BCSpectral              package:biclust              R Documentation

_T_h_e _S_p_e_c_t_r_a_l _B_i_c_l_u_s_t_e_r _a_l_g_o_r_i_t_h_m

_D_e_s_c_r_i_p_t_i_o_n:

     Performs Spectral Biclustering as described in Kluger et al.,
     2003. Spectral biclustering supposes that normalized microarray
     data matrices have a checkerboard structure that can be discovered
     by the use of svd decomposition in eigenvectors, applied to genes
     (rows) and conditions (columns).

_U_s_a_g_e:

     ## S4 method for signature 'matrix, BCSpectral':
     biclust(x, method=BCSpectral(), normalization="log", numberOfEigenvalues=3, 
                 minr=2, minc=2, withinVar=1)

_A_r_g_u_m_e_n_t_s:

       x: The data matrix where biclusters are to be found

  method: Here BCSpectral, to perform Spectral algorithm

normalization: Normalization method to apply to mat. Three methods are
          allowed as described by Kluger et al.: "log" (Logarithmic
          normalization), "irrc" (Independent Rescaling of Rows and
          Columns) and "bistochastization". If "log"  normalization is
          used, be sure you can apply logarithm to elements in data
          matrix, if there are values under 1, it automatically will
          sum to each element in mat (1+abs(min(mat))) Default is
          "log", as recommended by Kluger et al.

numberOfEigenvalues: the number of eigenValues considered to find
          biclusters. Each row (gene) eigenVector will be combined with
          all column (condition) eigenVectors for the first
          numberOfEigenValues eigenvalues. Note that a high number
          could increase dramatically time performance. Usually, only
          the very first eigenvectors are used. With "irrc" and
          "bistochastization" methods, first eigenvalue contains
          background (irrelevant) information, so it is ignored.

    minr: minimum number of rows that biclusters must have. The
          algorithm will not consider smaller biclusters.

    minc: minimum number of columns that biclusters must have. The
          algorithm will not consider smaller biclusters.

withinVar: maximum within variation allowed. Since spectral
          biclustering outputs a checkerboard structure despite of
          relevance of individual cells, a filtering of only relevant
          cells is necessary by means of this within variation
          threshold.

_V_a_l_u_e:

     Returns an object of class 'Biclust'.

_A_u_t_h_o_r(_s):

     Rodrigo Santamaria rodri@usal.es

_R_e_f_e_r_e_n_c_e_s:

     Kluger et al., "Spectral Biclustering of Microarray Data:
     Coclustering Genes and Conditions", Genome Research, 2003, vol.
     13, pages 703-716

_E_x_a_m_p_l_e_s:

       #Random matrix with embedded bicluster  
       test <- matrix(rnorm(5000),100,50)
       test[11:20,11:20] <- rnorm(100,10,0.1)
       res1 <- biclust(test, method=BCSpectral(), numberOfEigenvalues=1)
       res1
       

