dksClassify              package:dualKS              R Documentation

_P_r_e_d_i_c_t _c_l_a_s_s_e_s _f_o_r _g_e_n_e _e_x_p_r_e_s_s_i_o_n _s_e_t_s.

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

     Kolmogorov-Smirnov rank sum scoring will be used to assign  one or
     more samples to one of two or more classes based on  previously
     defined gene signatures (see 'dksTrain').

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

     dksClassify(eset, classifier, rescale=FALSE, method="kort")

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

    eset: An 'ExpressionSet' or 'matrix' containing the gene 
          expression data for the samples to be classified.

classifier: An 'DKSClassifier' produced by  'dksSelectGenes' describing
          the gene expression  signature for each class.

 rescale: If TRUE, scores for each class will be mean centered  and
          normalized to remove arbitrary differences in scale and
          baseline  value between signatures for different classes.

  method: Two methods are supported.  The 'kort' method returns  the
          maximum of the running sum.  The 'yang' method  returns the
          sum of the maximum and the minimum of the  running sum,
          thereby penalizing classes that are highly enriched in a
          subset of genes of a given signature, but highly  down
          regulated in another subset of that same signature.

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

     An object of class 'DKSPredicted' containing the  class to which
     each sample in the 'eset' was assigned as  well as other
     information.  This object has its own 'summary'  and 'show'
     functions useful for displaying this information  in a user
     friendly format.

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

     Eric J. Kort, Yarong Yang

_S_e_e _A_l_s_o:

     'dksTrain', 'dksSelectGenes', 'dksClassify', 'DKSGeneScores',
     'DKSPredicted',  'DKSClassifier'

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

             data("dks")
             tr <- dksTrain(eset, 1, "up")
             cl <- dksSelectGenes(tr, 100)
             pr <- dksClassify(eset, cl,rescale=FALSE)
             summary(pr, pData(eset)[,1])
             show(pr)
             plot(pr, actual=pData(eset)[,1])        

