fitProbeDistribution: Fit Probe-Level Convolution Distributions

In pkimes/upbm: Tools for the analysis of universal protein binding microarrays

Description

This function fits either a normal+exponential or normal+gamma convolution model to the probe-level intensities of a PBMExperiment. The normal+exponential convolution model is fit using limma::normexp.fit and the normal+gamma convolution model is fit using NormalGamma::normgam.fit. This function returns the model parameter estimates for each sample in a data.frame.

For more details on the estimated parameters, see the underlying functions.

Usage

fitProbeDistribution(
  pe,
  assay = SummarizedExperiment::assayNames(pe)[1],
  model = c("NormGam", "NormExp"),
  method = "mle"
)

Arguments

pe	a PBMExperiment object containing GPR intensity information.
assay	string name of the assay. (default = SummarizedExperiment::assayNames(pe)[1])
model	character string specifying model for fitting intensityies. Must be one of "NormGam", "NormExp". (default = "NormGam")
method	method used to estimate the parameters for normal+exponential model. See more details from limma::normexp.fit. (default = "mle")

Value

A data.frame with scans as rows and parameters in columns.

Author(s)

Dongyuan Song

References

• Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47.

• Plancade, S., Rozenholc, Y., & Lund, E. (2012). Generalization of the normal-exponential model: exploration of a more accurate parametrisation for the signal distribution on Illumina BeadArrays. BMC Bioinformatics, 13(1), 329.

pkimes/upbm documentation built on Oct. 17, 2020, 9:10 a.m.