MS Metabolomics Data Processing Utilities

Description Usage Arguments Value References See Also Examples

Apply probabilistic quotient normalization (PQN) to a matrix or poplin object. For the calculation of quotients, a reference spectrum needs to be obtained from a mean or median 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.

## S4 method for signature 'matrix'
normalize_pqn(
  x,
  ref_samples = colnames(x),
  min_frac = 0.5,
  type = c("mean", "median")
)

## S4 method for signature 'poplin'
normalize_pqn(
  x,
  xin,
  xout,
  ref_samples = colnames(x),
  min_frac = 0.5,
  type = c("mean", "median")
)

`x`	A matrix or poplin object.
`ref_samples`	A vector of sample names or indices to specify 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 minimum proportion of reference samples for features to be considered in the calculation of a reference spectrum, between 0 and 1. For `min_frac = 1`, all reference samples should have non-missing values for the features to be included.
`type`	A method to compute a reference spectrum. Either "mean" or "median".
`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.

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.

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

data(faahko_poplin)

## poplin object
normalize_pqn(faahko_poplin, xin = "knn", xout = "knn_pqn")

## matrix
m <- poplin_data(faahko_poplin, "knn")
normalize_pqn(m)