screening.nl              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 _w_i_t_h _a _n_o_n_l_i_n_e_a_r _m_o_d_e_l.

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

     screening.nl(eset,genes,HVDM,transforms,cl1zscorelow,cl1modelscorehigh,cl1degraterange,criterion)

_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 kinetic parameter identifiers that
          have to be transformed during optimisation (optional) 

    HVDM: a HVDM object (see details) 

cl1zscorelow: the Vj 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 

criterion: criterion used to select the model. "BIC" (bayesian,
          default) or "AIC" (akaike) 

_D_e_t_a_i_l_s:

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

     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 Vj 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(0.01,5.0)). 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 as well as the  kinetic parameters (Vj
     and Kj) of the production function(through the transforms
     argument). This forces the values for 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, "Dj": degradation rate. The entry label
     indicates the transform to be applied; presently,

     The argument 'criterion' specifies which criterion should be used
     for model selection.

_V_a_l_u_e:

     a list containing the results.

_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','training','fitgene.nl'

_E_x_a_m_p_l_e_s:

     #load data and fit a linear model
     data(HVDMexample)
     rm(fiveGyMAS5)
     data(HVDMexample2)
     tp532<-training(eset=twodosesMAS5,genes=p53traingenes,degrate=0.8)
     #formulate constraints
     CONSTRAINTS<-c(350,35)
     names(CONSTRAINTS)<-c("trfact1.5Gy.1.4","trfact1.hGy.1.4")
     #specify individual gene models
     GENEMODELS<-rep("MM",5)
     GENEMODELS[c(1,3)]<-"hill"
     names(GENEMODELS)<-p53traingenes
     #run the model
     tp53hyb<-training.nl(inputHVDM=tp532,constraints=CONSTRAINTS,genemodels=GENEMODELS)
     #launch screening
     screenlist<-screening.nl(eset=twodosesMAS5,genes=genestoscreen[c(1:5)],HVDM=tp53hyb)

