calculate.GSEA          package:sigPathway          R Documentation

_C_a_l_c_u_l_a_t_e _2-_s_i_d_e_d _s_t_a_t_i_s_t_i_c_s _b_a_s_e_d _o_n _t_h_e _G_S_E_A _a_l_g_o_r_i_t_h_m

_D_e_s_c_r_i_p_t_i_o_n:

     Calculates the 2-sided statistics based on the GSEA algorithm.

_U_s_a_g_e:

     calculate.GSEA(tab, phenotype, gsList, nsim = 1000,
                    verbose = FALSE, alwaysUseRandomPerm = FALSE)

_A_r_g_u_m_e_n_t_s:

     tab: a numeric matrix of expression values, with the rows and
          columns representing probe sets and sample arrays,
          respectively

phenotype: a numeric or character vector indicating the phenotype

  gsList: a list containing three vectors from the output of the
          'selectGeneSets' function

    nsim: an integer indicating the number of permutations to use

 verbose: a boolean to indicate whether to print debugging messages to
          the R console

alwaysUseRandomPerm: a boolean to indicate whether the algorithm can
          use complete permutations for cases where 'nsim' is greater
          than the total number of unique permutations possible with
          the 'phenotype' vector

_D_e_t_a_i_l_s:

     This function assumes 2 distinct types of phenotypes in the data.
     It calculates a variant of the GSEA statistics (Mootha et al.)
     with the following modifications: (a) GSEA was changed from a
     1-sided to a 2-sided approach. (b) The 2-group t-statistics is
     used as the difference metric.

     The function also normalizes the GSEA statistic and calculates the
     corresponding q-values for each gene set as described in Tian et
     al. (2005)  The function's output can be used for further analysis
     in other functions such as 'rankPathways.NGSk' or
     'getPathwayStatistics.NGSk'.

_V_a_l_u_e:

     A list containing 

     ngs: number of gene sets

    nsim: number of permutations performed

   t.set: a numeric vector of Tk statistics

t.set.new: a numeric vector of NTk statistics

  p.null: the proportion of nulls

 p.value: a numeric vector of p-values

 q.value: a numeric vector of q-values

_A_u_t_h_o_r(_s):

     Lu Tian, Peter Park, and Weil Lai

_R_e_f_e_r_e_n_c_e_s:

     Mootha V.K., Lindgren C.M., Eriksson K.F., Subramanian A., Sihag
     S., Lehar J., Puigserver P., Carlsson E., Ridderstrale M., Laurila
     E., Houstis N., Daily M.J., Patterson N., Mesirov J.P., Golud
     T.R., Tamayo P., Spiegelman B., Lander E.S., Hirshhorn J.N.,
     Altshuler D., Groop L.C.  (2003)  PGC-1alpha-responsive genes
     involved in oxidative phosphorylation are coordinately
     downregulated in human diabetes. _Nature Genetics_, *34*, 267-73.

     Tian L., Greenberg S.A., Kong S.W., Altschuler J., Kohane I.S.,
     Park P.J. (2005)  Discovering statistically significant pathways
     in expression profiling studies.  _Proceedings of the National
     Academy of Sciences of the USA_, *102*, 13544-9.

     <URL: http://www.pnas.org/cgi/doi/10.1073/pnas.0506577102>

