topTags                package:edgeR                R Documentation

_D_i_s_p_l_a_y_s _t_h_e _t_o_p _d_i_f_f_e_r_e_n_t_i_a_l_l_y _e_x_p_r_e_s_s_e_d _t_a_g_s _i_n _a _t_a_b_l_e

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

     Displays/Returns the top DE tags in a data frame

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

      
     topTags(object,pair,n=10,adj.method= "BH", verbose=TRUE)

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

  object: 'deDGEList', output from 'deDGE'

    pair: pair of groups to be compared

       n: number of tags to display/return

adj.method: method used to adjust P-values, using 'p.adjust'

 verbose: whether to write comments, default 'TRUE'

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

     Data frame containing the relative level of expression, log fold
     changes, unadjusted and adjusted P-values

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

     Mark Robinson, Davis McCarthy

_R_e_f_e_r_e_n_c_e_s:

     Robinson MD, Smyth GK. 'Small-sample estimation of negative
     binomial dispersion, with applications to SAGE data.'
     Biostatistics. 2008 Apr;9(2):321-32.

     Robinson MD, Smyth GK. 'Moderated statistical tests for assessing
     differences in tag abundance.' Bioinformatics. 2007 Nov
     1;23(21):2881-7.

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

     # generate raw data from NB, create list object
     y<-matrix(rnbinom(80,size=1,mu=10),nrow=20)
     d<-DGEList(data=y,group=rep(1:2,each=2),lib.size=rep(c(1000:1001),2))
     rownames(d$data)<-paste("tagno",1:nrow(d$data),sep=".")

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

     # look at top 10
     topTags(ms)

