loess.normalize             package:affy             R Documentation

_N_o_r_m_a_l_i_z_e _a_r_r_a_y_s

_D_e_s_c_r_i_p_t_i_o_n:

     This function treats PM and MM as the raw data on each chip. It
     fits loess curves to MVA plots and tries to normalize the chips
     with respect to each other by forcing log ratios to be scattered
     around the same constant.

_U_s_a_g_e:

     loess.normalize(mat, subset = sample(1:(dim(mat)[2]), 5000), epsilon
                      = 10^-2, maxit = 1, log.it = TRUE, verbose = TRUE,
                      span = 2/3, family.loess = "symmetric")

_A_r_g_u_m_e_n_t_s:

     mat: a matrix with columns containing the values of the chips to
          normalize.

  subset: a subset of the data to fit a loess to.

 epsilon: small value used for the stopping criterion.

   maxit: maximum number of iterations.

  log.it: logical. If 'TRUE' it takes the log2 of 'mat'

 verbose: logical. If 'TRUE' displays current pair of chip being worked
          on.

    span: span to be used by loess

family.loess: '"gaussian"' or '"symmetric"' as in 'loess'

_D_e_t_a_i_l_s:

     Experience shows that you only need 1-2 iterations to obtain
     useful results. This function is not written in an efficient way.
     In order to make it faster, loess is fit to a sample of the data
     which we then use to predict the curve for all the data. By
     setting 'family.loess="gaussian"' the function is faster, but you
     risk losing information on differentially expressed genes. The
     function 'normalize.quantiles' is faster.

_V_a_l_u_e:

     A matrix with normalized values for chips in columns.

_A_u_t_h_o_r(_s):

     Rafael A. Irizarry

_S_e_e _A_l_s_o:

     'normalize.quantiles', 'maffy.normalize', 'maffy.subset'

