genki: SNP derived performance metrics for Illumina 450K DNA...
In wateRmelon: Illumina 450 methylation array normalization and metrics

Description Usage Arguments Details Value Note Author(s) References Examples

A very simple genotype calling by one-dimensional K-means clustering is performed on each SNP, and for those SNPs where there are three genotypes, the squared deviations are summed for each genotype (similar to a standard deviation for each of allele A homozygote, heterozygote and allele B homozygote). By default these are further divided by the square root of the number of samples to get a standard error-like statistic.

1	genki(bn, g = getsnp(rownames(bn)), se = TRUE)

`bn`	a matrix of beta values(default method), a `MethyLumiSet` object (`methylumi` package), a `MethylSet` or `RGChannelSet` object (`minfi` package) or a `exprmethy450` object (`IMA` package).
`g`	vector of SNP names
`se`	TRUE or FALSE specifies whether to calculate the standard error-like statistic

There are 65 well-behaved SNP genotyping probes included on the array. These each produce a distribution of betas with tight peaks for the three possible genotypes, which will be broadened by technical variation between samples. The spread of the peaks is thus usable as a performance metric.

a vector of 3 values for the dispersion of the three genotype peaks (AA, AB, BB : low, medium and high beta values)

Corrected RGChannelSet methods - 12/10/2015

Leonard.Schalkwyk@kcl.ac.uk

Pidsley R, Wong CCY, Volta M, Lunnon K, Mill J, Schalkwyk LC: A data-driven approach to preprocessing Illumina 450K methylation array data (submitted)

  #MethyLumiSet method
     data(melon)
     genki(melon)

  #MethyLumiSet method after normalization
     melon.dasen <- dasen(melon)
     genki(melon.dasen)