normalize.exprSet          package:affyPLM          R Documentation

_N_o_r_m_a_l_i_z_a_t_i_o_n _a_p_p_l_i_e_d _t_o _e_x_p_r_S_e_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Allows the user to apply normalization routines to exprSets.

_U_s_a_g_e:

       normalize.exprSet.quantiles(eset,transfn=c("none","log","antilog"))
       normalize.exprSet.loess(eset,transfn=c("none","log","antilog"),...)
       normalize.exprSet.contrasts(eset, span = 2/3, choose.subset = TRUE, subset.size = 5000, verbose = TRUE, family = "symmetric",transfn=c("none","log","antilog"))
       normalize.exprSet.qspline(eset,transfn=c("none","log","antilog"),...)
       normalize.exprSet.invariantset(eset,prd.td = c(0.003, 0.007),verbose=FALSE,transfn=c("none","log","antilog"),baseline.type=c("mean","median","pseudo-mean","pseudo-median"))
     normalize.exprSet.scaling(eset,trim=0.02,baseline=-1,transfn=c("none","log","antilog"))

_A_r_g_u_m_e_n_t_s:

    eset: An 'exprSet'

    span: parameter to be passed to the function 'loess'.

choose.subset: 

subset.size: 

 verbose: verbosity flag

  family: parameter to be passed to the function 'loess'.

  prd.td: cutoff parameter (details in the bibliographic reference)

    trim: How much to trim from the top and bottom before computing the
          mean when using the scaling normalization

baseline: Index of array to use as baseline, negative values
          (-1,-2,-3,-4) control different baseline selection methods

 transfn: Transform the exprSet before normalizing. Useful when dealing
          with expression values that are log-scale

baseline.type: A method of selecting the baseline array

     ...: Additional parameters that may be passed to the normalization
          routine

_D_e_t_a_i_l_s:

     This function carries out normalization of expression values. In
     general you should either normalize at the probe level or at the
     expression value level, not both.

     Typing 'normalize.exprSet.methods' should give you a list of
     methods that you may use. note that you can also use the
     'normalize' function on exprSets. Use 'method' to select the
     normalization method.

_V_a_l_u_e:

     A normalized 'exprSet'.

_A_u_t_h_o_r(_s):

     Ben Bolstad, bolstad@stat.berkeley.edu

_R_e_f_e_r_e_n_c_e_s:

     Bolstad, BM (2004) _Low Level Analysis of High-density
     Oligonucleotide Array Data: Background, Normalization and
     Summarization_. PhD Dissertation. University of California,
     Berkeley.

_S_e_e _A_l_s_o:

     'normalize'

_E_x_a_m_p_l_e_s:

     data(affybatch.example)
     eset <- rma(affybatch.example,normalize=FALSE,background=FALSE)
     normalize(eset)

