decideTests              package:limma              R Documentation

_M_u_l_t_i_p_l_e _T_e_s_t_i_n_g _A_c_r_o_s_s _G_e_n_e_s _a_n_d _C_o_n_t_r_a_s_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Classify a series of related t-statistics as up, down or not
     significant. A number of different multiple testing schemes are
     offered which adjust for multiple testing down the genes as well
     as across contrasts for each gene.

_U_s_a_g_e:

     decideTests(object,method="separate",adjust.method="BH",p.value=0.05,lfc=0)

_A_r_g_u_m_e_n_t_s:

  object: 'MArrayLM' object output from 'eBayes' from which the
          t-statistics may be extracted.

  method: character string specify how probes and contrasts are to be
          combined in the multiple testing strategy.  Choices are
          '"separate"', '"global"', '"hierarchical"', '"nestedF"' or
          any partial string.

adjust.method: character string specifying p-value adjustment method. 
          Possible values are '"none"', '"BH"', '"fdr"' (equivalent to
          '"BH"'), '"BY"' and '"holm"'. See 'p.adjust' for details.

 p.value: numeric value between 0 and 1 giving the desired size of the
          test

     lfc: minimum log2-fold-change required

_D_e_t_a_i_l_s:

     These functions implement multiple testing procedures for
     determining whether each statistic in a matrix of t-statistics
     should be considered significantly different from zero. Rows of
     'tstat' correspond to genes and columns to coefficients or
     contrasts.

     The setting 'method="separate"' is equivalent to using 'topTable'
     separately for each coefficient in the linear model fit, and will
     give the same lists of probes if 'adjust.method' is the same.
     'method="global"' will treat the entire matrix of t-statistics as
     a single vector of unrelated tests. 'method="hierarchical"'
     adjusts down genes and then across contrasts. 'method="nestedF"'
     adjusts down genes and then uses 'classifyTestsF' to classify
     contrasts as significant or not for the selected genes. Please see
     the limma User's Guide for a discussion of the statistical
     properties of these methods.

_V_a_l_u_e:

     An object of class 'TestResults'. This is essentially a numeric
     matrix with elements '-1', '0' or '1' depending on whether each
     t-statistic is classified as significantly negative, not
     significant or significantly positive respectively.

     If 'lfc>0' then contrasts are judged significant only when the
     log2-fold change is at least this large in absolute value. For
     example, one might choose 'lfc=log2(1.5)' to restrict to 50%
     changes or 'lfc=1' for 2-fold changes. In this case, contrasts
     must satisfy both the p-value and the fold-change cutoff to be
     judged significant.

_A_u_t_h_o_r(_s):

     Gordon Smyth

_S_e_e _A_l_s_o:

     An overview of multiple testing functions is given in 08.Tests.