segmentData             package:CGHcall             R Documentation

_B_r_e_a_k_p_o_i_n_t _d_e_t_e_c_t_i_o_n _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:

     A wrapper function to run existing breakpoint detection algorithms
     on arrayCGH data. Currently only DNAcopy is implemented.

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

     segmentData(input, method = "DNAcopy", ...)

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

   input: Object of class 'cghRaw'. 

  method: The method to be used for breakpoint detection. Currently
          only 'DNAcopy' is supported, which will run the 'segment'
          function.

     ...: Arguments for 'segment'. 

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

     See 'segment' for details on the algorithm.

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

     This function returns a dataframe in the same format as the input
     with segmented arrayCGH data.

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

     Sjoerd Vosse & Mark van de Wiel

_R_e_f_e_r_e_n_c_e_s:

     Venkatraman, A.S., Olshen, A.B. (2007). A faster circulary binary
     segmentation algorithm for the analysis of array CGH data.
     _Bioinformatics, 23_, 657-663.

_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)  
       ## Segmentation with slightly relaxed significance level to accept change-points.
       ## Note that segmentation can take a long time.
       ## Not run: segmented.data <- segmentData(normalized.data, alpha=0.02)

