doLPE                package:ABarray                R Documentation

_P_e_r_f_o_r_m _L_P_E _a_n_a_l_y_s_i_s

_D_e_s_c_r_i_p_t_i_o_n:

     The local pooled error test attempts to reduce dependence on the
     within-gene estimates in tests for differential expression, by
     pooling error estimates within regions of similar intensity. Note
     that with the large number of genes there will be genes with low
     within-gene error estimates by chance, so that some
     signal-to-noise ratios will be large regardless of mean expression
     intensities and fold-change. The local pooled error attempts to
     avert this by combining within-gene error estimates with those of
     genes with similar expression intensity.

_U_s_a_g_e:

     doLPE(eset, group, member, name = "", snThresh = 3, detectSample = 0.5)

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

    eset: an exprSet 'exprSet' object  

   group: which group should LPE be performed 

  member: optional. The member names in the group specified above 

    name: a prefix name for use when writing output to file 

snThresh: S/N ratio threshold to use to define gene detectability

detectSample: percetage of samples detectable above snThresh to include
          in LPE test. The default is 50%. If the probe is detected in
          50% or more samples in one of the subgroup, it is considered
          in LPE analysis 

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

     The LPE test statistic numerator is the difference in medians
     between the two experimental conditions. The test statistic
     denominator is the combined pooled standard error for the two
     experimental conditions obtained by looking up the var.M from each
     baseOlig.error variance function. The conversion to p-values is
     based on the Gaussian distribution for difference if order
     statistics (medians). The user may select bith the smoother
     degrees of freedom (smaller is smoother) and the trim percent to
     obtain a variance fucntion to suit particular issues i.e.
     variability of genes with low expression intensity.

_V_a_l_u_e:

     Dataframe

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

     Y Andrew Sun

_R_e_f_e_r_e_n_c_e_s:

     Bioconductor LPE package

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

     ##---- Some example usage ----

