normalize_pqn: Perform Probabilistic Quotient Normalization for intensities.

In lipidr: Data Mining and Analysis of Lipidomics Datasets

Description

Perform Probabilistic Quotient Normalization (PQN) for sample intensities. The PQN method determines a dilution factor for each sample by comparing the distribution of quotients between samples and a reference spectrum, followed by sample normalization using this dilution factor. The reference spectrum in this method is the average lipid abundance of all samples (excluding blanks).

Usage

normalize_pqn(data, measure = "Area", exclude = "blank", log = TRUE)

Arguments

data	LipidomicsExperiment object.
measure	Which measure to use as intensity, usually Area, Area Normalized or Height. Default is Area.
exclude	Samples to exclude, can be either: "blank" - automatically detected blank samples and exclude them logical vector with the same length as samples. Default.
log	Whether the normalized values should be log2 transformed. Default is TRUE.

Value

A LipidomicsExperiment object with normalized values

References

Dieterle, F., Ross, A., Schlotterbeck, G., & Senn, H. (2006). Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Analytical chemistry, 78(13), 4281-4290.

Examples

datadir <- system.file("extdata", package = "lipidr")
filelist <- list.files(datadir, "data.csv", full.names = TRUE)
d <- read_skyline(filelist)
clinical_file <- system.file("extdata", "clin.csv", package = "lipidr")
d <- add_sample_annotation(d, clinical_file)
d_summarized <- summarize_transitions(d, method = "average")

# Normalize data that have been summarized (single value per molecule).
data_normalized <- normalize_pqn(
  d_summarized,
  measure = "Area", exclude = "blank", log = TRUE
)

lipidr documentation built on Nov. 8, 2020, 7:50 p.m.