normRepLoess           package:maigesPack           R Documentation

_B_o_o_t_s_t_r_a_p _o_f _L_O_W_E_S_S _n_o_r_m_a_l_i_s_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     This function normalises a microarray object re-doing the LOWESS
     fitting several times, selecting a pre-specified proportion of
     points aleatorily.

_U_s_a_g_e:

     normRepLoess(raw, span=0.4, propLoess=0.5, nRep=50, func="none",
                  bkgSub="none", ...)

_A_r_g_u_m_e_n_t_s:

     raw: an object of class 'maigesRaw' to be normalised.

    span: real number in (0,1) representing the proportion of points to
          use in the loess regression.

propLoess: real number in (0,1) representing the proportion of points
          (spots) to be used in each iteration of loess.

    nRep: number of repetitions for loess procedure.

    func: character string giving the function to estimate the final W
          value. You must use 'mean', 'median' or 'none' (default).

  bkgSub: character with background subtraction method, using the
          function 'backgroundcorrect' from _limma_ package.

     ...: additional parameters for function 'loessFit' from _limma_
          package.

_D_e_t_a_i_l_s:

     The LOWESS fitting for normalising microarray data is a
     computational intensive task, so pay attention to not specify a
     very large argument in 'nRep'. If you do so, your process will
     take so much time to conclude.

_V_a_l_u_e:

     The result of this function is an object of class 'maiges'.

_A_u_t_h_o_r(_s):

     Gustavo H. Esteves <gesteves@vision.ime.usp.br>

_S_e_e _A_l_s_o:

     'loessFit'.

_E_x_a_m_p_l_e_s:

     ## Loading the dataset
     data(gastro)

     ## Doing the repetition loess with default parameters. Be carefull, this
     ## is very time consuming
     ## Not run: 
     gastro.norm = normRepLoess(gastro.raw2)
     ## End(Not run)

     ## Do the same normalization selecting 60% dos spots with 10
     ## repetitions and estimating the W by the mean value.
     ## Not run: 
     gastro.norm = normRepLoess(gastro.raw2, propLoess=0.6, nRep=10, func="mean")
     ## End(Not run)

