classifyTests             package:limma             R Documentation

_M_u_l_t_i_p_l_e _T_e_s_t_i_n_g _G_e_n_e_w_i_s_e _A_c_r_o_s_s _C_o_n_t_r_a_s_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     For each gene, classify a series of related t-statistics as up,
     down or not significant.

_U_s_a_g_e:

     classifyTestsF(object, cor.matrix=NULL, df=Inf, p.value=0.01, fstat.only=FALSE)
     classifyTestsT(object, t1=4, t2=3)
     classifyTestsP(object, df=Inf, p.value=0.05, method="holm")
     FStat(object, cor.matrix=NULL)

_A_r_g_u_m_e_n_t_s:

  object: numeric matrix of t-statistics or an 'MArrayLM' object from
          which the t-statistics may be extracted.

cor.matrix: covariance matrix of each row of t-statistics.  Defaults to
          the identity matrix.

      df: numeric vector giving the degrees of freedom for the
          t-statistics. May have length 1 or length equal to the number
          of rows of 'tstat'.

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

fstat.only: logical, if 'TRUE' then return the overall F-statistic as
          for 'FStat' instead of classifying the test results

      t1: first critical value for absolute t-statistics

      t2: second critical value for absolute t-statistics

  method: character string specifying p-value adjustment method.  See
          'p.adjust' for possible values.

_D_e_t_a_i_l_s:

     Note that these functions do not adjust for multiple testing
     across genes. The adjustment for multiple testing is across the
     contrasts rather than the more usual control across genes. The
     functions described here are called by 'decideTests'. Most users
     should use 'decideTests' rather than using these functions
     directly.

     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.

     'FStat' computes the gene-wise F-statistics for testing all the
     contrasts equal to zero. It is equivalent to 'classifyTestsF' with
     'fstat.only=TRUE'.

     'classifyTestsF' uses a nested F-test approach giving particular
     attention to correctly classifying genes which have two or more
     significant t-statistics, i.e., are differential expressed under
     two or more conditions. For each row of 'tstat', the overall
     F-statistics is constructed from the t-statistics as for 'FStat'.
     At least one constrast will be classified as significant if and
     only if the overall F-statistic is significant. If the overall
     F-statistic is significant, then the function makes a best choice
     as to which t-statistics contributed to this result. The
     methodology is based on the principle that any t-statistic should
     be called significant if the F-test is still significant for that
     row when all the larger t-statistics are set to the same absolute
     size as the t-statistic in question.

     'classifyTestsT' and 'classifyTestsP' implement simpler
     classification schemes based on threshold or critical values for
     the individual t-statistics in the case of 'classifyTestsT' or
     p-values obtained from the t-statistics in the case of
     'classifyTestsP'. For 'classifyTestsT', classifies any t-statistic
     with absolute greater than 't2' as significant provided that at
     least one t-statistic for that gene is at least 't1' in absolute
     value. 'classifyTestsP' applied p-value adjustment from 'p.adjust'
     to the p-values for each gene.

     If 'tstat' is an 'MArrayLM' object, then all arguments except for
     'p.value' are extracted from it.

     'cor.matrix' is the same as the correlation matrix of the
     coefficients from which the t-statistics are calculated. If
     'cor.matrix' is not specified, then it is calculated from 'design'
     and 'contrasts' if at least 'design' is specified or else defaults
     to the identity matrix. In terms of 'design' and 'contrasts',
     'cor.matrix' is obtained by standardizing the matrix '
     t(contrasts) %*% solve(t(design) %*% design) %*% contrasts ' to a
     correlation matrix.

_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.

     'FStat' produces a numeric vector of F-statistics with attributes
     'df1' and 'df2' giving the corresponding degrees of freedom.

_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.

_E_x_a_m_p_l_e_s:

     tstat <- matrix(c(0,5,0, 0,2.5,0, -2,-2,2, 1,1,1), 4, 3, byrow=TRUE)
     classifyTestsF(tstat)

     # See also the examples for contrasts.fit and vennDiagram