FLOC                 package:BicARE                 R Documentation

_P_e_r_f_o_r_m_s _t_h_e _F_L_O_C _a_l_g_o_r_i_t_h_m

_D_e_s_c_r_i_p_t_i_o_n:

     Find a given number of biclusters using the a modified version of
     the FLOC algorithm.

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

     FLOC(Data, k = 20, pGene = 0.5, pSample=pGene, r = NULL, N = 8, M = 6, t = 500, blocGene = NULL, blocSample = NULL)

_A_r_g_u_m_e_n_t_s:

    Data: an 'ExpressionSet-class' or a matrix (with genes on rows and
          conditions on columns)

       k: the number of biclusters searched

   pGene: genes initial probability of membership to the biclusters

 pSample: samples initial probability of membership to the biclusters

       r: the residue threshold

       N: minimal number of gene per bicluster

       M: minimal number of conditions per bicluster

       t: number of iterations

blocGene: a matrix indicating the directed initialisation for the genes
          (see details)

blocSample: a matrix indicating the directed initialisation for the
          conditions (see details)

_D_e_t_a_i_l_s:

     This biclustering algorithm is based on the FLOC algorithm
     (FLexible Overlapped biClustering) defined by Yang et al. (see
     references). It can discover a set of 'k', possibly overlapping,
     biclusters. If 'r' is set to NULL, the residue threshold used in
     the analysis is the residue of 'Data' divided by 10.

     'blocGene' and 'blocSample' are matrix of 0 and 1 with the rows
     representing the features (gene or samples) and the columns the
     biclusters. A 1 on line i and column j indicates that the feature
     i (gene or sample) will be include in the bicluster j during the
     initialisation step and will not be removed from it during the
     analysis. If the number of columns in these matrices is different
     from the number of bicluster searched, 'k' is set to the maximal
     value of these two.

     See 'bicluster' to extract a bicluster from the biclustering
     result.

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

     Returns an object of class 'biclustering', a list containing at
     least : 

    Call: the matched call.

ExpressionSet: the data used

   param: a data.frame with the algorithm parameters

  bicRow: a matrix of boolean indicating the belonging of the genes to
          the biclusters

  bicCol: the same as for bicRow but for the conditions

mat.resvol.bic: a matrix describing the biclusters

_A_u_t_h_o_r(_s):

     Pierre Gestraud (pierre.gestraud@curie.fr)

_R_e_f_e_r_e_n_c_e_s:

     J. Yang, H. Wang, W. Wang, and P.S. Yu. An improved biclustering
     method for analyzing gene expression. International Journal on
     Artificial Intelligence Tools, 14(5):771-789, 2005

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

     data(sample.bicData)     ## subset of sample.ExpressionSet from Biobase
     residue(sample.bicData)  ##  0.3401921
     resBic <- FLOC(sample.bicData, k=10, pGene=0.5,r=0.05,N=8,M=10,t=500)
     resBic

     ## initialising samples of 2 biclusters
     iniSample <- matrix(0, ncol=2, nrow=26)
     ## first bicluster initialised around Female cases
     iniSample[pData(sample.bicData)$sex=="Female",1] <- 1
     ## second bicluster initialised around control cases
     iniSample[pData(sample.bicData)$type=="Control",2] <- 1
     resBic <- FLOC(sample.bicData, k=10, pGene=0.5, r=0.05, N=8, M=10, t=500, blocSample=iniSample)
     resBic

