scoreReplicates           package:cellHTS2           R Documentation

_S_c_o_r_e_s _n_o_r_m_a_l_i_z_e_d _r_e_p_l_i_c_a_t_e _v_a_l_u_e_s _g_i_v_e_n _i_n _a _c_e_l_l_H_T_S _o_b_j_e_c_t

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

     This function scores the normalized replicate values stored in
     slot 'assayData' of a 'cellHTS' object. Current available options
     are to take the _z_-score value or the per-replicate normalized
     percent inhibition (NPI). Data are stored in slot 'assayData'
     overridding its current content.

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

     scoreReplicates(object, sign="+", method="zscore", ...)

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

  object: an object of class 'cellHTS' that has already been
          normalized.

    sign: a character string, either "+" (default) or "-", which
          corresponds to multiplying the data by '+1' or '-1',
          respectively, after applying the scoring method specified by
          argument 'method'. See details.

  method: a character string indicating which method to use to score
          the replicate measurements. Available options are "none",
          "zscore" (default), "NPI". See details.

     ...: additional parameters required by some of the methods chosen
          in 'method'.

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

     This function scores the normalized values given in the slot
     'assayData' of 'object'. Current availabe scoring methods are:

        *  'method="none"', no scoring is applied.

        *  'method="zscore"' (robust _z_-scores), for each replicate,
           this is calculated by subtracting the overall median from
           each measurement and dividing the result by the overall
           'mad'. These are estimated for each replicate by considering
           the distribution of intensities (over all plates) in the
           wells whose content is annotated as 'sample'.

        *  'method="NPI"' (normalized percent inhibition applied in a
           per-replicate basis, i.e. using the overall mean of positive
           and negative controls across all plates of a given
           replicate), for each replicate, this method consists of
           subtracting each measurement from the average of the
           intensities on the positive controls (taken across all
           plates), and this result is then divided by the difference
           between the averages of the measurements on the positive and
           the negative controls (taken across all plates). If this
           method is chosen, one may need to provide further arguments
           to 'scoreReplicates', namely, arguments 'posControls' and
           'negControls'. These arguments should be vectors of regular
           expression patterns specifying the name of the positive(s)
           and negative(s) controls, respectivey, as provided in the
           plate configuration file. The length of these vectors should
           match the current number of channels in 'object' (i.e.
           'dim(Data(object))[3]'). By default, if 'posControls' or
           'negControls' are not given, _pos_ and _neg_ will be taken
           as the name for the wells containing positive or negative
           controls. The content of 'posControls' and 'negControls' is
           passed to 'regexpr' for pattern matching within the well
           annotation given in the 'featureData' slot of 'object'
           (which can be accessed via 'wellAnno(object)') (see examples
           for 'summarizeChannels'). 

     After replicate scoring using the chosen method, the value given
     in 'sign' ("+" or "-") is used to set the sign of the calculated
     scores.  For example, with a 'sign="-"', a strong decrease in the
     signal will be represented  by a positive score, whereas setting
     'sign="+"', such a phenotype will be represented by a negative
     score. This option can be set to calculate the results to the
     commonly used convention.

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

     A 'cellHTS' object with its slot 'assayData' replaced with the
     scored values (same dimension). 

     _Important:_ Note that the processing state "scored" of the
     'cellHTS' object is only updated to 'TRUE' after summarizing the
     replicates, which is the next preprocessing step (see
     'summarizeReplicates').

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

     W. Huber huber@ebi.ac.uk, Ligia Braz ligia@ebi.ac.uk

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

     Boutros, M., Bras, L.P. and Huber, W. (2006) Analysis of
     cell-based RNAi screens, _Genome Biology_ *7*, R66.

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

     'normalizePlates', 'summarizeChannels', 'summarizeReplicates'.

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

         data(KcViabSmall)
         x <- normalizePlates(KcViabSmall, scale="multiplicative", method="median", varianceAdjust="none")
         x <- scoreReplicates(x, sign="-", method="zscore")
         x <- summarizeReplicates(x, summary="min") # conservative approach

