normalize.quantiles.robust       package:affy       R Documentation

_R_o_b_u_s_t _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:

     Using a normalization based upon quantiles, this function
     normalizes a matrix of probe level intensities. Allows weighting
     of chips

_U_s_a_g_e:

         normalize.AffyBatch.quantiles.robust(abatch,
     type=c("separate","pmonly","mmonly","together"),
     weights=NULL,remove.extreme=c("variance","mean","both","none"),n.remove=1,use.median=FALSE,use.log2=FALSE)

_A_r_g_u_m_e_n_t_s:

  abatch: An 'AffyBatch'

    type: A string specifying how the normalization should be applied.
          See details for more.

 weights: A vector of weights, one for each chip

remove.extreme: If weights is null, then this will be used for
          determining which chips to remove from the calculation of the
          normalization distribution, See details for more info

n.remove: number of chips to remove

use.median: if TRUE use the median to compute normalization chip,
          otherwise uses a weighted mean

use.log2: work on log2 scale. This means we will be using the geometric
          mean rather than ordinary mean

_D_e_t_a_i_l_s:

     This method is based upon the concept of a quantile-quantile  plot
     extended to n dimensions. Note that the matrix is of intensities
     not log intensities. The function performs better with raw
     intensities.

     Choosing *variance* will remove chips with variances much higher
     or lower than the other chips, *mean* removes chips with the mean
     most different from all the other means, *both* removes first
     extreme variance and then an extreme mean. The option *none* does
     not remove any chips, but will assign equal weights to all chips.

     The type argument should be one of
     '"separate","pmonly","mmonly","together"' which indicates whether
     to normalize only one probe type (PM,MM) or both together or
     separately.

_V_a_l_u_e:

     a matrix of normalized intensites

_N_o_t_e:

     This function is still experimental.

_A_u_t_h_o_r(_s):

     Ben Bolstad, bmb@bmbolstad.com

_S_e_e _A_l_s_o:

     'normalize', 'normalize.quantiles'

