normalizeIntensity: Normalization methods

In HimesGroup/qmtools: Quantitative Metabolomics Data Processing Tools

Normalization methods

Description

Performs a few data-driven normalization methods on a matrix-like object or SummarizedExperiment object. The methods include probabilistic quotient normalization (PQN), cyclic loess normalization, feature-based scaling, and many others from the MsCoreUtils::normalize_matrix. See the details below.

Usage

## S4 method for signature 'ANY'
normalizeIntensity(
  x,
  method = c("pqn", "div.sum", "div.mean", "div.median", "div.mad", "center.mean",
    "center.median", "diff.median", "cyclicloess", "vsn", "quantiles",
    "quantiles.robust", "feature.scale"),
  ...
)

## S4 method for signature 'SummarizedExperiment'
normalizeIntensity(
  x,
  method = c("pqn", "div.sum", "div.mean", "div.median", "div.mad", "center.mean",
    "center.median", "diff.median", "cyclicloess", "vsn", "quantiles",
    "quantiles.robust", "feature.scale"),
  i,
  name,
  ...
)

Arguments

x	A matrix-like object or SummarizedExperiment object.
method	A string specifying which normalization method to use.
...	Arguments passed to a specific normalization method.
i	A string or integer value specifying which assay values to use when x is a SummarizedExperiment object.
name	A string specifying the name to be used to store the normalized intensities in x when x is a SummarizedExperiment object. If not specified, a matrix containing the normalized intensities is returned.

Details

The method argument can be one of "pqn", "cyclicloess", "vsn", "feature.scale", "div.sum", "div.mean", "div.median", "div.mad", "center.mean", "center.median", "diff.median", "quantiles", and "quantiles.robust".

• "pqn" performs probabilistic quotient normalization, as described in Dieterle et al. (2006). See normalizePQN for details.

• "cyclicloess" performs cyclic LOESS normalization using the limma::normalizeCyclicLoess. The input x is expected to contain log-transformed intensities. See Bolstad et al. (2003) and Ballman et al. (2004) for details. Please use type if you want to specify a cyclic loess method due to a name conflict with the existing argument in this function.

• "vsn" performs variance stabilizing normalization (VSN), as described in Huber et al. (2002). It produces normalized intensities based on a glog (generalized logarithm) scale. See the vsn::vsn2 for details.

• "feature.scale" performs feature-based scaling (applied along the rows) as described in van den Berg et al. (2006). See scaleRows for details.

• For "div.sum", "div.mean", "div.median", and "div.mad", the respective sample intensities are divided by the column sums, means, medians, or median absolute deviations. See scaleCols for details.

• "center.mean" and "center.median" center the intensities by subtracting the column means or medians, respectively.

• "diff.median" centers all samples so that they all match the grand median by subtracting the respective columns medians differences to the grand median.

• "quantiles" and "quantiles.robust" perform quantiles normalization, as described in Bolstad et al. (2003). See the preprocessCore::normalize.quantiles and preprocessCore::normalize.quantiles.robust for details.

Value

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

References

Laurent Gatto, Johannes Rainer and Sebastian Gibb (2021). MsCoreUtils: Core Utils for Mass Spectrometry Data. R package version 1.4.0. https://github.com/RforMassSpectrometry/MsCoreUtils

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.

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 Apr 20;43(7):e47. doi: 10.1093/nar/gkv007. Epub 2015 Jan 20. PMID: 25605792; PMCID: PMC4402510.

Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003 Jan 22;19(2):185-93. doi: 10.1093/bioinformatics/19.2.185. PMID: 12538238.

Ballman KV, Grill DE, Oberg AL, Therneau TM. Faster cyclic loess: normalizing RNA arrays via linear models. Bioinformatics. 2004 Nov 1;20(16):2778-86. doi: 10.1093/bioinformatics/bth327. Epub 2004 May 27.

Huber W, von Heydebreck A, Sültmann H, Poustka A, Vingron M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics. 2002;18 Suppl 1:S96-104. doi: 10.1093/bioinformatics/18.suppl_1.s96. PMID: 12169536.

van den Berg RA, Hoefsloot HC, Westerhuis JA, Smilde AK, van der Werf MJ. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics. 2006 Jun 8;7:142. doi: 10.1186/1471-2164-7-142. PMID: 16762068; PMCID: PMC1534033.

See Also

See normalizePQN, scaleRows, scaleCols, limma::normalizeCyclicLoess, and MsCoreUtils::normalize_matrix for the underlying functions that do the work.

Examples

data(faahko_se)

## SummarizedExperiment object
se <- normalizeIntensity(faahko_se, i = "knn", name = "knn_pqn",
                         method = "pqn")
assayNames(se)

##' ## Matrix
m <- assay(faahko_se, "knn")
normalizeIntensity(m, method = "feature.scale", type = "pareto")

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