rsn                   package:lumi                   R Documentation

_R_o_b_u_s_t _S_p_l_i_n_e _N_o_r_m_a_l_i_z_a_t_i_o_n _b_e_t_w_e_e_n _c_h_i_p_s

_D_e_s_c_r_i_p_t_i_o_n:

     Robust spline normalization (monotonic curves) between chips

_U_s_a_g_e:

     rsn(x.lumi, targetArray = NULL, excludeFold = 2, span = 0.03, ifPlot = FALSE, ...)

_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. 

excludeFold: exclude the genes with fold change larger than
          "excludeFold" during fitting the curve in normalization 

    span: the span parameter used by 'monoSmu' 

  ifPlot: determine whether to plot intermediate results 

     ...: other parameters used by 'monoSmu' 

_D_e_t_a_i_l_s:

     The robust spline normalization (RSN) algorithm combines the
     features of quantile and loess normalization. It is designed to
     normalize the variance-stabilized data. The function will check
     whether the data is variance stabilized (vst or log2 transform),
     if not, it will automatically run lumiT before run rsn. For
     details of the algorithm, please see the reference.

     The targetArray can be a column index, a vector or a LumiBatch
     object with one sample, which corresponds to an external sample to
     be normalized with. This is very useful for handling large data
     set or normalizing the data set with a common reference
     (targetArray).

_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. If it is
     a LumiBatch object, it also includes the VST transform function
     and its parameters as attributes: "transformFun", "parameter". See
     'inverseVST' for details.

_A_u_t_h_o_r(_s):

     Pan Du, Simon Lin

_S_e_e _A_l_s_o:

     'lumiN', 'monoSmu'

