medianNormalise          package:beadarray          R Documentation

_M_e_d_i_a_n _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 equal median values across a series of arrays.

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

     medianNormalise(BLData)

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

  BLData: can be either an BeadLevelList object or an BeadSummaryList
          object.

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

     Normalization is intended to remove from the expression measures
     any systematic trends which arise from the microarray technology
     rather than from differences between the probes or between the
     target RNA samples hybridized to the arrays.

     For median normalisation, the intensity for each gene is adjusted
     by subtracting the median of all genes on the array and then
     adding the median across all arrays. The effect is that each array
     then has the same median value.

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

     Produces an BeadLevelList containing normalised intensity values.
     Values will be on the  log2 scale

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

     Mark Dunning

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

     data(BLData)

     BLData.med = medianNormalise(BLData)

     #All distributions are fixed to have same median

     boxplot(BLData.med$R~col(BLData.med$R))

