qnNormalize             package:ABarray             R Documentation

_P_e_r_f_o_r_m _q_u_a_n_t_i_l_e _n_o_r_m_a_l_i_z_a_t_i_o_n

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

     Perform quantile normalization between arrays

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

     qnNormalize(eData, snr, method = 'quantile', snThresh = 3, ties = TRUE)

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

   eData: matrix of gene expression values 

     snr: Optional signal/noise ratio. Only used for trimAMean method 

  method: The normalization method desired. Default method is quantile 

snThresh: Signal/noise threshold (default = 3) to indicate presence or
          absence of a probe signal 

    ties: handle values with same rank 

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

     This function performs various normalization for the array data.
     The default is quantile normalization method (adapted from
     Bioconductor limma package). Other normalization methods include
     median, mean, trimMean (trimmed mean), trimAMean (mean with absent
     gene removed).

     For the median normalizaiton, the median signal of each array is
     scaled to the same value (this value is calculated to equal to the
     median of all values in the data). The signal values for each
     array are then adjusted by the scaling factor.

     For the mean normalization, the approach is similar to the median
     normalization procedure except that the mean signal of each array
     is scaled to the same value (this value is median of all signals
     in the data).

     For the trimMean normalization, the approach is similar to the
     mean normalization except that the mean for each array is
     calculated after trimming the top and botton 5% of signals (a
     total of 10% of values).

     For the trimAMean normalization, the signal values for absent
     probes are not considered. If the s/n of a probe is less than
     snThresh (default = 3), the expression of the probe is considered
     not present (absent). The remaining values are then trimmed (top
     and botton 2.5%, a total of 5%), and the mean value for each array
     after trimming is scaled to the same value (median of all values
     in the data).

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

     data matrix with quantile normalized data values

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

     Yongming Sun

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

     bioconductor limma package for quantile normalization

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

