qsplineNormalise          package:beadarray          R Documentation

_Q_s_p_l_i_n_w _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 by fitting a smoothing curves to
     quantiles

     Function is based on the 'normalize.qspline' function in 'affy'

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

     qsplineNormalise(BLData)

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

  BLData: can be either an BeadLevelList or BeadSummaryList object

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

     This normalization method uses the quantiles from each array and
     the target to fit a system of cubic splines to normalize the data.
     The target should be the mean (geometric) or median of each probe
     but could also be the name of a particular chip in the abatch
     object.

     Parameters setting can be of much importance when using this
     method. The parameter fit.iter is used as a starting point to find
     a more appropriate value. Unfortunately the algorithm used do not
     converge in some cases. If this happens, the fit.iter value is
     used and a warning is thrown. Use of different settings for the
     parameter samples was reported to give good results. More
     specifically, for about 200 data points use samples = 0.33, for
     about 2000 data points use samples = 0.05, for about 10000 data
     points use samples = 0.02 (thanks to Paul Boutros).

     The type argument should be one of
     "separate","pmonly","mmonly","together" which indicates whether to
     normalize only one probe type (PM,MM) or both together or
     separately.

_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):

     Laurent and C Workman

_R_e_f_e_r_e_n_c_e_s:

     Christopher Workman, Lars Juhl Jensen, Hanne Jarmer, Randy Berka,
     Laurent Gautier, Henrik Bj{o}rn Nielsen, Hans-Henrik Saxild, Claus
     Nielsen, S{o}ren Brunak, and Steen Knudsen. A new non-linear
     normal- ization method for reducing variability in dna microarray
     experiments. Genome Biology, accepted, 2002

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

     data(BLData)

     #BLData.q = qsplineNormalise(BLData)

     #All distributions are fixed to be the same

     #boxplot(log2(BLData.q$R)~col(BLData.q$R))

