normalize.quantiles      package:preprocessCore      R Documentation

_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.

_U_s_a_g_e:

       normalize.quantiles(x,copy=TRUE)

_A_r_g_u_m_e_n_t_s:

       x: A matrix of intensities where each column corresponds to a
          chip and each row is a probe.

    copy: Make a copy of matrix before normalizing. Usually safer to
          work with a copy, but in certain situations not making a copy
          of the matrix, but instead normalizing it in place will be
          more memory friendly.

_D_e_t_a_i_l_s:

     This method is based upon the concept of a quantile-quantile plot
     extended to n dimensions. No special allowances are made for
     outliers. If you make use of quantile normalization  please cite
     Bolstad et al, Bioinformatics (2003).

     This functions will handle missing data (ie NA values), based on
     the assumption that the data is missing at random.

     Note that the current implementation optimizes for better memory
     usage at the cost of some additional run-time.

_V_a_l_u_e:

     A normalized 'matrix'.

_A_u_t_h_o_r(_s):

     Ben Bolstad, bmbolstad.com

_R_e_f_e_r_e_n_c_e_s:

     Bolstad, B (2001) _Probe Level Quantile Normalization of High
     Density Oligonucleotide Array Data_. Unpublished manuscript <URL:
     http://bmbolstad.com/stuff/qnorm.pdf>

     Bolstad, B. M., Irizarry R. A., Astrand, M, and Speed, T. P.
     (2003) _A Comparison of Normalization Methods for High Density
     Oligonucleotide Array Data Based on Bias and Variance._
     Bioinformatics 19(2) ,pp 185-193. <URL:
     http://bmbolstad.com/misc/normalize/normalize.html>

_S_e_e _A_l_s_o:

     'normalize.quantiles.robust'