venn                  package:limma                  R Documentation

_V_e_n_n _D_i_a_g_r_a_m_s

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

     Compute classification counts or plot classification counts in a
     Venn diagram.

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

     vennCounts(x, include="both")
     vennDiagram(object, include="both", names, mar=rep(1,4), cex=1.5, ...)

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

       x: numeric matrix of 0's and 1's indicating significance of a
          test. Usually created by 'classifyTests'.

  object: either a 'TestResults' matrix or a 'VennCounts' object
          produced by 'vennCounts'.

 include: character string specifying whether to counts genes
          up-regulated, down-regulated or both. Choices are '"both"',
          '"up"' or '"down"'.

   names: optional character vector giving names for the sets or
          contrasts

     mar: numeric vector of length 4 specifying the width of the
          margins around the plot. This argument is passed to 'par'.

     cex: numerical value giving the amount by which the contrast names
          should be scaled on the plot relative to the default.plotting
          text. See 'par'.

     ...: any other arguments are passed to 'plot'

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

     'vennCounts' produces a 'VennCounts' object, which is a numeric
     matrix with last column '"Counts"' giving counts for each possible
     vector outcome.

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

     Gordon Smyth and James Wettenhall

_S_e_e _A_l_s_o:

     An overview of linear model functions in limma is given by
     5.LinearModels.

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

     tstat <- matrix(rt(300,df=10),100,3)
     tstat[1:33,] <- tstat[1:33,]+2
     clas <- classifyTestsF(tstat,df=10,p.value=0.05)
     a <- vennCounts(clas)
     print(a)
     vennDiagram(a)

