normalize.quantiles.in.blocks    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 _c_a_r_r_i_e_d _o_u_t _s_e_p_a_r_a_t_e_l_y _w_i_t_h_i_n _b_l_o_c_k_s _o_f _r_o_w_s

_D_e_s_c_r_i_p_t_i_o_n:

     Using a normalization based upon quantiles this function
     normalizes the columns of a matrix such that different subsets of
     rows get normalized together.

_U_s_a_g_e:

       normalize.quantiles.in.blocks(x,blocks,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

  blocks: A vector giving block membership for each each row

_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 Unpublished manuscript 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'

_E_x_a_m_p_l_e_s:

        ### setup the data
        blocks <- c(rep(1,5),rep(2,5),rep(3,5))
        par(mfrow=c(3,2))
        x <- matrix(c(rexp(5,0.05),rnorm(5),rnorm(5,10)))
        boxplot(x ~ blocks)
        y <- matrix(c(-rexp(5,0.05),rnorm(5,10),rnorm(5)))
        boxplot(y ~ blocks)
        pre.norm <- cbind(x,y)

        ### the in.blocks version
        post.norm <- normalize.quantiles.in.blocks(pre.norm,blocks)
        boxplot(post.norm[,1] ~ blocks)
        boxplot(post.norm[,2] ~ blocks)

        ### the usual version
        post.norm  <- normalize.quantiles(pre.norm)
        boxplot(post.norm[,1] ~ blocks)
        boxplot(post.norm[,2] ~ blocks)