quantileNormalise         package:beadarray         R Documentation

_Q_u_a_n_t_i_l_e _N_o_r_m_a_l_i_s_a_t_i_o_n _f_o_r _B_e_a_d_L_e_v_e_l_L_i_s_t _o_b_j_e_c_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Normalizes expression intensities so that the intensities or
     log-ratios have similar distributions across a series of arrays.

     Function is based on the 'normalizeBetweenArrays' function in
     'limma'

_U_s_a_g_e:

     quantileNormalise(BLData, arrays=1:length(BLData$R[1,]))

_A_r_g_u_m_e_n_t_s:

  BLData: can be either an BeadLevelList or BeadSummaryList object

  arrays: choose a subset of arrays to normalise

_D_e_t_a_i_l_s:

     Quantile normalization was proposed by Bolstad et al (2003) for
     Affymetrix-style single-channel arrays and by Yang and Thorne
     (2003) for two-color cDNA arrays. 'method="quantile"' ensures that
     the intensities have the same empirical distribution across arrays
     and across channels.

_V_a_l_u_e:

     Produces an BeadLevelList or BeadSummaryList (depending on input
     type) containing normalised intensity values.

_A_u_t_h_o_r(_s):

     Mark Dunning

_S_e_e _A_l_s_o:

     'qsplineNormalise' 'medianNormalise'

_E_x_a_m_p_l_e_s:

     data(BSData)

     BSData.q = quantileNormalise(BSData)

     #All distributions are fixed to be the same

     boxplot(BSData.q$R~col(BSData.q$R))

