defineGcBins: Estimate the mean and standard deviation for the log...
In steveped/BMEA: Bayesian Modelling for Exon Arrays

Based on the supplied set of background probes, estimate the background mean and standard deviation for a given GC count.

1 2	defineGcBins(celSet, cdf = NULL, bgProbes = "r2.genomic.bgp", nBins = 20, fitIndividual = TRUE)

`celSet`	the set of arrays being processed, as an `AffymetrixCelSet`.
`cdf`	an alternate cdf. This will override the one specified in `celSet`. If using a custom Affymetrix cdf, the original Affymetrix cdf should be specified here as the custom one may not contain the set of background probes.
`bgProbes`	the tab delimited file containing the sequence data for each background probe.
`nBins`	the number of bins to form. Defaults to 20
`fitIndividual`	logical variable. If `fitIndividual=TRUE`, the parameters will be estimated on an array-specific basis, otherwise the parameters will be estimated across the entire celSet.

This function finds the log scale mean (lambda) and standard deviation (delta) of the specified bgProbes for each gc_count.

A list with the following components:

lambdathe mean of the log-transformed bgProbes for a given gc_count
deltathe standard deviation of the log-transformed bgProbes for a given gc_count

For easy downstream usage, the names/rownames for each component are specified as "GC4" through to "GC25."

steveped/BMEA documentation built on May 30, 2019, 5:38 p.m.