rmaPLM                package:affyPLM                R Documentation

_F_i_t _a _R_M_A _t_o _A_f_f_y_m_e_t_r_i_x _G_e_n_e_c_h_i_p _D_a_t_a _a_s _a _P_L_M_s_e_t

_D_e_s_c_r_i_p_t_i_o_n:

     This function converts an 'AffyBatch' into an 'PLMset' by fitting
     a multichip model. In particular we concentrate on the RMA model.

_U_s_a_g_e:

     rmaPLM(object,subset=NULL,normalize=TRUE,background=TRUE,background.method="RMA.2",normalize.method="quantile",background.param = list(),normalize.param=list(),output.param=list(),model.param=list())

_A_r_g_u_m_e_n_t_s:

  object: an 'AffyBatch'

  subset: a vector with the names of probesets to be used. If NULL then
          all probesets are used.

normalize: logical value. If 'TRUE' normalize data using quantile
          normalization

background: logical value. If 'TRUE' background correct using RMA
          background correction

background.method: name of background method to use.

normalize.method: name of normalization method to use.

background.param: A list of parameters for background routines

normalize.param: A list of parameters for normalization routines

output.param: A list of parameters controlling optional output from the
          routine.

model.param: A list of parameters controlling model procedure

_D_e_t_a_i_l_s:

     This function fits the RMA as a Probe Level Linear models to all
     the probesets in an 'AffyBatch'.

_V_a_l_u_e:

     An 'PLMset'

_A_u_t_h_o_r(_s):

     Ben Bolstad bmb@bmbolstad.com

_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, 

      Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B and Speed TP
     (2003) _Summaries of Affymetrix GeneChip probe level data_ Nucleic
     Acids Research 31(4):e15 

      Bolstad, BM, Irizarry RA, Astrand, M, and Speed, TP (2003) _A
     Comparison of Normalization Methods for High Density
     Oligonucleotide Array Data Based on Bias and Variance._
     Bioinformatics 19(2):185-193

_S_e_e _A_l_s_o:

     'expresso', 'rma', 'threestep','fitPLM', 'threestepPLM'

_E_x_a_m_p_l_e_s:

     # A larger example testing weight image function
     data(Dilution)
     ## Not run: Pset <- rmaPLM(Dilution,output.param=list(weights=TRUE))
     ## Not run: image(Pset)