Description Usage Arguments Details Value Author(s) Examples

Multivariate outlier detection based on PCA of QA statistics

1 2 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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
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)
``` |

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