activeNet             package:maigesPack             R Documentation

_F_u_n_c_t_i_o_n_a_l _c_l_a_s_s_i_f_i_c_a_t_i_o_n _o_f _g_e_n_e _n_e_t_w_o_r_k_s

_D_e_s_c_r_i_p_t_i_o_n:

     This function calculate a statistic for each gene network in each
     biological condition that measure the profile of activation of the
     network in that condition. Also the function measures the
     significance of the results.

_U_s_a_g_e:

     activeNet(data=NULL, samples=NULL, sLabelID="Classification",
               type="Rpearson", bRep=1000, alternative = "greater",
               adjP="none")

_A_r_g_u_m_e_n_t_s:

    data: object of class 'maiges' to be used to functionally classify
          gene networks stored in 'Paths' slot.

sLabelID: character string specifying identification of sample label to
          be used. 

 samples: a list with character vectors specifying the groups that must
          be compared.

    type: character string giving the type of correlation to be
          calculated. May be 'Rpearson' (default), 'pearson',
          'kendall', 'spearman' or 'MI'.

    bRep: integer number specifying the bootstraps to be done in the
          correlation test.

alternative: character string specifying the alternative hypotheses.
          May be 'greater' (default) to test the activity of the
          networks in accordance to the to the graph or 'less' to test
          the activity of the network antagonic to the graph.

    adjP: character string giving the type of p-value adjustment. May
          be 'Bonferroni', 'Holm', 'Hochberg', 'SidakSS', 'SidakSD',
          'BH', 'BY' or 'none'. Defaults to 'none'. See function
          'mt.rawp2adjp' in package multtest for more details.

_D_e_t_a_i_l_s:

     If the argument 'samples' is NULL, all types defined by the sample
     label given by 'sLabelID' are used. It is possible to use the
     'plot.maigesActNet' and 'image.maigesActNet' methods to display
     the results of this analysis.

_V_a_l_u_e:

     The result of this function is an object of class 'maigesActNet'.

_A_u_t_h_o_r(_s):

     Gustavo H. Esteves <gesteves@vision.ime.usp.br>

_S_e_e _A_l_s_o:

     'activeNetScoreHTML', 'maigesActNet', 'plot.maigesActNet',
     'image.maigesActNet', 'mt.rawp2adjp'

_E_x_a_m_p_l_e_s:

     ## Loading the dataset
     data(gastro)

     ## Doing functional classification of gene networks for sample Label
     ## given by 'Tissue'
     gastro.net = activeNet(gastro.summ, sLabelID="Tissue")

