ssn                   package:lumi                   R Documentation

_S_i_m_p_l_e _S_c_a_l_i_n_g _N_o_r_m_a_l_i_z_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     This function basically adjusts the samples to the same background
     level and then optionally scales to the same foreground level.

_U_s_a_g_e:

     ssn(x.lumi, targetArray = NULL, scaling = TRUE, bgMethod=c('density', 'mean', 'median', 'none'), fgMethod=c('mean', 'density', 'median'), ...)

_A_r_g_u_m_e_n_t_s:

  x.lumi: an ExpressionSet inherited object or a data matrix with
          columns as samples and rows as genes 

targetArray: A target chip is the model for other chips to normalize.
          It can be a column index, a vector or a LumiBatch object with
          one sample. 

 scaling: determine whether do scaling or just background shift 

bgMethod: optional methods of determining the background level 

fgMethod: optional methods of determining the foreground level 

     ...: other parameters used by 'density' function 

_D_e_t_a_i_l_s:

     This function basically adjusts the samples to the same background
     level and then optionally scales to the same foreground level. The
     adjustment is based on the raw scale data (For the transformed
     data, it still estimates the parameters in the raw scale by
     inverse transformation.). 

     Comparing with other normalization methods, like quantile and
     curve-fitting methods, SSN is a more conservative method. The only
     assumption is that each sample has the same background levels and
     the same scale (if do scaling). There are three methods
     ('density', 'mean' and 'median') for background estimation. If
     bgMethod is 'none', then the background level will be set as 0,
     i.e., no background adjustment. For the 'density' bgMethod, it
     estimates the background based on the mode of probe intensities
     based on the assumption that the background level intensity is the
     most frequent value across all the probes in the chip. For the
     foreground level estimation, it also provides three methods
     ('mean', 'density', 'median'). For the 'density' fgMethod, it
     assumes the background probe levels are symmetrically distributed.
     Then we estimate the foreground levels by taking the intensity
     mean of all other probes except from the background probes. For
     the 'mean' and 'median' methods (for both bgMethod and fgMethod),
     it basically estimates the level based on the mean or median of
     all probes of the sample. If the fgMethod is the same as bgMethod
     (except 'density' method), no scaling will be performed.

_V_a_l_u_e:

     Return an object with expression values normalized. The class of
     the return object is the same as the input object x.lumi.

_A_u_t_h_o_r(_s):

     Pan Du, Simon Lin

_S_e_e _A_l_s_o:

     'lumiN'

