relNetworkM            package:maigesPack            R Documentation

_R_e_l_e_v_a_n_c_e _N_e_t_w_o_r_k _a_n_a_l_y_s_i_s

_D_e_s_c_r_i_p_t_i_o_n:

     Function to construct Relevance Networks comparing two distinct
     biological types.

_U_s_a_g_e:

     relNetworkM(data=NULL, gLabelID="GeneName", sLabelID="Classification",
                 geneGrp=NULL, path=NULL, samples=NULL,
                 type="Rpearson", ...)

_A_r_g_u_m_e_n_t_s:

    data: object of class 'maiges'.

gLabelID: character string giving the identification of gene label ID.

sLabelID: character string giving the identification of sample label
          ID.

 geneGrp: character string (or numeric index) specifying the gene group
          to calculate the correlation values between them. If NULL
          (together with path) all genes are used.

    path: character string (or numeric index) specifying the gene
          network to calculate the correlation values between them. If
          NULL (together with geneGrp) all genes are used.

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

    type: type of correlation to be calculated. May be 'Rpearson'
          (default), 'pearson', 'kendall' or 'spearman'.

     ...: additional parameters for functions 'robustCorr' or 'cor'.

_D_e_t_a_i_l_s:

     This method uses the function 'cor' to calculate the usual
     correlation values or 'robustCorr' to calculate a robust
     correlation using an idea similar to the leave-one-out. The
     correlation values are calculated for pairs of genes in the two
     groups specified by the argument 'samples', then a Fisher's Z
     transformation are done to calculate the significance for the
     difference between the two correlation values, this is implemented
     in the function 'compCorr'. This method was first used in the work
     from Gomes et al. (2005).

_V_a_l_u_e:

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

_A_u_t_h_o_r(_s):

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

_R_e_f_e_r_e_n_c_e_s:

     Gomes, L.I.; Esteves, G.H.; Carvalho, A.F.; Cristo, E.B.; Hirata
     Jr., R.; Martins, W.K.; Marques, S.M.; Camargo, L.P.; Brentani,
     H.; Pelosof, A.; Zitron, C.; Sallum, R.A.; Montagnini, A.; Soares,
     F.A.; Neves, E.J. & Reis, L.F. Expression Profile of Malignant and
     Nonmalignant Lesions of Esophagus and Stomach: Differential
     Activity of Functional Modules Related to Inflammation and Lipid
     Metabolism, *Cancer Research*, 65, 7127-7136, 2005 (<URL:
     http://cancerres.aacrjournals.org/cgi/content/abstract/65/16/7127>)

_S_e_e _A_l_s_o:

     'cor', 'robustCorr' 'compCorr', 'maigesRelNetM',
     'plot.maigesRelNetM', 'image.maigesRelNetM'.

_E_x_a_m_p_l_e_s:

     ## Loading the dataset
     data(gastro)

     ## Constructing the relevance network for sample
     ## 'Tissue' comparing 'Neso' and 'Aeso' for the 1st gene group
     gastro.net = relNetworkM(gastro.summ, sLabelID="Tissue", 
       samples = list(Neso="Neso", Aeso="Aeso"), geneGrp=11,
       type="Rpearson")

