plotMA                 package:edgeR                 R Documentation

_M_A-_l_i_k_e _p_l_o_t _f_o_r _d_e_D_G_E_L_i_s_t _o_b_j_e_c_t_s

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

     Plots M (log-abundance ratio) against A (log-average abundance)
     for two groups.  A smear of points is shown on the left side for
     those genes with 0 counts in 1 of the 2 classes.

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

      
     plotMA(object,pair=c(1,2),xlab="A",ylab="M",ylim=NULL,pch=19,eps=0,smearOffset=0,...)

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

  object: 'deDGEList' object, as output from 'deDGE'

    pair: pair of groups to be plotted; default plots groups 1 and 2

    xlab: x-axis label

    ylab: y-axis label

    ylim: limits on y-axis, if left at 'NULL', scaled to be symmetric
          about 0

     pch: plot character

     eps: offset to plot in the log-ratios (i.e. log( [p1+eps]/[p2+eps]
          )

smearOffset: offest (to the left of the minimum A value) to plot the
          smear of 0-in-1-group values

     ...: further arguments to the 'plot' command

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

     A plot to the current device

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

     Mark Robinson, Davis McCarthy

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

     'deDGE'

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

     # generate raw data from NB, create list object
     y<-matrix(rnbinom(20,size=1,mu=10),nrow=5)
     d<-DGEList(data=y,group=rep(1:2,each=2),lib.size=rep(c(1000:1001),2))

     # find alpha and call main procedure to find differences
     alpha<-alpha.approxeb(d)
     ms<-deDGE(d,alpha=alpha$alpha)

     # plot it
     plotMA(ms)

