gsealmPerm              package:GSEAlm              R Documentation

_N_o_n_p_a_r_a_m_e_t_r_i_c _i_n_f_e_r_e_n_c_e _f_o_r _l_i_n_e_a_r _m_o_d_e_l_s _i_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 (_G_S_E_A)

_D_e_s_c_r_i_p_t_i_o_n:

     Provides permutation-based p-values for a main effect at the
     gene-set level, potentially adjusting for the effect of other
     variables via a linear model. This is a generalization and upgrade
     of 'gseattperm'.

_U_s_a_g_e:

     gsealmPerm(eSet, formula = "", mat, nperm, na.rm = TRUE,pooled=FALSE,...)

_A_r_g_u_m_e_n_t_s:

    eSet: An 'ExpressionSet' object.

 formula: An object of class 'formula' (or one that can be coerced to
          that class), specifying only the right-hand side starting
          with the '~' symbol. The LHS is automatically set as the
          expression levels provided in  'eSet'. The names of all
          predictors must exist in the phenotypic data of 'eSet'. See
          more below in "Details".

     mat: A 0/1 incidence matrix with each row representing a gene set
          and each column representing a gene.  A 1 indicates
          membership of a gene in a gene set. 

   nperm: Number of permutations used to simulate the reference null
          distribution.

   na.rm: Should missing observations be ignored? (passed on to
          'lmPerGene')  

  pooled: Should variance be pooled across all genes?  (passed on to
          'lmPerGene')

     ...: Additional parameters passed on to 'GSNormalize'.

_D_e_t_a_i_l_s:

     If a formula is provided, the permutation test permutes sample
     (i.e. column) labels, so essentially the effect is compared with
     the null distribution of effects for *each particular gene-set
     separately*. This neutralizes the impact of intra-sample
     correlations. If the formula contains two or more covariates, the
     effect of interest must be the first one in the formula. This
     effect's covariate values are permuted within each subgroup
     defined by identical values on all other covariates. This means,
     that the other covariates *must* be discrete, otherwise the
     analysis is meaningless. The effect of interest is the only one
     that can be continuous.

     If a formula is *not* provided, a row-permutation test is
     performed on average expression levels. This test examines whether
     each gene-set is differentially expressed (on the average),
     compared with a permutation baseline of random gene-sets of the
     same size.

_V_a_l_u_e:

     A matrix with the same number of rows as 'mat' and two columns,
     "Lower" and "Upper".  The "Lower" ("Upper") column gives the
     probability of seeing a t-statistic smaller (larger) than the
     observed. If 'mat' had row names, so will the output.

_W_a_r_n_i_n_g_s:

     1. Inference is *only* for the first term in the model. If you
     want inference for more terms, re-run the function on the same
     model, changing order of terms each time.

     2. To repeat: the adjusting covariates (all terms except the
     first) have to be discrete. Adding a continuous covariate with
     unique values for most samples, may result in an infinite loop.
     However, you *can* put a continuous covariate as your first term.

_N_o_t_e:

     This function is a generic template for GSEA permutation tests.
     The particular type of GSEA statistic used is determined by
     'GSNormalize', which is called by this function. Permutations are
     generated via repeated calls to 'lmPerGene'.

_A_u_t_h_o_r(_s):

     Assaf Oron

_S_e_e _A_l_s_o:

     'gseattperm','GSNormalize', 'lmPerGene'. The 'GlobalAncova'
     package provides a generic $F$-test for model selection, while
     'gsealmPerm' can be used as a Wald test for the addition of a
     single covariate to the model.

_E_x_a_m_p_l_e_s:

     data(sample.ExpressionSet)

     ### Generating random pseudo-gene-sets
     fauxGS=matrix(sample(c(0,1),size=50000,replace=TRUE,prob=c(.9,.1)),nrow=100)

     ### inference for sex: sex is first term
     sexPvals=gsealmPerm(sample.ExpressionSet,~sex+type,mat=fauxGS,nperm=40)

     ### inference for type: type is first term
     typePvals=gsealmPerm(sample.ExpressionSet,~type+sex,mat=fauxGS,nperm=40,removeShift=TRUE)

     ### plotting the p-values; note that the effect direction depends upon
     ### factor level order (defaults to alphabetical)
     layout(t(1:2))
     ### Sex p-values are center-heavy, typical when the effect is dominated
     ### by another effect
     hist(sexPvals[,2],10,main="Sex Effect p-values",xlab="p-values for Male minus Female",xlim=c(0,1))
     ### The dominating effect is type, where there is a baseline shift in
     ### favor of controls
     hist(typePvals[,1],10,main="Type Effect p-values",xlab="p-values for Case minus Control",xlim=c(0,1))

     ############
     ### Modeling type again - and now we add a baseline-shift removal (the 'removeShift' argument passed on to 'GSNormalize')
     typePvals1=gsealmPerm(sample.ExpressionSet,~type+sex,mat=fauxGS,nperm=40,removeShift=TRUE)
     ### Modeling type again - and now the shift removal is by mean instead
     ### of the default median
     typePvals2=gsealmPerm(sample.ExpressionSet,~type+sex,mat=fauxGS,nperm=40,removeShift=TRUE,removeStat=mean)

     ### Now notice the differences between the 3 versions! This is a weird
     ### dataset indeed; it's also important to undrestand which research
     ### question you are trying to answer :)
     hist(typePvals1[,1],10,main="Type Effect p-values",xlab="p-values for Case minus Control",xlim=c(0,1))
     hist(typePvals2[,1],10,main="Type Effect p-values",xlab="p-values for Case minus Control",xlim=c(0,1))

