toptable                package:limma                R Documentation

_T_a_b_l_e _o_f _T_o_p _G_e_n_e_s _f_r_o_m _L_i_n_e_a_r _M_o_d_e_l _F_i_t

_D_e_s_c_r_i_p_t_i_o_n:

     Extract a table of the top-ranked genes from a linear model fit.

_U_s_a_g_e:

     toptable(fit,coef=1,number=10,genelist=NULL,A=NULL,eb=NULL,adjust.method="holm",sort.by="B",...)
     topTable(fit,coef=1,number=10,genelist=NULL,adjust.method="holm",sort.by="B")

_A_r_g_u_m_e_n_t_s:

     fit: for 'toptable', this is an output list from 'lm.series',
          'gls.series' or 'rlm.series'. For 'topTable' is an object of
          class 'MArrayLM'.

    coef: column number of the effect or contrast to rank the genes on

  number: how many genes to pick out

genelist: a data frame containing the gene allocation list or a vector
          containing the gene names

       A: matrix of A-values or vector of average A-values.

      eb: output list from 'ebayes(fit)'

adjust.method: method to use to adjust the P-values for multiple
          testing, e.g., "holm" or "fdr". See 'p.adjust' for the
          available options. If 'NULL' or '"none"' then the P-values
          are not adjusted.

 sort.by: statistic to rank genes by.  Possibilities are '"M"', '"A"',
          '"T"', '"P"' or '"B"'.

     ...: any other arguments are passed to 'ebayes' if 'eb' is 'NULL'

_D_e_t_a_i_l_s:

     This function summarizes a fit object produced by 'lm.series',
     'gls.series' or 'rlm.series' by selecting the top-ranked genes for
     any given contrast.

_V_a_l_u_e:

     A dataframe with a row for the 'number' top genes and the
     following columns: 

genelist: if genelist was included as input

       M: estimate of the effect or the contrast, on the log2 scale

       t: moderated t-statistic

 P.Value: nominal P-value

       B: log odds that the gene is differentially expressed

_A_u_t_h_o_r(_s):

     Gordon Smyth

_S_e_e _A_l_s_o:

     'ebayes', 'p.adjust', 'lm.series', 'gls.series', 'rlm.series'.

_E_x_a_m_p_l_e_s:

     #  Simulate gene expression data,
     #  6 microarrays and 100 genes with first gene differentially expressed
     M <- matrix(rnorm(100*6,sd=0.3),100,6)
     M[1,1:3] <- M[1,1:3] + 2
     #  Design matrix includes two treatments, one for first 3 and one for last 3 arrays
     design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
     fit <- lm.series(M,design=design)
     toptable(fit)

