BCPlaid               package:biclust               R Documentation

_T_h_e _P_l_a_i_d _M_o_d_e_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 Plaid Model Biclustering as described in Turner et al.,
     2003. This is an improvement of original 'Plaid Models for Gene
     Expression Data' (Lazzeroni and Owen, 2002). This algorithm models
     data matrices to a sum of layers, the model is fitted to data
     through minimization of error.

_U_s_a_g_e:

     ## S4 method for signature 'matrix, BCPlaid':
     biclust(x, method=BCPlaid(), cluster="b", fit.model = y ~ m + a + b, 
       background = TRUE, row.release = 0.7, col.release = 0.7, 
       shuffle = 3, back.fit = 0, max.layers = 20,
       iter.startup = 5, iter.layer = 10, verbose = TRUE)

_A_r_g_u_m_e_n_t_s:

       x: The data matrix where biclusters have to be found

  method: Here BCPlaid, to perform Plaid algorithm

 cluster: 'r', 'c' or 'b', to cluster rows, columns or both (default
          'b')

fit.model: Model (formula) to fit each layer. Usually, a linear model
          is used, that stimates three parameters: m (constant for all
          elements in the bicluster), a(contant for all rows in the
          bicluster) and b (constant for all columns). Thus, default
          is: y ~ m + a + b.

background: If 'TRUE' the method will consider that a background layer
          (constant for all rows and columns) is present in the data
          matrix.

 shuffle: Before a layer is added, it's statistical significance is
          compared against a number of layers obtained by random
          defined by this parameter. Default is 3, higher numbers could
          affect time performance. 

iter.startup: Number of iterations to find starting values 

iter.layer: Number of iterations to find each layer 

back.fit: After a layer is added, additional iterations can be done to
          refine the fitting of the layer (default set to 0)

row.release: Scalar in [0,1](with interval recommended [0.5-0.7]) used
          as threshold to prune rows in the layers depending on row
          homogeneity

col.release: As above, with columns 

max.layers: Maximum number of layer to include in the model 

 verbose: If 'TRUE' prints extra information on progress.

_V_a_l_u_e:

     Returns an Biclust object.

_A_u_t_h_o_r(_s):

     Adaptation of original code from Heather Turner from Rodrigo
     Santamaria rodri@usal.es. rodri@usal.es

_R_e_f_e_r_e_n_c_e_s:

     Heather Turner et al, "Improved biclustering of microarray data
     demonstrated through systematic performance tests",Computational
     Statistics and Data Analysis, 2003, vol. 48, pages 235-254.

     Lazzeroni and Owen, "Plaid Models for Gene Expression Data",
     Standford University, 2002.

_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(100,3,0.3)
       res<-biclust(test, method=BCPlaid())
       res

       #microarray matrix
       data(BicatYeast)
       res<-biclust(BicatYeast, method=BCPlaid(), verbose=FALSE)
       res
       

