generateExprVal             package:affy             R Documentation

_C_o_m_p_u_t_e _a _s_u_m_m_a_r_y _e_x_p_r_e_s_s_i_o_n _v_a_l_u_e _f_r_o_m _t_h_e _p_r_o_b_e_s _i_n_t_e_n_s_i_t_i_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Compute a summary expression value from the probes intensities

_U_s_a_g_e:

     generateExprVal.methods # vector of names of methods

     express.summary.stat(x, pmcorrect, summary, ...)
     express.summary.stat.methods # vector of names of methods

_A_r_g_u_m_e_n_t_s:

       x: a ('ProbeSet'

pmcorrect: the method used to correct the PM values before summarizing
          to an expression value.

 summary: the method used to generate the expression value.

     ...: other parameters the method might need... (see the
          corresponding methods below...)

_V_a_l_u_e:

     Returns a vector of expression values.

_E_x_a_m_p_l_e_s:

     data(affybatch.example)

     p <- probeset(affybatch.example, "A28102_at")[[1]]

     par(mfcol=c(5,2))
     mymethods <- express.summary.stat.methods
     nmet <- length(mymethods)
     nc <- ncol(pm(p))

     layout(matrix(c(1:nc, rep(nc+1, nc)), nc, 2), width = c(1, 1))

     barplot(p)

     results <- matrix(0, nc, nmet)
     rownames(results) <- paste("sample", 1:nc)
     colnames(results) <- mymethods

     for (i in 1:nmet) {
       ev <- express.summary.stat(p, summary=mymethods[i], pmcorrect="pmonly")
       if (mymethods[[i]] != "medianpolish")
         results[, i] <- 2^(ev$exprs)
       else
         results[, i] <- ev$exprs
     }

     dotchart(results, labels=paste("sample", 1:nc))