affycompTable            package:affycomp            R Documentation

_E_x_p_r_e_s_s_i_o_n _A_s_s_e_s_s_m_e_n_t _T_a_b_l_e

_D_e_s_c_r_i_p_t_i_o_n:

     These functions takes as an argument the output of the asessment
     functions.

_U_s_a_g_e:

     affycompTable(...,Table=NULL,assessment.list=NULL,method.names=NULL)

     tableAll(...,assessment.list=NULL,method.names=NULL)

     tableDilution(l, method.names=NULL)

     tableFC(l, method.names=NULL)

     tableFC2(l, method.names=NULL)

     tableSignal(l, method.names=NULL)

     tableOverallSNR(...,assessment.list=NULL,method.names=NULL,ngenes=12626)

     tableLS(...,assessment.list=NULL,method.names=NULL,ngenes=12626,rank=TRUE)

_A_r_g_u_m_e_n_t_s:

     ...: lists produced by the assessment functions

   Table: If 'TableAll' was used one can send it through this argument

assessment.list: Alternatively, one can also send a list of lists
          produced by 'tableAll'.

method.names: A character vector with the names of the epxression
          measure methodology.

       l: list of assessments.

    rank: if 'TRUE' 'tableLS' will present ranks instead of local
          slopes.

  ngenes: when computing ranks, out of how many genes should we do it?

_D_e_t_a_i_l_s:

     Read the vignette for more details on what the entries of the
     table are. 'affycompTable' has a few entries per graph. 'tableAll'
     has more entries. Once an assessment is used this function knows
     what to do. You can call any of the assessment functions described
     in 'assessSpikeIn', 'assessDilution' and 'assessSD'.

     Note 'tableLS' and 'tableOverallSNR' work on the results from
     'assessSpikeIn2'.

_V_a_l_u_e:

     A matrix. One column per each method and one row for each
     comparison.

_A_u_t_h_o_r(_s):

     Rafael A. Irizarry

_E_x_a_m_p_l_e_s:

     data(rma.assessment) ##this was produced with affycomp.assess
     data(mas5.assessment) ##this one too
     tmp <- affycompTable(mas5.assessment,rma.assessment)
     format(tmp,digit=2)

