esApply               package:Biobase               R Documentation

_A_p_p_l_y _f_o_r _t_h_e _E_x_p_r_e_s_s_i_o_n _D_a_t_a _i_n '_e_x_p_r_S_e_t'

_D_e_s_c_r_i_p_t_i_o_n:

     'esApply' is a wrapper to apply for use with 'exprSet's and
     'eSet's. Because the application of a function to the rows of the
     expression array usually involves variables in the 'phenoData'
     slot we have used a special evaluation paradigm here. The function
     'FUN' may reference any data in phenoData by name.

     Consider defining the function as a method for 'exprSet', 'eSet'

_U_s_a_g_e:

     esApply(X, MARGIN, FUN, ...)

_A_r_g_u_m_e_n_t_s:

       X: An instance of class 'exprSet'. It is assumed that 'X' has
          information on gene expression for G genes in N tissue
          samples. 

  MARGIN: The margin to apply to, either 1 for rows or 2 for columns.

     FUN: Any function 

     ...: Additional parameters for 'FUN'

_D_e_t_a_i_l_s:

     The 'phenoData' from 'X' is installed in an environment. This
     environment is installed as the environment of 'FUN'. This will
     then provide bindings for any symbols in 'FUN' that are the same
     as the names of the 'phenoData' of 'X'. If 'FUN' has an
     environment already it is retained but placed after the newly
     created environment. Some variable shadowing could occur under
     these circumstances.

_V_a_l_u_e:

     The result of 'apply(exprs(X),MARGIN, FUN, ...)'.

_A_u_t_h_o_r(_s):

     V.J. Carey <stvjc@channing.harvard.edu>, R. Gentleman

_S_e_e _A_l_s_o:

     'apply', 'exprSet'

_E_x_a_m_p_l_e_s:

     data(sample.exprSet.1)

     # we know that eset has covariates in the pData called "cov1" and "cov2"
     # here cov1 is an unbound value, it will be resolved by using the pData
     # here are two functions conforming to the esApply protocol

     mytt.demo <- function(y) {
      ys <- split( y, cov1 )
      t.test( ys[[1]], ys[[2]] )$p.value
      }

     # obtain the p value of the slope associated with cov2, adjusting for cov1
     # (if we were concerned with sign we could save the z statistic instead at coef[3,3]
     myreg.demo <- function( y ) {
        summary(lm(y~cov1+cov2))$coef[3,4]
     }

     newt <- esApply( sample.exprSet.1, 1, mytt.demo )

     # a resampling method
     resamp <- function( ESET ) {
      ntiss <- ncol(exprs(ESET))
      newind <- sample(1:ntiss, size=ntiss, replace=TRUE)
      ESET[newind,]
      }

     # a filter
     q3g100filt <- function( eset ) {
      apply( exprs(eset), 1, function(x)quantile(x,.75)>100 )
      }

     # filter after resampling and then apply
     set.seed(123)
     rest <- esApply( { bool <- q3g100filt(resamp(sample.exprSet.1)); sample.exprSet.1[bool,] }, 1,
     mytt.demo )