PGSEA                 package:PGSEA                 R Documentation

_P_a_r_a_m_e_t_r_i_c _G_e_n_e _S_e_t _E_n_r_i_c_h_m_e_n_t _A_n_a_l_y_s_i_s

_D_e_s_c_r_i_p_t_i_o_n:

     This package contains functions for parametric analysis of gene
     expression data. This type of analysis can assist in determining
     of lists of genes, such as those deregulated in defined
     experimental systems, are similarly disregulated in other data
     sets. 

     This function subsets the data based on lists of genes, computes a
     summary statistic for each gene list, and returns the results in a
     convenient form.

_U_s_a_g_e:

     PGSEA(exprs, cl, range = c(25, 500), ref = NULL, center = TRUE, p.value = 0.005, weighted = TRUE, ...)

_A_r_g_u_m_e_n_t_s:

   exprs: matrix expression data, a numeric matrix, eSet, or exprSet 

      cl: List of class "SMC" objects - concept list 

   range: a 2 element vector describing the min and max length of
          concepts to analyze  

     ref: a vector containing the index of reference samples from which
          to make comparisons. Defaults to NULL (internally referenced
          samples)

  center: boolean - median center gene expression matrix columns prior
          to analysis. Can be helpful if 'ref' is used 

 p.value: numeric p.value threshold or NA to return all data or TRUE to
          return a matrix of p.values 

weighted: boolean - weight results by the size of each gene list 

     ...: extra arguments passed along to FUN 

_D_e_t_a_i_l_s:

     Gene expression values are separated into subsets based on the
     lists of genes contained in the 'smc' (Simple Molecular Concept)
     object list. For example, 'readGmt' can be used to produce a 'smc'
     object list from a simple tab-delimited text file. The gene
     expression values from each of these gene lists is extracted and a
     summary statistic is computed for each region.

     The expression data must have the same identifiers as the list of
     genes being tested. If they are not, the expression data can be
     converted using the 'aggregateExprs' function, that can use a
     current annotation environment to convert and condense the gene
     expression data.

     By default the 't.test' fuction is applied to the gene set,
     however the weighted argument uses the method set out by Kim and
     Volsky. <URL: http://www.biomedcentral.com/1471-2105/6/144>

     The function is set up to perform the analysis on individual
     samples. For convenient method to analyze groups of samples, see
     the "Limma User's Guide" for more information on how to see up a
     contrast matrix and perform a linear model fit.  The coefficents
     of the fit can then be used a input into the 'PGSEA' function.

_V_a_l_u_e:

     If p.value is set to a number, a matrix of results that pass at
     that significance is returned, of size <number of samples> x
     <number of molecular concepts>.

     If p.value is set to NA, all results are returned.

     If p.value is set to TRUE, then a list is returned that consists
     of the PGSEA results as well as their p.values.

_N_o_t_e:

     <URL: http://www.biomedcentral.com/1471-2105/6/144>

_A_u_t_h_o_r(_s):

     Kim SY, Volsky DJ., kyle.furge@vai.org and karl.dykema@vai.org

_R_e_f_e_r_e_n_c_e_s:

     PGSEA: Parametric Analysis of Gene Set Enrichment

_E_x_a_m_p_l_e_s:

             datadir <- system.file("data", package = "PGSEA")
             sample <- readGmt(file.path(datadir, "sample.gmt"))
             data(nbEset)
             pg <- PGSEA(nbEset@exprs,cl=sample,ref=1:5)
             
             print(pg[,-c(1:5)])

