ArrayOutliers           package:arrayMvout           R Documentation

_M_u_l_t_i_v_a_r_i_a_t_e _o_u_t_l_i_e_r _d_e_t_e_c_t_i_o_n _b_a_s_e_d _o_n _P_C_A _o_f
_Q_A _s_t_a_t_i_s_t_i_c_s

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

     Multivariate outlier detection based on PCA of QA statistics

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

     ArrayOutliers (data, alpha, alphaSeq = c(0.01, 0.05, 0.1),  ... )
     #    qcOutput = NULL, plmOutput = NULL, degOutput = NULL, prscale = TRUE, 
     #    pc2use = 1:3) 

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

    data: an (affy) AffyBatch instance with at least 11 samples

   alpha: false positive rate for outlier detection, adjusting for
          multiple comparisons according to Caroni and Prescott's
          adaptation of Rosner (1983); full report based on this choice
          of alpha

alphaSeq: vector of alpha candidates to be quickly tried for short
          report

     ...: additional parameters, see below 

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

     Additional parameters may be supplied

     _q_c_O_u_t_p_u_t optional result of simpleaffy qc() to speed computations

     _p_l_m_O_u_t_p_u_t optional result of affyPLM fitPLM() to speed
          computations

     _d_e_g_O_u_t_p_u_t optional result of affy AffyRNAdeg() to speed
          computations

     _p_r_s_c_a_l_e scaling option for prcomp

     _p_c_2_u_s_e selection of principal components to use for outlier
          detection

     Data elements afxsubDEG, afxsubQC, s12cDEG, s12cQC are precomputed
     RNA degradation and simpleaffy qc() results; s12c is an AffyBatch
     with digital contamination of some samples.

     Data elements maqcQA and itnQA are affymetrix QC statistics on
     large collections of arrays.  Data element ilmQA is a derived from
     a LumiBatch of the Illumina-submitted MAQC raw data, 19 arrays. 
     (Conveyed by Leming Shi, personal communication). Data element
     spikQA is a 12x9 matrix of QA parameters obtained for 12 arrays
     from U133A spikein dataset, with first 2 arrays digitally
     contaminated as described in  Asare et al.

     Data element fig3map gives the indices of the points labeled A-H
     in Figure 3 of the manuscript by Asare et al. associated with this
     package.

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

     an instance of arrOutStruct class, a list with a partition of
     samples into two data frames (inl and outl) with QA summary
     statistics

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

     Z. Gao et al.

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

     library(simpleaffy)
     setQCEnvironment("hgu133acdf")  # no CDF corresponding to tag array
     if ( require("mvoutData") ) { 
      data(s12c)
      data(s12cQC)
      data(s12cDEG)
      library(affyPLM)
      s12cPset = fitPLM(s12c)
      ao = ArrayOutliers(s12c, alpha=0.05, qcOut=s12cQC, plmOut=s12cPset, degOut=s12cDEG)
      ao
      }
     if (require("lumiBarnes")) {
      library(lumiBarnes)
      data(lumiBarnes)
      ArrayOutliers(lumiBarnes, alpha=0.05)
      lb2 = lumiBarnes
      exprs(lb2)[1:20000,1:2] = 10000*exprs(lb2)[1:20000,1:2]
      ArrayOutliers(lb2, alpha=0.05)
      }
     data(maqcQA) # affy
     ArrayOutliers(maqcQA[,-c(1:2)], alpha=.05)
     ArrayOutliers(maqcQA[,-c(1:2)], alpha=.01)
     data(ilmQA) # illumina
     ArrayOutliers(data.frame(ilmQA), alpha=.01)
     data(itnQA) # 507 arrays from ITN
     ArrayOutliers(itnQA, alpha=.01)

