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=NULL,number=10,genelist=fit$genes,adjust.method="BH",sort.by="B",resort.by=NULL)
     toptable(fit,coef=1,number=10,genelist=NULL,A=NULL,eb=NULL,adjust.method="BH",sort.by="B",resort.by=NULL,...)
     topTableF(fit,number=10,genelist=fit$genes,adjust.method="BH")

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

     fit: list containing a linear model fit produced by 'lmFit',
          'lm.series', 'gls.series' or 'mrlm'. For 'topTable', 'fit'
          should be an object of class 'MArrayLM' as produced by
          'lmFit' and 'eBayes'.

    coef: column number or column name specifying which coefficient or
          contrast of the linear model is of interest. Can also be a
          vector of column subscripts, in which case the gene ranking
          is by F-statistic for that set of contrasts.

  number: how many genes to pick out

genelist: data frame or character vector containing gene information.
          For 'topTable' only, this defaults to 'fit$genes'.

       A: matrix of A-values or vector of average A-values. For
          'topTable' only, this defaults to 'fit$Amean'.

      eb: output list from 'ebayes(fit)'. If 'NULL', this will be
          automatically generated.

adjust.method: method used to adjust the p-values for multiple testing.
           Options, in increasing conservatism, include '"none"',
          '"BH"', '"BY"' and '"holm"'. See 'p.adjust' for the complete
          list of options. A 'NULL' value will result in the default
          adjustment method, which is '"BH"'.

 sort.by: character string specifying statistic to rank genes by. 
          Possibilities are '"logFC"', '"A"', '"T"', '"t"', '"P"',
          '"p"' or '"B"'. '"M"' is allowed as a synonym for '"logFC"'
          for backward compatibility.

resort.by: character string specifying statistic to sort the selected
          genes by in the output data.frame.  Possibilities are
          '"logFC"', '"A"', '"T"', '"t"', '"P"', '"p"' or '"B"'. '"M"'
          is allowed as a synonym for '"logFC"' for backward
          compatibility.

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

_D_e_t_a_i_l_s:

     Note that 'toptable' is an earlier interface and is retained only
     for backward compatibility.

     This function summarizes a linear model fit object produced by
     'lmFit', 'lm.series', 'gls.series' or 'mrlm' by selecting the
     top-ranked genes for any given contrast. 'topTable()' assumes that
     the linear model fit has already been processed by 'eBayes()'.

     The p-values for the coefficient/contrast of interest are adjusted
     for multiple testing by a call to 'p.adjust'. The '"BH"' method,
     which controls the expected false discovery rate (FDR) below the
     specified value, is the default adjustment method because it is
     the most likely to be appropriate for microarray studies. Note
     that the adjusted p-values from this method are bounds on the FDR
     rather than p-values in the usual sense. Because they relate to
     FDRs rather than rejection probabilities, they are sometimes
     called q-values. See 'help("p.adjust")' for more information.

     Note, if there is no good evidence for differential expression in
     the experiment, that it is quite possible for all the adjusted
     p-values to be large, even for all of them to be equal to one. It
     is quite possible for all the adjusted p-values to be equal to one
     if the smallest p-value is no smaller than '1/ngenes' where
     'ngenes' is the number of genes with non-missing p-values.

     The 'sort.by' argument specifies the criterion used to select the
     top genes. The choices are: '"logFC"' to sort by the (absolute)
     coefficient representing the log-fold-change; '"A"' to sort by
     average expression level (over all arrays) in descending order;
     '"T"' or '"t"' for absolute t-statistic; '"P"' or '"p"' for
     p-values; or '"B"' for the 'lods' or B-statistic.

     Normally the genes appear in order of selection in the output
     table. If one wants the table to be in a different order, the
     'resort.by' argument may be used. For example, 'topTable(fit,
     sort.by="B", resort.by="logFC")' selects the top genes according
     to log-odds of differential expression and then orders the
     resulting genes by log-ratio in decreasing order. Or
     'topTable(fit, sort.by="logFC", resort.by="logFC")' would select
     the genes by absolute log-ratio and then sort then by signed
     log-ratio from must positive to most negative.

     'topTableF' ranks genes on the basis of the moderated F-statistic
     rather than t-statistics. If 'topTable' is called with 'coef' has
     length greater than 1, then the specified columns will be
     extracted from 'fit' and 'topTableF' called on the result.
     'topTable' with 'coef=NULL' is the same as 'topTableF', unless the
     fitted model 'fit' has only one column.

_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

   logFC: estimate of the log2-fold-change corresponding to the effect
          or contrast

 AveExpr: average log2-expression for the probe over all arrays and
          channels, same as 'Amean' in the 'MarrayLM' object

       t: moderated t-statistic

 P.Value: raw p-value

adj.P.Value: adjusted p-value or q-value

       B: log odds that the gene is differentially expressed

_N_o_t_e:

     This is not the right function to use to create summary statistics
     for all the probes on an array. Please consider using 'write.fit'
     or 'write' for this purpose, rather than using 'topTable' with
     'number=nrow(fit)'.

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

     Gordon Smyth

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

     An overview of linear model and testing functions is given in
     06.LinearModels. See also 'p.adjust' in the 'stats' package.

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

     #  See lmFit examples

