normalizeQuantiles           package:limma           R Documentation

_N_o_r_m_a_l_i_z_e _C_o_l_u_m_n_s _o_f _a _M_a_t_r_i_x _t_o _h_a_v_e _t_h_e _s_a_m_e _Q_u_a_n_t_i_l_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Normalize the columns of a matrix to have the same quantiles,
     allowing for missing values. Users do not normally need to call
     this function directly - use 'normalizeBetweenArrays' instead.

_U_s_a_g_e:

     normalizeQuantiles(A, ties=TRUE)

_A_r_g_u_m_e_n_t_s:

       A: numeric matrix. Missing values are allowed.

    ties: logical. If 'TRUE', ties in each column of 'A' are treated in
          careful way. tied values will be normalized to the mean of
          the corresponding pooled quantiles.

_D_e_t_a_i_l_s:

     This function is intended to normalize single channel or A-value
     microarray intensities between arrays. Each quantile of each
     column is set to the mean of that quantile across arrays. The
     intention is to make all the normalized columns have the same
     empirical distribution. This will be exactly true if there are no
     missing values and no ties within the columns: the normalized
     columns are then simply permutations of one another.

     If there are ties amongst the intensities for a particular array,
     then with 'ties=FALSE' the ties are broken in an unpredictable
     order. If 'ties=TRUE', all the tied values for that array will be
     normalized to the same value, the average of the quantiles for the
     tied values.

_V_a_l_u_e:

     A matrix of the same dimensions as 'A' containing the normalized
     values.

_A_u_t_h_o_r(_s):

     Gordon Smyth

_R_e_f_e_r_e_n_c_e_s:

     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*, 185-193.

_S_e_e _A_l_s_o:

     An overview of LIMMA functions for normalization is given in
     05.Normalization.

