generateExprSet-method         package:affy         R Documentation

_g_e_n_e_r_a_t_e _a _s_e_t _o_f _e_x_p_r_e_s_s_i_o_n _v_a_l_u_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Generate a set of expression values from the probe pair
     information. The set of expression is returned as an
     'ExpressionSet' object.

_U_s_a_g_e:

       computeExprSet(x, pmcorrect.method, summary.method, ...)

       generateExprSet.methods()

       upDate.generateExprSet.methods(x)

_A_r_g_u_m_e_n_t_s:

       x: a 'AffyBatch' holding the probe level informations to
          generate the expression values, for computeExprSet, and for
          upDate.generateExprSet.methods it is a character vector..

pmcorrect.method: the method used to correct PM values (see section
          'details').

summary.method: the method used to generate the expression value (see
          section 'details').

     ...: any of the options of the normalization you would like to
          modify

_D_e_t_a_i_l_s:

     An extra argument 'ids=' can be passed. It must be a vector of
     affids. The expression values will only be computed and returned
     for these affyids.

     The different methods available through this mecanism can be
     accessed by calling the method 'generateExprSet.methods' with an
     object of call 'Cel.container' as an argument.

     In the Affymetrix design, _MM_ probes were included to measure the
     noise (or background signal). The original algorithm for
     background correction was to subtract the _MM_ signal to the _PM_
     signal. The methods currently inclluded in the package are
     "bg.correct.subtractmm", "bg.correct.pmonly" and
     "bg.correct.adjust".

     To alter the available methods for generating ExprSets use
     upDate.generateExprSet.methods.

_S_e_e _A_l_s_o:

     method 'generateExprSet' of the class 'AffyBatch'
      'expresso'

_E_x_a_m_p_l_e_s:

     if (require(affydata)) {
       data(Dilution)

       ids <- c( "1000_at","1001_at")

       eset <- computeExprSet(Dilution, pmcorrect.method="pmonly",
                              summary.method="avgdiff", ids=ids)
     }

