screening               package:rHVDM               R Documentation

_F_i_t_s _t_h_e _o_p_t_i_m_a_l _k_i_n_e_t_i_c _p_a_r_a_m_e_t_e_r _v_a_l_u_e_s _f_o_r _s_e_v_e_r_a_l _g_e_n_e_s.

_D_e_s_c_r_i_p_t_i_o_n:

     This method fits the three kinetic parameter values for each gene
     in a user-supplied vector.  It returns a list containing the
     results.

_U_s_a_g_e:

     screening(eset,genes,HVDM,transforms,cl1zscorelow,cl1modelscorehigh,cl1degraterange)

_A_r_g_u_m_e_n_t_s:

    eset: an ExpressionSet object (Biobase) 

   genes: a vector containing the genes identifiers to be screened (in
          character format) 

transforms: a vector containing the kintetic parameter identifiers that
          have to be transformed during optimisation (optional) 

    HVDM: a HVDM object (see details) 

cl1zscorelow: the sensitivity Z-score cutoff value for a gene to be
          classified as a putative target 

cl1modelscorehigh: the model score cutoff value for a gene to be
          classified as a putative target 

cl1degraterange: the degradation rate bounds applied for a gene to be
          classified as a putative target 

_D_e_t_a_i_l_s:

     This screening step can only be applied if a 'training()' step has
     already been run.  The output to the 'training()' step can be
     given as the "HVDM" argument. A fit of each gene in the "genes"
     argument is then performed. 

     Alternatively an output to a previously run 'screening()' step can
     be supplied as an "HVDM" argument. In this case, the fit is not
     performed once again. Feeding a previous output of 'screening()'
     to that same function again is useful only if the various bounds
     altered in the secon run. In the case this option is used, the
     "eset" and "genes" arguments do not have to be supplied (they will
     be ignored anyway).

     The output of that function is a list containing the results. The
     relevant data frame is in the "results" member of the output. 
     Putative targets can be identified using the "class1" field of
     that data frame (see example).

     Bounds determining whether a gene is a target of the transcription
     factor under review have to be supplied. They are:

     - cl1zscorelow: lower bound for the sensitivity Z-score (default:
     2.5)

     - cl1modelscore: upper bound for the model score (default: 100.0).
      This default will have to be changed in most cases. As a rule of
     thumb,   5x the model score for the genes in the training set can
     be used.

     - cl1degraterange: lower and upper bounds for the degradation rate
     (default: c(0.05,5.0)). This is to  exclude those genes with an
     absurd degradation rate, measured in (unit time)\^(-1). In our
     example the unit time is an hour. In the case the unit time is
     different, these default bounds will have to be altered
     accordingly.

     An exponential transform is set by default for both the basal (Bj)
     and degradation (Dj) rates (through the transforms argument). This
     forces the values for both these parameters to be positive. It
     also helps to reach a better fit. To turn this off let
     transforms=c(). Even in this case the degradation rate will not be
     allowed to take non positive values as it causes problems with the
     differential operator used internally. The value in the vector
     indicates the parameter to be transformed: "Bj": basal rate of
     transcription, "Sj": sensitivity, "Dj": degradation rate. The
     entry label indicates the transform to be applied; presently, only
     log-tranforms are implemented (ie "exp").

_V_a_l_u_e:

     a list containing the results (see documentation for more
     details).

_N_o_t_e:

     Obviously, the expression set given as a 'eset' argument has to be
     the same as the one used for the training set.

_A_u_t_h_o_r(_s):

     Martino Barenco

_R_e_f_e_r_e_n_c_e_s:

     M. Barenco, D. Tomescu, D. Brewer, R. Callard, J. Stark, M. Hubank
     (2006) Ranked predictions of p53 targets using Hidden Variable
     Dynamic Modelling. _Genome Biology_, *V7(3)*, R25.

_S_e_e _A_l_s_o:

     'training','HVDMreport','fitgene'

_E_x_a_m_p_l_e_s:

     data(HVDMexample)
     tHVDMp53<-training(eset=fiveGyMAS5,genes=p53traingenes,degrate=0.8,actname="p53")
     screenp53<-screening(eset=fiveGyMAS5,genes=genestoscreen[1:10],HVDM=tHVDMp53)

     #extracting a list of putative p53 targets
     p53targets<-screenp53$results[screenp53$results$class1,]

     #shifting the goal posts
     screenp53B<-screening(HVDM=screenp53,cl1zscorelow=3.5)

