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 'ExpressionSet' using the
     robust multi-array average (RMA) expression measure with help of
     probe sequences.

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

                 just.gcrma(..., filenames=character(0),
                            phenoData=new("AnnotatedDataFrame"),
                            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"),
                            cdfname = NULL, read.verbose = FALSE)

                 justGCRMA(..., 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"),
                          cdfname = NULL, read.verbose = FALSE)

_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 'AnnotatedDataFrame' 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', then normalize data using quantile
          normalization.

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

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

    fast: Logical value. If 'TRUE', then 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.

bgversion: integer value indicating which RMA background to use 1: use
          background similar to pure R rma background given in affy
          version 1.0 - 1.0.2 2: use background similar to pure R rma
          background given in affy version 1.1 and above.

 minimum: .

celfile.path: a character denoting the path 'ReadAffy' should look for
          cel files.

sampleNames: a character vector of sample names to be used in the
          'AffyBatch'.

 cdfname: Used to specify the name of an alternative cdf package. If
          set to 'NULL', the usual cdf package based on Affymetrix'
          mappings will be used. Note that the name should not include
          the 'cdf' on the end, and that the corresponding probe
          package is also required to be installed. If either package
          is missing an error will result.

read.verbose: Logical value. If 'TRUE', then messages will be printed
          as each celfile is read in.

_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 'ExpressionSet'.

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

     James W. MacDonald

