gapFilter             package:genefilter             R Documentation

_A _f_i_l_t_e_r _t_o _s_e_l_e_c_t _g_e_n_e_s _b_a_s_e_d _o_n _t_h_e_r_e _b_e_i_n_g _a _g_a_p.

_D_e_s_c_r_i_p_t_i_o_n:

     The 'gapFilter' looks for genes that might usefully discriminate
     between two groups (possibly unknown at the time of filtering). To
     do this we look for a gap in the ordered expression values. The
     gap must come in the central portion (we exclude jumps in the
     initial 'Prop' values or the final 'Prop' values). Alternatively,
     if the IQR for the gene is large that will also pass our test and
     the gene will be selected.

_U_s_a_g_e:

     gapFilter(Gap, IQR, Prop, na.rm=TRUE, neg.rm=TRUE)

_A_r_g_u_m_e_n_t_s:

     Gap: The size of the gap required to pass the test. 

     IQR: The size of the IQR required to pass the test. 

    Prop: The proportion (or number) of samples to exclude at either
          end.

   na.rm: If 'TRUE' then 'NA''s will be removed before processing. 

  neg.rm: If 'TRUE' then negative values in 'x' will be removed before
          processing.

_D_e_t_a_i_l_s:

     As stated above we are interested in

_V_a_l_u_e:

     A function that returns either 'TRUE' or 'FALSE' depending on
     whether the vector supplied has a gap larger than 'Gap' or an IQR
     (inter quartile range) larger than 'IQR'. For computing the gap we
     want to exclude a proportion, 'Prop' from either end of the sorted
     values. The reason for this requirement is that genes which differ
     in expression levels only for a few samples are not likely to be
     interesting.

_A_u_t_h_o_r(_s):

     R. Gentleman

_S_e_e _A_l_s_o:

     'ttest', 'genefilter'

_E_x_a_m_p_l_e_s:

      set.seed(256)
      x <- c(rnorm(10,100,3), rnorm(10, 100, 10))
      y <- x + c(rep(0,10), rep(100,10))
      tmp <- rbind(x,y) 
      Gfilter <- gapFilter(200, 100, 5)
      ffun <- filterfun(Gfilter)
      genefilter(tmp, ffun)