bubbleplot              package:biclust              R Documentation

_B_u_b_b_l_e_p_l_o_t

_D_e_s_c_r_i_p_t_i_o_n:

     Draws a bubble plot where each bicluster is represented as a
     circle (bubble). Color represents the bicluster set to which
     bicluster pertains (up to three bicluster sets can be represented
     simultaneously). Brightness representes the bicluster homogeneity
     (darker, less homogeneous). Size represents the size of the
     bicluster, as (number of genes)x(number of conditions). Location
     is a 2D-projection of gene and condition profiles.

_U_s_a_g_e:

     bubbleplot(x, bicResult1, bicResult2=NULL, bicResult3=NULL, projection="mean", showLabels=FALSE)

_A_r_g_u_m_e_n_t_s:

       x: The data matrix from which biclusters were identified.

bicResult1: BiclustResult object with a bicluster result set whose
          biclusters will be drawn in green.

bicResult2: BiclustResult object with an optional second bicluster
          result set. Will be drawn in red (default NULL)

bicResult3: BiclustResult object with an optional third bicluster
          result set. Will be drawn in blue (default NULL)

projection: Projection algorithm used to position bubbles. Allowed
          projections are 'mean', 'isomds' and 'cmdscale' (default
          'mean'). See details section for a broader explanation.

showLabels: If 'TRUE', puts a label over each bubble that tells the
          number within the corresponding bicluster result (default
          'FALSE').

_D_e_t_a_i_l_s:

     Position of circles depend on a 2D projection of the
     multidimensional point formed by rows and columns present in the
     bicluster. For example, if we have a 3x3  matrix to analyze and we
     find a bicluster with rows 1 and 3 and columns 2 and 3, the
     corresponding multidimensional point will be p=(1,0,1,0,1,1). For
     this example, 'mean' projection will map the bicluster with the
     point  x=(1+3)/2=2 and y=(2+3)/2=2,5. Other projections will take
     the point p and project it following the corresponding algorithms
     (see the corresponding help pages for details)

_N_o_t_e:

     Bubbleplot 2D-projection, as any multidimensional scaling, loses
     information, trying to take the main relationships and trends of
     n-dimensional data. Thus, locations and intersections between
     bubbles-biclusters are only an estimate of its similarity. This
     visualization should be used just as a help to understand overall
     behavior of biclustering methods, detect trends and outliers, etc.

_A_u_t_h_o_r(_s):

     Rodrigo Santamaria rodri@usal.es

_S_e_e _A_l_s_o:

     'drawHeatmap' for single representation of biclusters inside data
     matrix, 'parallelCoordinates' for single representation of
     biclusters as lines of gene or condition profiles, 'cmdscale,
     isomds' for multidimensional scaling and 'plot' for other point
     representations.

_E_x_a_m_p_l_e_s:

       
       #Simplified yeast microarray data
       ## Not run:  
       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)
       bubbleplot(BicatYeast,bics1, showLabels=TRUE)

       loma=binarize(BicatYeast,2)
       bics2=biclust(loma,BCBimax(), minr=4, minc=4, number=10)
       bubbleplot(BicatYeast,bics1,bics2)
       ## End(Not run)
       

