training                package:rHVDM                R Documentation

_P_e_r_f_o_r_m_s _t_h_e _H_V_D_M _t_r_a_i_n_i_n_g _s_t_e_p _a_n_d _r_e_t_u_r_n_s _a _l_i_s_t _c_o_n_t_a_i_n_i_n_g _t_h_e _r_e_s_u_l_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     This method performs the training step of the HVDM algorithm. It
     returns a list that will then be used in the subsequent screening
     step.

_U_s_a_g_e:

     training(eset,genes,transforms,degrate,actname,pdata,forcetransforms)

_A_r_g_u_m_e_n_t_s:

    eset: an ExpressionSet object (Biobase) 

   genes: a vector containg the gene identifiers of the training genes 

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

 degrate: value of the anchoring gene degradation rate, expressed in
          inverse unit time (optional) 

   pdata: a data frame, phenoData to be used for the training
          (optional)

 actname: name of the transcription factor of interest (optional)

forcetransforms: Boolean, whether the transformation in argument
          transforms have to be applied 

_D_e_t_a_i_l_s:

     The first entry in the genes vector is the anchoring gene. This
     means that the sensitivity (Sj) for this genes is set at 1.0 by
     default and that if a degradation rate is supplied it applies to
     that gene.

     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, set the
     'forcetransforms' switch to FALSE. 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").

     The 'degrate' argument is optional, but it is recommended to
     provide the algorithm with an externally measured degradation
     rate, as this greatly improves the accuracy and robustness of the
     outcome.

     The 'pdata' argument is also optional. By default the method will
     use the phenoData contained in the expression set. This argument
     can be used for excluding a time point, or an entire replicate. To
     extract the phenoData from the expression set,  use
     'dataframe<-pData(eset)'. The 'dataframe' object obtained can then
     be manipulated as desired.

     The default name of the transcription factor is "trfact1".

_V_a_l_u_e:

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

_N_o_t_e:

     It is recommended to run the HVDMcheck method before running this
     command.

_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:

     'HVDMcheck','screening','fitgene','HVDMreport'

_E_x_a_m_p_l_e_s:

     data(HVDMexample)
     tHVDMp53<-training(eset=fiveGyMAS5,genes=p53traingenes,degrate=0.8,actname="p53")

