justGCRMA               package:gcrma               R Documentation

_C_o_m_p_u_t_e _G_C_R_M_A _D_i_r_e_c_t_l_y _f_r_o_m _C_E_L _F_i_l_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     This function converts CEL files into an 'exprSet' using the
     robust multi-array average (RMA) expression measure with help of
     probe sequences.

_U_s_a_g_e:

     just.gcrma <- function(..., filenames=character(0),
                            phenoData=new("phenoData"),
                            description=NULL,
                            notes="", compress=getOption("BioC")$affy$compress.cel,
                            normalize=TRUE, bgversion=2, affinity.info=NULL,
                            type=c("fullmodel","affinities","mm","constant"),
                            k=6*fast+0.5*(1-fast), stretch=1.15*fast+1*(1-fast),
                            correction=1, rho=0.7, optical.correct=TRUE,
                            verbose=TRUE, fast=TRUE, minimum=1, optimize.by=c("speed","memory"))

     justGCRMA <- function(..., filenames=character(0),
                          widget=getOption("BioC")$affy$use.widgets,
                          compress=getOption("BioC")$affy$compress.cel,
                          celfile.path=getwd(),
                          sampleNames=NULL,
                          phenoData=NULL,
                          description=NULL,
                          notes="",
                          normalize=TRUE, 
                          bgversion=2, affinity.info=NULL,
                          type=c("fullmodel","affinities","mm","constant"),
                          k=6*fast+0.5*(1-fast), stretch=1.15*fast+1*(1-fast),
                          correction=1, rho=0.7, optical.correct=TRUE,
                          verbose=TRUE, fast=TRUE, minimum=1, optimize.by=c("speed","memory"))

_A_r_g_u_m_e_n_t_s:

     ...: file names separated by comma.

filenames: file names in a character vector.

  widget: a logical specifying if widgets should be used.

compress: are the CEL files compressed ?

phenoData: a 'phenoData' object

description: a 'MIAME' object

   notes: notes 

affinity.info: 'NULL' or a list of three components: apm,amm and index,
          for PM probe affinities, MM probe affinities, the index of
          probes with known sequence, respectively.

    type: "fullmodel" for sequence and MM model. "affinities" for
          sequence information only. "mm" for using MM without sequence
          information.

       k: A tuning factor.

     rho: correlation coefficient of log background intensity in a pair
          of pm/mm probes. Default=.7

 stretch: 

correction: .

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

optical.correct: Logical value. If 'TRUE', optical background
          correction is performed.

 verbose: Logical value. If 'TRUE' messages about the progress of the
          function is printed.

    fast: Logicalvalue. If 'TRUE' a faster add-hoc algorithm is used.

optimize.by: "speed" will use a faster algorithm but more RAM, and
          "memory" will be slower, but require less RAM.

_D_e_t_a_i_l_s:

     This method should require much less RAM than the conventional
     method of first creating an 'AffyBatch' and then running 'gcrma'.

     Note that this expression measure is given to you in log base 2
     scale. This differs from most of the other expression measure
     methods.

     The tuning factor 'k' will have different meanings if one uses the
     fast (add-hoc) algorithm or the empirical bayes approach. See Wu
     et al. (2003)

     'fast.bkg' and 'mem.bkg' are two internal functions.

_V_a_l_u_e:

     An 'exprSet'.

_A_u_t_h_o_r(_s):

     James W. MacDonald

_E_x_a_m_p_l_e_s:

