normalizePQN: Probabilistic quotient normalization (PQN)

In HimesGroup/qmtools: Quantitative Metabolomics Data Processing Tools

Probabilistic quotient normalization (PQN)

Description

Performs probabilistic quotient normalization (PQN) on a matrix-like object where rows present features and columns represent samples.

Usage

normalizePQN(x, ref_samples = NULL, min_frac = 0.5, type = c("median", "mean"))

Arguments

x	A matrix-like object.
ref_samples	A vector of sample names or column indices specifying reference samples for the calculation of quotients. Must be a subset of colnames(x) if it is a character vector. If NULL, all samples are used.
min_frac	A numeric value between 0 and 1 specifying a minimum proportion of reference samples for features to be included in the calculation of a reference spectrum.
type	A method to compute a reference spectrum. Either "median" or "mean".

Details

For the calculation of quotients, a reference spectrum needs to be obtained from a median or mean spectrum based on all spectra of the study or a subset of the study. Feature intensities are normalized by the median of quotients. See Dieterle et al. (2006) for details.

Value

A matrix of the same dimension as x containing the normalized intensities.

References

Dieterle F, Ross A, Schlotterbeck G, Senn H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal Chem. 2006 Jul 1;78(13):4281-90. doi: 10.1021/ac051632c. PMID: 16808434.

See Also

See normalizeIntensity that provides a SummarizedExperiment-friendly wrapper for this function.

Examples

data(faahko_se)

m <- assay(faahko_se, "knn")
normalizePQN(m)

HimesGroup/qmtools documentation built on April 16, 2023, 8 p.m.