metahdep              package:metahdep              R Documentation

_m_e_t_a_h_d_e_p

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

     Takes a list of 'metaprep' objects and performs the specified
     meta-analysis on each element.  Intended mainly for meta-analyzing
     the results of multiple gene expression studies.

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

     metahdep(prep.list, genelist = NULL, method = "HBLM", n = 10, m = 10, 
              center.X = FALSE, delta.split = FALSE, return.list = FALSE, 
              two.sided = TRUE)

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

prep.list: A list of 'metaprep' class objects as returned by the
          'metahdep.format()' function. 

genelist: (optional) A subsetting parameter.  A vector of gene/probeset
          names on which to perform the meta-analyses. 

  method: (optional) One of: "FEMA" - fixed effects meta-analysis,
          "REMA" - random effects meta-analysis, or "HBLM" -
          hierarchical Bayes linear model.  This defaults to "HBLM". 

       n: (optional) An even integer specifying the number of steps to
          take over each quartile in the numerical integration over tau
          when doing HBLM.  See 'metahdep.HBLM'. 

       m: (optional) An even integer specifying the number of steps to
          take in the numerical integration over varsigma (given tau)
          when doing HBLM.  See 'metahdep.HBLM'. 

center.X: (optional) A logical value specifying whether or not to
          center the columns of the covariate matrices.  If 'TRUE',
          then for the covariate matrix of each 'metaprep' object, the
          mean each non-intercept column will be subtracted from every
          element in the column prior to the meta-analysis.  This
          changes the interpretation of the intercept coefficient
          estimate from the model fit. 

delta.split: (optional) A logical value specifying whether or not to
          account for hierarchical dependence via delta-splitting. 
          Only used in methods "REMA" and "HBLM".  If 'TRUE', then each
          'metaprep' object must include a dependence matrix M. 

return.list: (optional) A logical value specifying whether to return
          the results as a list of lists rather than as a 'data.frame'.
          The default is 'FALSE'.

two.sided: (optional) A logical value specifying whether to transform
          the posterior probabilities from the HBLM method.  The
          default 'TRUE' returns 2-sided p-values for the parameter
          estimates for convenience in interpretation.  If this is set
          to FALSE, then it will return 1-sided posterior probabilities
          representing P( beta[j] > 0 | data ). 

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

     Returns a 'data.frame' by default.  The exact contents of the
     'data.frame' will vary depending on the 'method' argument.  The
     row names of the 'data.frame' will be the gene names from the
     'prep.list' argument.  For all 'method' options, the first several
     columns of the resulting 'data.frame' will be the model parameter
     estimates (beta hats).  The next group of columns will be the
     elements of the variance/covariance matrix for the beta hats.  The
     next group of columns will be the p-values for the parameter
     estimates.  The remaining columns will change depending on the
     method. 

     For FEMA (and REMA), the remaining columns are the Q statistic and
     its p-value - testing for model homogeneity.

     For HBLM, the remaining columns are the posterior mean and
     variance of tau, the posterior mean and variance of varsigma, and
     the posterior covariance of tau and varsigma.

     All columns in the 'data.frame' have meaningful names to aid their
     interpretation.

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

     John R. Stevens, Gabriel Nicholas

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

     Stevens J.R. and Doerge R.W., 2005, _A Bayesian and Covariate
     Approach to Combine Results from Multiple Microarray Studies_,
     Proceedings of Conference on Applied Statistics in Agriculture.

     Stevens J.R. and Taylor A.M. _Hierarchical Dependence in
     Meta-Analysis_, to appear, _Journal of Educational and Behavioral
     Statistics_.

     See also the _metahdep_ package vignette.

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

     data(HGU.prep.list)

     ##  do FEMA and REMA, and view the results
     FEMA.results <- metahdep(HGU.prep.list, method="FEMA", center.X=TRUE)
     head(FEMA.results)

     REMA.results <- metahdep(HGU.prep.list, method="REMA", center.X=TRUE)
     head(REMA.results)

     ##  get a small subset of genes
     ##  some of these may not be suitable for all methods 
     ##  (there may not be enough data for that gene)
     data(HGU.newnames)
     set.seed(123)
     gene.subset <- sample(HGU.newnames$new.name, 50)

     ##  view results from REMA and HBLM with delta splitting on subset of genes
     REMA.dsplit.results <- metahdep(HGU.prep.list, method="REMA", 
       genelist=gene.subset, delta.split=TRUE, center.X=TRUE)
     head(REMA.dsplit.results)

     HBLM.dsplit.results <- metahdep(HGU.prep.list, method="HBLM", 
       genelist=gene.subset, delta.split=TRUE, center.X=TRUE)
     head(HBLM.dsplit.results)

