iccFun: Intra-cluster correlations (ICC) for Illumina bead arrays
In beadarrayFilter: Bead filtering for Illumina bead arrays

Description Usage Arguments Details Value Author(s) References Examples

Fits the filtering model of Forcheh et al. (2012) and derives the ICC that can be used for filtering. Fitting is done using a modified version of the “MLM.beadarray" function of Kim and Lin (2011).

1	iccFun(eSet, seSet, nSet, ProbeID, iccQuant, diffIcc = TRUE, keepData = TRUE)

`eSet`	The summarized and normalized average intensities
`seSet`	The standard errors of the average intensities
`nSet`	The number of beads used for summarization
`ProbeID`	The bead types idenficiation variable e.g, ProbeID
`iccQuant`	It refers to the number summary described in Forcheh et al. (2012). It can be a vector of quantiles or a single value (scalar). For example, iccQuant = c(0, 0.25 ,0.5, 0.75, 1) means that summarization of the ICCs be done at the minimum ICC, 0.25, 0.5, 0.75 for the 25, 50, and 75 quantiles and the maximum ICC respectively. iccQuant = 1 means that the ICCs be summarized only using the maximum ICC.
`diffIcc`	If TRUE, then iccQuant must be a vector, if FALSE, iccQuant must be a single value. The default is TRUE.
`keepData`	If TRUE, then, the ICC at the number summary, the within array variances, the between array variances as well as all the ICCs will be returned. If FALSE, only the ICCs at the number summary will be returned.

Bead filtering aims at removing non-informative bead types prior to the downstream analysis. This function takes as input the summarized and normalized average intensities (eSet), their standard errors (seSet) and the number of beads used for summarization (nSet). It fits the filtering model of Forcheh et al. (2012) and derives the ICCs. The obtained ICC can then be used for filtering bead types or to assess the filtering strategies. See examples for more on this.

`icc`	The summarized ICCs at the number summary
`withinvar`	The within array variances
`betweenvar`	The between array variances
`iccAll`	All the ICCs, i.e., one ICC per bead type per array

Anyiawung Chiara Forcheh, Geert Verbeke, Adetayo Kasim, Dan Lin, Ziv Shkedy, Willem Talloen, Hinrich WH Gohlmann, Lieven Clement.

Dunning, M.J., Smith, M.L., Ritchie, M.E., Tavare, S. (2007). beadarray: R classes and methods for Illumina bead-based data, Bioinformatics. 23(16):2183-2184.

Dunning, M.J. (2011). beadarrayExampleData: Example data for the beadarray package. R package version 1.0.4.

Forcheh, A.C, Verbeke, G., Kasim, A., Lin, D., Shkedy, Z., Talloen, W., Gohlmann, H.W.H, Clement, L. (2012). Gene Filtering in the Analysis of Illumina Microarray Experiments, Statistical Applications in Genetics and Molecular Biology, Berkeley Electronic Press, vol. 11(2), Article 3.

Kim, R.S. and Lin, J. (2011). Multi-level mixed effects models for bead arrays. Bioinformatics, 27(5):633-640.

require(beadarrayExampleData)
data(exampleSummaryData)
exampleSummaryDatalog2 <- channel(exampleSummaryData, "G")[1:40,]
exampleSummaryDataNorm <- normaliseIllumina(exampleSummaryDatalog2, 
method = "quantile", transform = "none")[1:40,]
aaa<-na.omit(data.frame(I(rownames(exprs(exampleSummaryDataNorm))),exprs(exampleSummaryDataNorm)))[1:40,]
ProbeID <- aaa[,1][1:40]
    eSet <- na.omit(exprs(exampleSummaryDataNorm))[1:40,]
    stddev <- na.omit(se.exprs(exampleSummaryDataNorm))[1:40,]
    nSet <- na.omit(attributes(exampleSummaryDataNorm)$assayData$nObservations)[1:40,]
    seSet<-stddev/sqrt(nSet)
    iccResults<-iccFun(eSet,seSet,nSet,ProbeID =ProbeID ,iccQuant=1,
    diffIcc=FALSE,keepData=TRUE)
    
    
    informProbeNames<- subset(iccResults$icc, iccResults$icc[,2]>=0.5)[,1]
filterDataNorm<-exampleSummaryDataNorm[informProbeNames,]