rma                   package:xps                   R Documentation

_R_o_b_u_s_t _M_u_l_t_i-_A_r_r_a_y _A_v_e_r_a_g_e _E_x_p_r_e_s_s_i_o_n _M_e_a_s_u_r_e

_D_e_s_c_r_i_p_t_i_o_n:

     This function converts a 'DataTreeSet' into an 'ExprTreeSet' using
     the robust multi-array average (RMA) expression measure.

_U_s_a_g_e:

     rma(xps.data,
         filename   = character(0),
         filedir    = getwd(),
         tmpdir     = "",
         background = "pmonly",
         normalize  = TRUE,
         option     = "transcript",
         exonlevel  = "",
         xps.scheme = NULL,
         add.data   = TRUE,
         verbose    = TRUE)

     xpsRMA(object, ...)

_A_r_g_u_m_e_n_t_s:

xps.data: object of class 'DataTreeSet'.

filename: file name of ROOT data file.

 filedir: system directory where ROOT data file should be stored.

  tmpdir: optional temporary directory where temporary ROOT files
          should be stored.

background: probes used to compute background, one of 'pmonly',
          'mmonly', 'both'; for genome/exon arrays one of 'genomic',
          'antigenomic'

normalize: logical. If 'TRUE' normalize data using quantile
          normalization.

  option: option determining the grouping of probes for summarization,
          one of  'transcript', 'exon', 'probeset'; exon arrays only.

exonlevel: exon annotation level determining which probes should be
          used for summarization; exon/genome arrays only.

xps.scheme: optional alternative 'SchemeTreeSet'.

add.data: logical. If 'TRUE' expression data will be included as slot
          'data'.

 verbose: logical, if 'TRUE' print status information.

  object: object of class 'DataTreeSet'.

     ...: the arguments described above.

_D_e_t_a_i_l_s:

     This function computes the RMA (Robust Multichip Average)
     expression measure described in  Irizarry et al. for both
     expression arrays and exon arrays. For exon arrays it is necessary
      to supply the requested 'option' and 'exonlevel'.

     Following 'option's are valid for exon arrays:

       'transcript':  expression levels are computed for transcript clusters, i.e. probe sets containing the same 'transcript_cluster_id'.
       'exon':        expression levels are computed for exon clusters, i.e. probe sets containing the same 'exon_id', where each exon cluster consists of one or more 'probeset's.
       'probeset':    expression levels are computed for individual probe sets, i.e. for each 'probeset_id'.

     Following 'exonlevel' annotations are valid for exon arrays:

         'core':          probesets supported by RefSeq and full-length GenBank transcripts.
         'metacore':      core meta-probesets.
         'extended':      probesets with other cDNA support.
         'metaextended':  extended meta-probesets.
         'full':          probesets supported by gene predictions only.
         'metafull':      full meta-probesets.
         'affx':          standard AFFX controls.
         'all':           combination of above (including affx).

     Following 'exonlevel' annotations are valid for whole genome
     arrays:

         'core':      probesets with category 'unique', 'similar' and 'mixed'.
         'metacore':  probesets with category 'unique' only.
         'affx':      standard AFFX controls.
         'all':       combination of above (including affx).

     Exon levels can also be combined, with following combinations
     being most useful:

       'exonlevel="metacore+affx"':       core meta-probesets plus AFFX controls
       'exonlevel="core+extended"':       probesets with cDNA support
       'exonlevel="core+extended+full"':  supported plus predicted probesets

     Exon level annotations are described in the Affymetrix whitepaper
     exon_probeset_trans_clust_whitepaper.pdf: 
       "Exon Probeset Annotations and Transcript Cluster Groupings".

     In order to use an alternative 'SchemeTreeSet' set the
     corresponding SchemeSet 'xps.scheme'.

     'xpsRMA' is the 'DataSet' method called by function 'rma', 
     containing the same parameters.

_V_a_l_u_e:

     An 'ExprTreeSet'

_N_o_t_e:

     In contrary to other implementations of RMA the expression measure
     is given to you in linear scale, analogously to the expression
     measures computed with 'mas5' and 'mas4'. 

     It is also possible to skip background correction by setting
     parameter 'background="none"'.

     For the analysis of many exon arrays it may be better to define a
     'tmpdir', since this will store only the results in the main file
     and not e.g. background and normalized intensities, and thus will
     reduce the file size of the main file. For quantile normalization
     memory should not be an issue, however medianpolish depends on RAM
     unless you are using a temporary file.

