ROC                 package:cellHTS2                 R Documentation

_C_r_e_a_t_e_s _a_n _o_b_j_e_c_t _o_f _c_l_a_s_s "_R_O_C" _w_h_i_c_h _c_a_n _b_e _p_l_o_t_t_e_d _a_s _a _R_O_C _c_u_r_v_e

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

     The function 'ROC' construct an object of S4 class 'ROC', which
     represents a receiver-operator-characteristic curve, from the data
     of the annotated positive and negative controls in a scored
     'cellHTS' object.

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

     ## S4 method for signature 'cellHTS':
     ROC(object, positives, negatives)
     ## S4 method for signature 'ROC, missing':
     plot(x, col="darkblue", type="l", main = "ROC curve", ...)
     ## S4 method for signature 'ROC':
     lines(x, ...)

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

  object: a 'cellHTS' object which replicate data have already been
          scored and summarized (see details).

positives: a list or vector of regular expressions specifying the name
          of the positive control(s).  See the details for the argument
          'posControls' of 'writeReport' function. The default is
          '"^pos$"'.

negatives: a vector of regular expressions specifying the name of the
          negative control(s).  See the details for the argument
          'negControls' of 'writeReport' function. The default is
          '"^neg$"'.

       x: a 'ROC' object obtained using function 'ROC'.

     col: the graphical parameter for color; see 'par' for details.

    type: the graphical parameter giving the type of plot desired; see
          'par' for details.

    main: the graphical parameter giving the desired title of plot; see
          'par' for details.

     ...: other graphical parameters as in 'par' may be also passed as
          arguments.

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

     The 'cellHTS' object 'object' must be already scored
     ('state(object)["scored"]=TRUE'), and selection proceeds from
     large to small values of this single per-probe score. Furthermore,
     'object' is expected to contain positive and negative controls
     annotated in the column 'controlStatus' of the 'featureData' slot
     - which can be accessed via 'wellAnno(object)'. The arguments
     'positives' and 'negatives' should be given as regular expression
     patterns specifying the name of the positive(s) and negative(s)
     controls, respectivey.  By default, if 'positives' is not given,
     _pos_ will be taken as the name for the wells containing positive
     controls. Similarly, if 'negatives' is missing, by default _neg_
     will be considered as the name used to annotate the negative
     controls.  The content of 'posControls' and 'negControls' are
     passed to 'regexpr' for pattern matching within the well
     annotation (see examples for 'summarizeChannels').  If the assay
     is a two-way experiment, 'positives' should be a list with
     components 'act'  and 'inh', specifying the name of the
     activators, and inhibitors, respectively. In this case, the ROC
     curve is constructed based on the absolute values of
     'Data(object)'.

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

     An S4 object of class 'ROC'. There are methods 'show', 'plot' and
     'lines'.

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

     Ligia P. Bras ligia@ebi.ac.uk

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

         data(KcViabSmall)
         x <- normalizePlates(KcViabSmall, scale="multiplicative", log=FALSE, method="median", varianceAdjust="byExperiment")
         x <- scoreReplicates(x, sign="-", method="zscore")
         x <- summarizeReplicates(x, summary="mean")
         y <- ROC(x)
         plot(y)
         lines(y, col="green")
         show(y)

