mas5.call                package:xps                R Documentation

_M_A_S _5._0 _A_b_s_o_l_u_t_e _D_e_t_e_c_t_i_o_n _C_a_l_l

_D_e_s_c_r_i_p_t_i_o_n:

     Performs the Wilcoxon signed rank-based gene expression
     presence/absence  detection algorithm first implemented in the
     Affymetrix Microarray Suite version 5.

_U_s_a_g_e:

     mas5.call(xps.data,
               filename = character(0), filedir = getwd(), tmpdir = "",
               tau = 0.015, alpha1 = 0.04, alpha2 = 0.06, ignore.saturated = TRUE,
               option = "transcript", exonlevel = "", xps.scheme = NULL, add.data = TRUE, verbose = TRUE)

     xpsMAS5Call(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.

     tau: a small positive constant.

  alpha1: a significance threshold in (0,alpha2).

  alpha2: a significance threshold in (alpha1,0.5).

ignore.saturated: logical. If 'TRUE' do the saturation correction
          described in the paper, with a saturation level of 46000.

  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' call data will be added to slots 'data'
          and 'detcall'.

 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 performs the hypothesis test:

     H0: median(Ri) = tau, corresponding to absence of transcript H1:
     median(Ri) > tau, corresponding to presence of transcript

     where Ri = (PMi - MMi) / (PMi + MMi) for each i a probe-pair in
     the probe-set represented by data.

     The p-value that is returned estimates the usual quantity:

     Pr(observing a more "present looking" probe-set than data | data
     is absent)

     Small p-values imply presence while large ones imply absence of
     transcript.  The detection call is computed by thresholding the
     p-value as in:

     call "P" if p-value < alpha1 
      call "M" if alpha1 <= p-value < alpha2 
      call "A" if alpha2 <= p-value

     The defaults for 'tau', 'alpha1' and 'alpha2' correspond to those
     in MAS5.0 for expression arrays. However, when using this function
     for exon or whole genome arrays, new values for 'alpha1' and
     'alpha2' must be determined. The recommended function for
     exon/genome arrays is 'dabg.call'.

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

     'xpsMAS5Call' is the 'DataTreeSet' method called by function
     'mas5.call',  containing the same parameters.

_V_a_l_u_e:

     A 'CallTreeSet'

_A_u_t_h_o_r(_s):

     Christian Stratowa

_R_e_f_e_r_e_n_c_e_s:

     Liu, W. M. and Mei, R. and Di, X. and Ryder, T. B. and Hubbell, E.
     and Dee, S. and Webster, T. A. and Harrington, C. A. and Ho, M. H.
     and Baid, J. and Smeekens, S. P. (2002) Analysis of high density
     expression microarrays with signed-rank call algorithms,
     Bioinformatics, 18(12), pp. 1593-1599.

     Liu, W. and Mei, R. and Bartell, D. M. and Di, X. and Webster, T.
     A. and Ryder, T. (2001) Rank-based algorithms for analysis of
     microarrays, Proceedings of SPIE, Microarrays: Optical
     Technologies and Informatics, 4266.

     Affymetrix (2002) Statistical Algorithms Description Document,
     Affymetrix Inc.,  Santa Clara, CA, whitepaper. <URL:
     http://www.affymetrix.com/support/technical/whitepapers/sadd_whitepaper.pdf>

_S_e_e _A_l_s_o:

     'dabg.call'

_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="/"))

     ## MAS5 detection call
     call.mas5 <- mas5.call(data.test3,"tmp_Test3Call",tmpdir="",verbose=FALSE)

     ## get data.frames
     pval.mas5 <- pvalData(call.mas5)
     pres.mas5 <- presCall(call.mas5)
     head(pval.mas5)
     head(pres.mas5)

     ## plot results
     if (interactive()) {
     callplot(call.mas5, beside=FALSE, ylim=c(0,125))
     }

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

