romer                 package:limma                 R Documentation

_R_o_t_a_t_i_o_n _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:

     Rotation-mean50-rank version of GSEA (gene set enrichment
     analysis) for linear models.

_U_s_a_g_e:

     romer(iset=NULL,y,design,contrast=ncol(design),array.weights=NULL,block=NULL,correlation,nrot=10000)

_A_r_g_u_m_e_n_t_s:

    iset: list of indices specifying the rows of 'y' in the gene sets.
          The list can be made by symbols2indices and the gene sets can
          be retrieved from the molecular signatures database in Broad
          Institute.

       y: numeric matrix giving log-expression values.

  design: design matrix

contrast: contrast for which the test is required. Can be an integer
          specifying a column of 'design', or else a contrast vector of
          length equal to the number of columns of 'design'.

array.weights: optional numeric vector of array weights.

   block: optional vector of blocks.

correlation: correlation between blocks.

    nrot: number of rotations used to estimate the p-values.

_D_e_t_a_i_l_s:

     This function implements a GSEA of a battery of gene sets similar
     in motivation to Subramanian et al (2005) but designed for use
     with linear models. In is a competitive test, in that the
     different gene sets are pitted against one another. The genes are
     ranked by moderated t-statistic, then the mean rank of the top 50
     Instead of permuation, it uses rotation, which is a smoothed
     version of permutation suitable for linear models (Langsrud,
     2005). 'romer' can be used with any linear model with some level
     of replication.

     Number of genes is given for each gene set.

     p-values are given for four possible alternative hypotheses. The
     alternative "up" means the genes in the set tend to be
     up-regulated, with positive t-statistics. The alternative "down"
     means the genes in the set tend to be down-regulated, with
     negative t-statistics. The alternative "either" means the set is
     either up or down-regulated as a whole. The alternative "mixed"
     test whether the genes in the set tend to be differentially
     expressed, without regard for direction. In this case, the test
     will be significant if the set contains mostly large test
     statistics, even if some are positive and some are negative.

     The first three alternatives are appropriate if you have a prior
     expection that all the genes in the set will react in the same
     direction. The "mixed" alternative is appropriate if you know only
     that the genes are involved in the relevant pathways, without
     knowing the direction of effect for each gene. The "mixed"
     alternative is the only one possible with F-like statistics.

     Note that 'romer' estimates p-values by simulation, specifically
     by random rotations of the orthogonalized residuals. This means
     that the p-values will vary slightly from run to run. To get more
     precise p-values, increase the number of rotations 'nrot'. The
     strategy of random rotations is due to Langsrud (2005).

_V_a_l_u_e:

     Matrix with the rows corresponding to estimated p-values for each
     gene set and the columns corresponding to the number of genes for
     each gene set and the alternative hypotheses mixed, up, down or
     either.

_A_u_t_h_o_r(_s):

     Yifang Hu and Gordon Smyth

_R_e_f_e_r_e_n_c_e_s:

     Langsrud, O, 2005. Rotation tests. _Statistics and Computing_ 15,
     53-60

     Subramanian, A, Tamayo, P, Mootha, VK, Mukherjee, S, Ebert, BL,
     Gillette, MA, Paulovich, A, Pomeroy, SL, Golub, TR, Lander, ES and
     Mesirov JP, 2005. Gene set enrichment analysis: a knowledge-based
     approach for interpreting genome-wide expression profiles. _Proc
     Natl Acad Sci U S A_ 102, 15545-15550

_S_e_e _A_l_s_o:

     'roast', 'geneSetTest'

_E_x_a_m_p_l_e_s:

     y <- matrix(rnorm(100*4),100,4)
     design <- cbind(Intercept=1,Group=c(0,0,1,1))
     iset <- 1:5
     y[iset,3:4] <- y[iset,3:4]+3

     iset1 <- 1:5
     iset2 <- 6:10
     romer(iset=list(iset1=iset1,iset2=iset2),y=y,design=design,contrast=2)