fitgene                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 _a _p_a_r_t_i_c_u_l_a_r _g_e_n_e.

_D_e_s_c_r_i_p_t_i_o_n:

     This method fits the three kinetic parameter values for a
     particular gene. It returns a list containing the results.

_U_s_a_g_e:

     fitgene(eset,gene,tHVDM,transforms,firstguess,criterion)

_A_r_g_u_m_e_n_t_s:

    eset: an ExpressionSet object (Biobase) 

    gene: the gene identifier in character format 

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

   tHVDM: the output of the training set 

firstguess: first guess for the fitting (optional, see details)

criterion: model selection in the nonlinear case 

_D_e_t_a_i_l_s:

     An exponential transform is set by default for both the basal
     (Bj), degradation (Dj) rates (through the transforms argument) and
     possibly Kj (in case a nonlinear model is used). This forces the
     values for both these parameters to be positive. It also helps to
     reach a better fit. To turn this off let transforsm=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": degrdation rate. The entry label
     indicates the transform to be applied; presently, only
     log-tranforms are implemented (ie "exp").

     This 'fitgene()' step can only be applied after a 'training()'
     step. The output to the 'training()' step has to be fed through 
     the 'tHVDM' argument.

     The 'firstguess' argument is optional (a first guess is generated
     internally by default).  However a first guess can be supplied by
     the user which can take several forms.  It can either be a vector
     with three entries containing a first guess for the basal rate, 
     the sensitivity, the degradation rate (in that order). 
     Alternatively, another output from the 'fitgene()' function (for
     example from a gene that has a similar expression profile) can be
     supplied as a 'firstguess' argument.

     The 'criterion' argument is only used if the training object fed
     through the tHVDM command is a non-linear fit and determines the
     type of criterion used for model selection between Michelis-Menten
     and Hill. Possible values fed throught this argument are "BIC"
     (Bayesian information criterion, default) and "AIC" (Akaike
     information critertion). This argument is ignored in case of
     linear fitting.

_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 step.

_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','screening','HVDMreport'

_E_x_a_m_p_l_e_s:

     data(HVDMexample)
     tHVDMp53<-training(eset=fiveGyMAS5,genes=p53traingenes,degrate=0.8,actname="p53")
     sHVDMcd38<-fitgene(eset=fiveGyMAS5,gene="205692_s_at",tHVDM=tHVDMp53)

