relNetworkB            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 for one biological type
     (Butte's Relevance Network).

_U_s_a_g_e:

     relNetworkB(data=NULL, gLabelID="GeneName", sLabelID="Classification",
                 geneGrp=NULL, path=NULL, samples=NULL,
                 type="Rpearson", bRep=1000, ...)

_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 character vector specifying the group to be compared.

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

    bRep: integer specifying the number of bootstrap permutation to
          calculate the significance of correlation values.

     ...: 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, 'robustCorr' to calculate a robust correlation
     using an idea similar to the leave-one-out or 'MI' to calculate
     mutual information values.

_V_a_l_u_e:

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

_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:

     Butte, A.J. and Kohane, I.S. Unsupervised Knowledge discovery in
     medical databases using relevance networks. In Proc. AMIA Symp.,
     711-715, 1999 (<URL:
     http://www.amia.org/pubs/symposia/D005550.HTM>)

     Butte, A.J.; Tamayo, P.; Slonim, D.; Golub, T.R. and Kohane, I.S.
     Discovering functional relationships between RNA expression and
     chemotherapeutic susceptibility using relevance networks, *PNAS*,
     97, 12182-12186, 2000 (<URL:
     http://www.pnas.org/cgi/content/full/97/22/12182>)

     Butte, A.J. and Kohane, I.S. Mutual information relevance
     networks: functional genomic clustering using pairwise entropy
     measurements. In Pacific Symposium on Biocomputing, 5, 415-426,
     2000 (<URL:
     http://psb.stanford.edu/psb-online/proceedings/psb00/>)

_S_e_e _A_l_s_o:

     'cor', 'robustCorr', 'MI' 'maigesRelNetB', 'plot.maigesRelNetB',
     'image.maigesRelNetB'.

_E_x_a_m_p_l_e_s:

     ## Loading the dataset
     data(gastro)

     ## Constructing the relevance network (Butte's method) for sample
     ## 'Tissue' equal to 'Neso' for the 1st gene group
     gastro.net = relNetworkB(gastro.summ, sLabelID="Tissue", 
       samples="Neso", geneGrp=1, type="Rpearson")

     ## Constructing the relevance network (Butte's method) for sample
     ## 'Type' equal to 'Col' for the 1st gene group using the conventional
     ## pearson correlation
     gastro.net = relNetworkB(gastro.summ, sLabelID="Type", 
       samples="Col", geneGrp=1, type="pearson")

