defineMatBins: Define the bins, mean & sd for log background signal using...
In steveped/BMEA: Bayesian Modelling for Exon Arrays

Description Usage Arguments Details Value References See Also

Define the boundaries for the background signal, based on the fitted MAT values for each probe, and the subsequent means & standard deviations.

1	defineMatBins(bgParam, nBins = 20, fitIndividual = TRUE)

`bgParam`	the output from the function `fitBackgroundParameters`. Must contain the slot `$coef`.
`nBins`	specifies the number of bins to form based on the fitted MAT values. Defaults to 20 bins.
`fitIndividual`	logical.Determines whether bin means & sds are estimated for each array or for the entire dataset.

Given the fitted MAT values from the background set of probes in the bgParam argument, the boundaries for forming the background prior distributions are calculated using this function. Bins are formed using approximately equal numbers of probes.

Returns a list with the components:

$boundariesthe boundaries for the bins. The lower boundary is implied as zero & the upper boundary is implies as Inf.
$lambdathe observed mean of the background probes classified into each bin
$delta the observed standard deviation of the background probes as classified into each bin

All means and standard deviations are on the log-scale and refer to the normal distributions for log(Background).

Kapur, K., Xing, Y., Ouyang, Z., Wong, WH. (2007) Exon arrays provide accurate assessments of gene expression Genome Biol. 8(5):R82

Johnson, W.E., Li, W., Meyer, C.A., Gottardo, R., Carroll, J.S., Brown, M., Liu, X.S. (2006) Model-based analysis of tiling-arrays for ChIP-chip. Proc Natl Acad Sci USA 103:12457-12462

fitBackgroundParameters

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