normalizeRobustSpline         package:limma         R Documentation

_N_o_r_m_a_l_i_z_e _S_i_n_g_l_e _M_i_c_r_o_a_r_r_a_y _U_s_i_n_g _S_h_r_u_n_k _R_o_b_u_s_t _S_p_l_i_n_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Normalize the M-values for a single microarray using robustly
     fitted regression splines and empirical Bayes shrinkage.

_U_s_a_g_e:

     normalizeRobustSpline(M,A,layout,df=5,method="M")

_A_r_g_u_m_e_n_t_s:

       M: numeric vector of M-values

       A: numeric vector of A-values

  layout: list specifying the dimensions of the spot matrix and the
          grid matrix

      df: degrees of freedom for regression spline, i.e., the number of
          regression coefficients and the number of knots

  method: choices are '"M"' for M-estimation or '"MM"' for high
          breakdown point regression

_D_e_t_a_i_l_s:

     This function implements an idea similar to print-tip loess
     normalization but uses regression splines in place of the loess
     curves and uses empirical Bayes ideas to shrink the individual
     prtin-tip curves towards a common value. This allows the technique
     to introduce less noise into good quality arrays with little
     spatial variation while still giving good results on arrays with
     strong spatial variation.

_V_a_l_u_e:

     Numeric vector containing normalized M-values.

_A_u_t_h_o_r(_s):

     Gordon Smyth

_R_e_f_e_r_e_n_c_e_s:

     The function is based on unpublished work by the author.

_S_e_e _A_l_s_o:

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

_E_x_a_m_p_l_e_s:

     library(sma)
     data(MouseArray)
     M <- m.spot(mouse1)
     A <- a.spot(mouse1)
     M <- normalizeRobustSpline(M,A,mouse.setup)

