coherence              package:biclust              R Documentation

_C_o_h_e_r_e_n_c_e _m_e_a_s_u_r_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Different prelilminary measures of how much constant or (additive,
     multiplicative, sign) coherent a bicluster is, following Madeira
     and Oliveira classification of biclusters.

_U_s_a_g_e:

     constantVariance(x, resultSet, number, dimension="both")
     additiveVariance(x, resultSet, number, dimension="both")
     multiplicativeVariance(x, resultSet, number, dimension="both")
     signVariance(x, resultSet, number, dimension="both")

_A_r_g_u_m_e_n_t_s:

       x: The data matrix from which biclusters were identified

resultSet: BiclustResult object with a bicluster result set where is
          the bicluster to measure

  number: Number of the bicluster withing the result set

dimension: "both" for determining overall variance, "row" for gene
          variance and "col" for column variance. Default "both"

_D_e_t_a_i_l_s:

     Returns the corresponding variance of genes or conditions as the
     average of the sum of euclidean distances between all rows and/or
     columns of the bicluster. For additive, multiplicative and sign
     variance first a transformation of the bicluster is done, so
     variance is computed on a matrix that reflects difference, rest or
     change of sign between rows, columns or both.

     The lower the value returned, the more constant or coherent the
     bicluster is. If the value returned is 0, the bicluster is ideally
     constant or coherent. Usually, a value above 1-1.5 is enough to
     determine the bicluster is not constant or coherent.

_N_o_t_e:

     There are preliminary measures for coherence. Since
     transformations are different, measures are not normalized and
     comparison between, for example, additive and multiplicative
     variance is not meaningful. Only comparisons between different
     measures of the same kind of variance are reliable by now.

_A_u_t_h_o_r(_s):

     Rodrigo Santamaria rodri@usal.es

_E_x_a_m_p_l_e_s:

       #Simplified yeast microarray data
       data(BicatYeast)
       set.seed(1)
       bics1 <- biclust(BicatYeast,BCPlaid(), back.fit = 2, shuffle = 3, fit.model = ~m + a + b,
       row.release = 0.7, col.release = 0.7,
       verbose = FALSE, max.layers = 10, iter.startup = 5,
       iter.layer = 30)
       
       constantVariance(BicatYeast, bics1,1,"row")
       constantVariance(BicatYeast, bics1,1,"col")
       constantVariance(BicatYeast, bics1,1,"both")
       additiveVariance(BicatYeast, bics1,1,"both")
       multiplicativeVariance(BicatYeast, bics1,1,"both")
       signVariance(BicatYeast, bics1,1,"both")
       

