scores2calls            package:cellHTS2            R Documentation

_S_i_g_m_o_i_d_a_l _t_r_a_n_s_f_o_r_m_a_t_i_o_n _o_f _t_h_e _s_c_o_r_e _v_a_l_u_e_s _s_t_o_r_e_d _i_n _a _c_e_l_l_H_T_S _o_b_j_e_c_t _o_b_t_a_i_n_i_n_g _t_h_e _c_a_l_l _v_a_l_u_e_s _f_o_r _e_a_c_h _p_r_o_b_e.

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

     Apply a sigmoidal transformation with parameters z0 and lambda to
     the summarized scored values stored in a 'cellHTS' object.  The
     obtained results are called _calls_ and are stored in slot
     'assayData', overridding its current content.

     Currently this function is implemented only for single-color data.

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

     scores2calls(x, z0, lambda)

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

       x: an object of class 'cellHTS' containing replicate data that
          have already been scored and summarized (see details).

      z0: a numeric value giving the centre of the sigmoidal
          transformation. See details.

  lambda: a numeric value (>0) that corresponds to the parameter
          'lambda' of the sigmoidal transformation. This value should
          be '>0', but usually it makes more sense to use a value
          '>=1'. See details.

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

     This function applies a sigmoidal transformation with parameters
     z0 and lambda to the single per-probe score values stored  in a
     'cellHTS' object. The obtained results are called _calls_. The
     transformation is given by:


                   1 / (1 + exp(-lambda * (z- z0)))


     where 'z' are the score values, 'z0' is the centre of the
     sigmoidal transformation, and the 'lambda' is a parameter that
     controls the smoothness of the transformation. The higher is
     'lambda', more steeper is the transition from lower to higher
     values. 'lambda' should be '> 0', but usually it makes more sense
     to use a value '>=1'.

     This transformation maps the score values to the interval '[0,1]',
     and is intended to expand the scale of scores with intermediate
     values and shrink the ones showing extreme values, therefore
     making the difference between intermediate phenotypes larger.

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

     The 'cellHTS' object with the call values stored in slot
     'assayData'. This is an object of class 'assayData' corresponding
     to a single matrix of dimensions 'Features x 1'.

_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', 'scoreReplicates',
     'summarizeReplicates', 'imageScreen'.

_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")
         xc <- scores2calls(x, z0=1.5, lambda=2) 
         plot(Data(x), Data(xc), col="blue", xlab="z-scores", ylab="calls", main=expression(1/(1+e^{-lambda *(z-z[0])})))
         if(require(splots)) {
          sp = split(Data(xc), plate(xc))
          grid.newpage()
          plotScreen(sp, zrange=c(0,1), fill=c("white", "red"), na.fill="yellow",
                    main="Calls", ncol=3L)
         }

