getZfactor             package:cellHTS2             R Documentation

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

     Calculates per-experiment Z'-factor of data stored in a 'cellHTS'
     object. The Z'-factor is a measure that quantifies the separation
     between the distribution of positive and negative controls.

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

     getZfactor(x, 
     robust=TRUE,
     verbose=interactive(), 
     posControls, 
     negControls)

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

       x: a configured 'cellHTS' object. See details.

  robust: a logical, if 'TRUE' the Z'-factor is calculated using the
          median and MAD instead of mean and standard deviation,
          respectively.

 verbose: a logical, if 'TRUE' the function reports some of its
          intermediate progress. The default is the state of
          'interactive()'.

posControls: (optional) a list or vector of regular expressions
          specifying the name of the positive controls. See details.

negControls: (optional) a vector of regular expressions specifying the
          name of the negative controls. See details.

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

     'x' should be an already configured 'cellHTS' object
     ('state(x)["configured"]=TRUE'), so that the information about the
     well annotation of the plates is available.

     The per-experiment Z'-factor values are calculated for the data
     stored in slot 'assayData' of 'x'.

     If 'robust=TRUE' (default), the Z'-factor is calculated using
     robust estimates of location (median) and spread (mad).

     'posControls' and 'negControls' should be given as a vector of
     regular expression patterns specifying the name of the positive(s)
     and negative(s) controls, respectivey, as provided in the plate
     configuration file  (and accessed via 'wellAnno(x)'). The length
     of these vectors should be equal to the current number of channels
     in  'x' ('dim(Data(x))[3]'). By default, if 'posControls' is not
     given, 'pos' will be taken as the name for the wells containing
     positive controls. Similarly, if 'negControls' is missing, by
     default 'neg' will be considered as the name used to annotated the
     negative controls.  The content of 'posControls' and 'negControls'
     will be passed to 'regexpr' for pattern matching within the well
     annotation given in 'wellAnno(x)' (see examples). If no controls
     are available for a given channel, use '""' or 'NA' for that
     channel. For example, 'posControls = c("", "(?i)^diap$")' means
     that channel 1 has no positive controls, while 'diap' is the
     positive control for channel 2.

     The arguments 'posControls' and 'negControls' are particularly
     useful in multi-channel data since the controls might be
     reporter-specific, or after normalizing multi-channel data.

     If there are different positive controls, the Z'-factor is
     calculated between each of the positive controls and the negative
     controls.

     In the case of a two-way assay, where two types of "positive"
     controls are used in the screen ("activators" and "inhibitors"),
     'posControls' should be defined as a list with two components
     (called 'act' and 'inh'), each of which should be vectors of
     regular expressions of the same length as the current number of
     reporters (as explained above).    The Z'-factor values are
     calculated between each type of positive control ('activators' or
     'inhibitors') and the negative controls.

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

     The function generates a list with the per-experiment Z'-factor
     values in each channel and each replicate.  Each element of this
     list is a matrix with dimensions 'nrReplicates x nrChannels', and
     is named by the positive controls. In the case of a two-way assay,
     these elements are called 'activators' and 'inhibitors', while for
     a one-way assay, the elements have the same name of the positive
     controls. See examples section.

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

     Ligia P. Bras ligia@ebi.ac.uk

_R_e_f_e_r_e_n_c_e_s:

     Zhang, J.H., Chung, T.D. and Oldenburg, K.R. (1999) A simple
     statistical parameter for use in evaluation and validation of high
     throughput screening assays, _J. Biomol. Screen._ *4*(2), 67-73.

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

     'configure',  'writeReport'

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

         data(KcViabSmall)
         ## pCtrls <- c("pos") 
         ## nCtrls <- c("neg") 
         ## or for safety reasons (not a problem for the current well annotation, however) 
         pCtrls <- c("^pos$") 
         nCtrls <- c("^neg$")
         zf <- getZfactor(KcViabSmall, robust=TRUE, posControls=pCtrls, negControls=nCtrls)
         
         x <- normalizePlates(KcViabSmall, scale="multiplicative", log=FALSE, method="median", varianceAdjust="none")
         zfn <- getZfactor(x)

