preprocess               package:Ringo               R Documentation

_P_r_e_p_r_o_c_e_s_s _N_i_m_b_l_e_g_e_n _R_a_w _I_n_t_e_n_s_i_t_i_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Calls one of various 'limma' functions to transform raw probe
     intensities into (background-corrected) normalized log ratios
     (M-values).

_U_s_a_g_e:

     preprocess(myRG, method = "vsn", returnMAList=FALSE,
                idColumn="PROBE_ID", verbose=TRUE, ...)

_A_r_g_u_m_e_n_t_s:

    myRG: object of class 'RGList'

  method: string; denoting which normalization method to choose, see
          below for details

returnMAList: logical; should an MAList object be returned? Default is
          to return an ExpressionSet object.

idColumn: string; indicating which column of the 'genes' data.frame of
          the RGList holds the identifier for reporters on the
          microarray. This column, after calling 'make.names' on it,
          will make up the unique 'featureNames' of the resulting
          'ExpressionSet'. If argument 'returnMAList' is 'TRUE', this
          argument is ignored.

 verbose: logical; progress output to STDOUT?

     ...: further arguments to be passed on 'normalizeWithinArrays' and
          'normalizeBetweenArrays'

_D_e_t_a_i_l_s:

     The procedure and called 'limma' functions depend on the choice of
     method.

     _l_o_e_s_s First performs background correction of intensity values
          using 'limma''s function 'backgroundCorrect' with
          'method="normexp"' and 'offset=50'. Then calls
          'normalizeWithinArrays'.

     _v_s_n Does not do any background correction, but rather directly
          calls 'normalizeBetweenArrays' with 'method="vsn"'.

     _G_q_u_a_n_t_i_l_e Does not do any background correction, but rather
          directly calls 'normalizeBetweenArrays' with
          'method="Gquantile"'.

     _R_q_u_a_n_t_i_l_e Does not do any background correction, but rather
          directly calls 'normalizeBetweenArrays' with
          'method="Rquantile"'.

     _m_e_d_i_a_n First performs background correction of intensity values
          using 'limma''s function 'backgroundCorrect' with
          'method="normexp"' and 'offset=50'. Then calls
          'normalizeWithinArrays' with 'method="median".'

     _n_i_m_b_l_e_g_e_n Scaling procedure used by Nimblegen. Yields scaled
          log-ratios by a two step procedure: srat = log2(R) - log2(G)
          srat = srat - tukey.biweight(srat)

     _n_o_n_e No normalization of probe intensities, takes raw
          'log2(R)-log2(G)' as component 'M' and '(log2(R)+log2(G))/2'
          as component 'A'; uses 'normalizeWithinArrays' with
          'method="none"'.

_V_a_l_u_e:

     Returns normalized, transformed values as an object of class
     'ExpressionList' or 'MAList'.

_A_u_t_h_o_r(_s):

     Joern Toedling toedling@ebi.ac.uk

_S_e_e _A_l_s_o:

     'backgroundCorrect', 'normalizeWithinArrays',
     'normalizeBetweenArrays', 'malist','ExpressionSet',
     'tukey.biweight'

_E_x_a_m_p_l_e_s:

        exDir <- system.file("exData",package="Ringo")
        exRG <- readNimblegen("example_targets.txt","spottypes.txt",path=exDir)
        exampleX <- preprocess(exRG)
        sampleNames(exampleX) <- 
          make.names(paste(exRG$targets$Cy5,"vs",exRG$targets$Cy3,sep="_"))
        print(exampleX)

        ### compare VSN to NimbleGen's tukey-biweight scaling
        exampleX.NG <- preprocess(exRG, method="nimblegen")
        sampleNames(exampleX.NG) <- sampleNames(exampleX)
        if (interactive())
          corPlot(cbind(exprs(exampleX),exprs(exampleX.NG)),
            grouping=c("VSN normalized","Tukey-biweight scaled"))

