normalize_loess: Loess Normalization

In pmartR/pmartR: Panomics Marketplace - Quality Control and Statistical Analysis for Panomics Data

Loess Normalization

Description

Perform Loess normalization

Usage

normalize_loess(omicsData, method = "fast", span = 0.4)

Arguments

omicsData	an object of the class 'pepData', 'proData', 'metabData', 'lipidData', or 'nmrData', created by as.pepData, as.proData, as.metabData, as.lipidData, or as.nmrData, respectively. The function group_designation must have been run on omicsData to use several of the subset functions (i.e. rip and ppp_rip).
method	character string specifying which variant of the cyclic loess method to use. Options are "fast" (default), "affy", or "pairs"
span	span of loess smoothing window, between 0 and 1.

Details

A wrapper for the normalizeCyclicLoess function from the limma package.

Value

The normalized data is returned in an object of the appropriate S3 class (e.g. pepData), on the same scale as omicsData (e.g. if omicsData contains log2 transformed data, the normalization will be performed on the non-log2 scale and then re-scaled after normalization to be returned on the log2 scale).

References

Bolstad, B. M., Irizarry R. A., Astrand, M., and Speed, T. P. (2003). A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics 19, 185-193.

Ballman, KV Grill, DE, Oberg, AL and Therneau, TM (2004). Faster cyclic loess: normalizing RNA arrays via linear models. Bioinformatics 20, 2778-2786.

See Also

normalizeCyclicLoess in the limma package

Examples

library(pmartRdata)
mypep <- edata_transform(pep_object, "log2")
result <- normalize_loess(mypep)

pmartR/pmartR documentation built on March 4, 2024, 8:32 a.m.