     Parameter 'exonlevel' determines not only which probes are used
     for medianpolish, but also the probes used for background
     calculation and for quantile normalization. If you want to use
     seperate probes for background calculation, quantile normalization
     and medianpolish summarization, you can pass a numeric vector
     containing three integer values corresponding to the respective 
     'exonlevel', e.g. you can use 'exonlevel=c(16316,8252,8252)', see
     function  'exonLevel' for more details.

_A_u_t_h_o_r(_s):

     Christian Stratowa

_R_e_f_e_r_e_n_c_e_s:

     Rafael. A. Irizarry, Benjamin M. Bolstad, Francois Collin, Leslie
     M. Cope, Bridget Hobbs and Terence P. Speed (2003), Summaries of
     Affymetrix GeneChip probe level data Nucleic Acids Research
     31(4):e15

     Bolstad, B.M., Irizarry R. A., Astrand M., and Speed, T.P. (2003),
     A Comparison of Normalization Methods for High Density
     Oligonucleotide Array Data Based on Bias and Variance.
     Bioinformatics 19(2):185-193

     Irizarry, RA, Hobbs, B, Collin, F, Beazer-Barclay, YD, Antonellis,
     KJ, Scherf, U, Speed, TP (2003) Exploration, Normalization, and
     Summaries of High Density Oligonucleotide Array Probe Level Data.
     Biostatistics .Vol. 4, Number 2: 249-264

_S_e_e _A_l_s_o:

     'express'

_E_x_a_m_p_l_e_s:

     ## first, load ROOT scheme file and ROOT data file
     scheme.test3 <- root.scheme(paste(.path.package("xps"),"schemes/SchemeTest3.root",sep="/"))
     data.test3 <- root.data(scheme.test3, paste(.path.package("xps"),"rootdata/DataTest3_cel.root",sep="/"))

     data.rma <- rma(data.test3,"tmp_Test3RMA",tmpdir="",background="pmonly",normalize=TRUE,verbose=FALSE)

     ## get data.frame
     expr.rma <- validData(data.rma)
     head(expr.rma)

     ## plot results
     if (interactive()) {
     boxplot(data.rma)
     boxplot(log2(expr.rma))
     }

     rm(scheme.test3, data.test3)
     gc()

     ## Not run: 
     ## examples using Affymetrix human tissue dataset (see also xps/examples/script4exon.R)
     ## first, load ROOT scheme file and ROOT data file from e.g.:
     scmdir <- "/Volumes/GigaDrive/CRAN/Workspaces/Schemes"
     datdir <- "/Volumes/GigaDrive/CRAN/Workspaces/ROOTData"

     ## 1. example - expression array, e.g. HG-U133_Plus_2:
     scheme.u133p2 <- root.scheme(paste(scmdir,"Scheme_HGU133p2_na25.root",sep="/"))
     data.u133p2   <- root.data(scheme.u133p2, paste(datdir,"HuTissuesU133P2_cel.root",sep="/"))

     workdir <- "/Volumes/GigaDrive/CRAN/Workspaces/Exon/hutissues/u133p2"
     data.rma <- rma(data.u133p2,"MixU133P2RMA",filedir=workdir,tmpdir="",
                     background="pmonly",normalize=TRUE)

     ## 2. example - whole genome array, e.g. HuGene-1_0-st-v1:
     scheme.genome <- root.scheme(paste(scmdir,"Scheme_HuGene10stv1r3_na25.root",sep="/"))
     data.genome   <- root.data(scheme.genome, paste(datdir,"HuTissuesGenome_cel.root",sep="/"))

     workdir <- "/Volumes/GigaDrive/CRAN/Workspaces/Exon/hutissues/hugene"
     data.g.rma <- rma(data.genome,"HuGeneMixRMAMetacore",filedir=workdir,tmpdir="",
                       background="antigenomic",normalize=T,exonlevel="metacore+affx")

     ## 3. example - exon array, e.g. HuEx-1_0-st-v2:
     scheme.exon <- root.scheme(paste(scmdir,"Scheme_HuEx10stv2r2_na25.root",sep="/"))
     data.exon   <- root.data(scheme.exon, paste(datdir,"HuTissuesExon_cel.root",sep="/"))

     workdir <- "/Volumes/GigaDrive/CRAN/Workspaces/Exon/hutissues/exon"
     data.x.rma <- rma(data.exon,"MixRMAMetacore",filedir=workdir,tmpdir="",background="antigenomic",
                       normalize=T,option="transcript",exonlevel="metacore")
     ## End(Not run)

