MS Metabolomics Data Processing Utilities

Description Usage Arguments Value References See Also Examples

Apply feature-based scaling to a matrix or poplin object. The supported methods include

Auto scaling (unit variance scaling): each feature is scaled by its standard deviation.
Range scaling: each feature is scaled by its range.
Pareto scaling: each feature is scaled by the square root of its standard deviation.
Vast scaling (variance stability scaling): it is an extension of auto scaling, using the product of standard deviation and coefficient of variation as a scaling factor.
Level scaling: each feature is scaled by its mean.

Note that each feature is mean-centered prior to division. See van den Berg et al. (2006) for details.

## S4 method for signature 'matrix'
normalize_scale(x, type = c("auto", "range", "pareto", "vast", "level"))

## S4 method for signature 'poplin'
normalize_scale(
  x,
  xin,
  xout,
  type = c("auto", "range", "pareto", "vast", "level")
)

`x`	A matrix or poplin object.
`type`	The scaling method to be applied.
`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.

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.

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

data(faahko_poplin)

## poplin object
normalize_scale(faahko_poplin, xin = "knn", xout = "knn_auto",
                type = "auto")

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