ArrayOutliers: Multivariate outlier detection based on PCA of QA statistics
In arrayMvout: multivariate outlier detection for expression array QA

Description Usage Arguments Details Value Author(s) Examples

Multivariate outlier detection based on PCA of QA statistics

1
2
3

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

`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

Additional parameters may be supplied

qcOutput: optional result of simpleaffy qc() to speed computations
plmOutput: optional result of affyPLM fitPLM() to speed computations
degOutput: optional result of affy AffyRNAdeg() to speed computations
prscale: scaling option for prcomp
pc2use: 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.

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

Z. Gao et al.

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)