generateExprVal.method.pdnn     package:affypdnn     R Documentation

_C_o_m_p_u_t_e _P_M _c_o_r_r_e_c_t_i_o_n _a_n_d _s_u_m_m_a_r_y _e_x_p_r_e_s_s_i_o_n _v_a_l_u_e

_D_e_s_c_r_i_p_t_i_o_n:

     Compute PM correction and summary expression value with PDNN
     method

_U_s_a_g_e:

     pmcorrect.pdnn(object, params, gene=NULL, gene.i=NULL,
                    params.chiptype=NULL, outlierlim=3, callingFromExpresso=FALSE)
     pmcorrect.pdnnpredict(object, params, gene=NULL, gene.i=NULL,
                    params.chiptype=NULL, outlierlim=3, callingFromExpresso=FALSE)
     generateExprVal.method.pdnn(probes, params)

_A_r_g_u_m_e_n_t_s:

  object: object of 'ProbeSet' 

  probes: matrix of PM-corrected signals (should be coming out of
          'pmcorrect.pdnn')

  params: experiments specific parameters

    gene: gene (probe set) ID (from wich the 'gene.i' would be derived)

  gene.i: gene index (see details)

params.chiptype: chip-specific parameters

outlierlim: threshold for tagging a probe as an outlier

callingFromExpresso: is the function called through expresso. DO NOT
          play with that.

_D_e_t_a_i_l_s:

     Only one of 'gene', 'gene.i' should be specified. For most the
     users, this is 'gene'. 'pmcorrect.pdnn' and
     'pmcorrect.pdnnpredict' return what is called GSB and GSB + NSB +
     B in the paper by Zhang Li and collaborators.

_V_a_l_u_e:

     'pmcorrect.pdnn' and 'pmcorrect.pdnnpredict' return a matrix (one
     row per probe, one column per chip) with attributes attached.
     'generateExprVal' returns a 'list': 

  exprs : expression values

se.exprs: se expr. val.

_S_e_e _A_l_s_o:

     'pdnn.params.chiptype'

_E_x_a_m_p_l_e_s:

     data(hgu95av2.pdnn.params)
     library(affydata)
     data(Dilution)

     ## only one CEL to go faster
     abatch <- Dilution[, 1]

     ## get the chip specific parameters
     params <- find.params.pdnn(abatch, hgu95av2.pdnn.params)

     ## The thrill part: do we get like in the Figure 1-a of the reference ?
     par(mfrow=c(2,2))
     ##ppset.name <- sample(geneNames(abatch), 2)
     ppset.name <- c("41206_r_at", "31620_at")
     ppset <- probeset(abatch, ppset.name)
     for (i in 1:2) {
     ##ppset[[i]] <- transform(ppset[[i]], fun=log) # take the log as they do
     probes.pdnn <- pmcorrect.pdnnpredict(ppset[[i]], params,
                                          params.chiptype=hgu95av2.pdnn.params)
     ##probes.pdnn <- log(probes.pdnn)
     plot(ppset[[i]], main=paste(ppset.name[i], "\n(raw intensities)"))
     matplotProbesPDNN(probes.pdnn, main=paste(ppset.name[i], "\n(predicted intensities)"))
     }

     ## pick the 50 first probeset IDs
     ## (to go faster)
     ids <- geneNames(abatch)[1:100]

     ## compute the expression set (object of class 'exprSet')
     eset <- computeExprSet(abatch, pmcorrect.method="pdnn",
                            summary.method="pdnn", ids=ids,
                            summary.param = list(params, params.chiptype=hgu95av2.pdnn.params))

