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, type = "dataframe", method = "DNAcopy", ...)

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

   input: Either the name of a file or a dataframe. See details for the
          format. 

    type: What kind of data format is used as input? Either 'dataframe'
          or 'file'. 

  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:

     The input should be either a dataframe or a tabseparated textfile
     (textfiles must contain a header). The first three columns should
     contain the name, chromosome and position in bp for each array
     target respectively. The chromosome and position column must
     contain numbers only. Following these is a column with normalized
     log2 ratios for each of your samples. If the input type is a
     textfile, missing values should be represented as 'NA' or an empty
     field.

_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)
       ## First preprocess the data
       raw.data <- preprocess(Wilting, type="dataframe")
       ## 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)

