normalize.quantiles.target      package:affyPLM      R Documentation

_Q_u_a_n_t_i_l_e _N_o_r_m_a_l_i_z_a_t_i_o_n _u_s_i_n_g _a _s_p_e_c_i_f_i_e_d _t_a_r_g_e_t _d_i_s_t_r_i_b_u_t_i_o_n _v_e_c_t_o_r

_D_e_s_c_r_i_p_t_i_o_n:

     Using a normalization based upon quantiles, these function
     normalizes the columns of a matrix based upon a specified
     normalization distribution

_U_s_a_g_e:

       normalize.quantiles.use.target(x,target,copy=TRUE)
       normalize.quantiles.determine.target(x,target.length=NULL)

_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

  target: A vector containing datapoints from the distribution to be
          normalized to

target.length: number of datapoints to return in target distribution
          vector. If 'NULL' then this will be taken to be equal to the
          number of rows in the matrix.

_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 either through
     'rma' or 'expresso' please cite Bolstad et al, Bioinformatics
     (2003).

_V_a_l_u_e:

     From 'normalize.quantiles.use.target' a normalized 'matrix'.

_A_u_t_h_o_r(_s):

     Ben Bolstad, bmb@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 Unpublished manuscript 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'

