mulTestCor             package:Envisage             R Documentation

_M_u_l_t_i_p_l_e _t_e_s_t_i_n_g _c_o_r_r_e_c_t_i_o_n_s _f_o_r _p_a_c_k_a_g_e _E_n_v_i_s_a_g_e

_D_e_s_c_r_i_p_t_i_o_n:

     This function corrects the p-values obtained from package
     'Envisage' to account for problems with multiple testing. These
     corrected p-values give a measure of the significance of model
     terms on the effects on gene expression changes for individual
     genes.

_U_s_a_g_e:

       mulTestCor(pValues, MTC, pData, exprData, param4INT) 

_A_r_g_u_m_e_n_t_s:

 pValues: An object of class 'data.frame' containing the p-values from
          the Type II sum of squares analysis for each model term for
          each gene.

     MTC: A character string that defines which multiple testing 
          correction method to use (if any).  Available methods are
          'None', 'BH', 'BY', 'Bonferroni', 'Holm', 'Hochberg',
          'SidakSS', 'SidakSD'. More information on these can be found
          in the package vignette. 'BH' is selected by default.

   pData: An object of class 'phenoData' containing the phenotypic
          information for the experiment.

exprData: A matrix of expression values for each gene for each sample.
          Usually calculated using 'exprs' on an object of class
          'ExpressionSet'.

param4INT: Character vector of variables for which interaction terms
          should be calculated.

_D_e_t_a_i_l_s:

     The 'Envisage' package contains methods allowing the use of 
     linear models (LMs) for analysing significantly changing genes in 
     experiments with a variety of sources of variation, be they
     experimentally controlled variables such as drug treatment or
     time, or non-controlled sources of confounding variation such as
     phenotypic or environmental differences. This allows all sources
     of variation to be considered when analysing for significant
     differential expression, ensuring resulting genes are biologically
     relevent to the experimental question. 

     Due to the large number of genes analysed, gene expression
     analyses can suffer from problems due to multiple hypothesis
     testing. The function 'mulTestCor' performs the selected multiple
     testing correction on the p-values indicating significance of each
     model term for each gene in the analysis. 

     'mulTestCor' is an internal function and is not designed to be
     accessed by users.

_V_a_l_u_e:

     An object of class 'data.frame' is returned containing the
     adjusted p-values for each model term for each gene.

_A_u_t_h_o_r(_s):

     Sam Robson S.C.Robson@warwick.ac.uk

_R_e_f_e_r_e_n_c_e_s:

     Robson, S. C., Hunter, E., Bird, H., Turner, H. (2008)  _Envisage:
     model-based significance analysis of microarray gene expression
     data_, manuscript in preparation

_S_e_e _A_l_s_o:

     For the main 'Envisage' method, see 'envisage'.

