_a_r_i_t_y _a_d_j_u_s_t_m_e_n_t _f_o_r _a_r_r_a_y_C_G_H _d_a_t_a.

_D_e_s_c_r_i_p_t_i_o_n:

     This function normalizes arrayCGH data using the global mode or
     median. It can also adjust for the cellularity of your data.

_U_s_a_g_e:

     normalize(input, method = "median", cellularity = 1, smoothOutliers = TRUE, ...)

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

   input: Object of class 'cghRaw'. 

  method: Normalization method, either 'median', 'mode', or 'none'. 

cellularity: A vector of cellularities ranging from 0 to 1 to define
          the contamination of your sample with healthy cells (1 = no
          contamination). See details for more information. 

smoothOutliers: Logical. Indicates whether outliers should be smoothed
          using the 'smooth.CNA' function.

     ...: Arguments for 'smooth.CNA'. 

_D_e_t_a_i_l_s:

     The cellularity parameter should be a vector of length n where n
     is the number of samples in your dataset. The vector is recycled
     if there are not enough values in it, or truncated if there are
     too many. For more information on the correction we refer to
     section 1.6 of the supplementary information for van de Wiel et
     al. 2006.

_V_a_l_u_e:

     This function returns a dataframe in the same format as the input
     with normalized and/or cellularity adjusted log2 ratios.

_A_u_t_h_o_r(_s):

     Sjoerd Vosse & Mark van de Wiel

_E_x_a_m_p_l_e_s:

       data(Wilting)
       ## Convert to this-is-escaped-codenormal-bracket27bracket-normal object
       cgh <- make_cghRaw(Wilting)
       ## First preprocess the data
       raw.data <- preprocess(cgh)
       ## Simple global median normalization for samples with 75% tumor cells
       perc.tumor <- rep(0.75, 3)
       normalized.data <- normalize(raw.data, cellularity=perc.tumor)

