MS Metabolomics Data Processing Utilities

Description Usage Arguments Value References See Also Examples

Apply Cyclic LOESS normalization to a matrix or poplin object. This is an interface to the normalizeCyclicLoess from the limma package. The input x is expected to contain log-transformed raw intensities. See Bolstad et al. (2003) and Ballman et al. (2004) for details.

## S4 method for signature 'matrix'
normalize_cyclicloess(
  x,
  pre_log2,
  type = c("fast", "affy", "pairs"),
  span = 0.7,
  iterations = 3
)

## S4 method for signature 'poplin'
normalize_cyclicloess(
  x,
  xin,
  xout,
  pre_log2,
  type = c("fast", "affy", "pairs"),
  span = 0.7,
  iterations = 3
)

`x`	A matrix or poplin object.
`pre_log2`	Logical controlling whether feature intensities are log2-transformed before normalization.
`type`	Character specifying which variant of the cyclic LOESS method to use.
`span`	Span of LOESS smoothing window, between 0 and 1.
`iterations`	Number of times to cycle through all pairs of columns.
`xin`	Character specifying the name of data to retrieve from `x` when `x` is a poplin object.
`xout`	Character specifying the name of data to store in `x` when `x` is a poplin object.

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

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. PMID: 15166021.

Other normalization methods: normalize_mad(), normalize_mean(), normalize_median(), normalize_pqn(), normalize_scale(), normalize_sum(), normalize_vsn(), poplin_normalize()

data(faahko_poplin)

if (requireNamespace("limma", quietly = TRUE)) {
  ## poplin object
  normalize_cyclicloess(faahko_poplin, xin = "knn", xout = "knn_cyclic",
                       pre_log2 = TRUE)
  ## matrix
  m <- poplin_data(faahko_poplin, "knn")
  normalize_cyclicloess(m, pre_log2 = TRUE)
}