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.quantiles.robust(x,copy=TRUE,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:

       x: A matrix of intensities, columns are chips, rows are probes

    copy: Make a copy of matrix before normalizing. Usually safer to
          work with a copy

 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.

_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'

