poplin_normalize: Normalization methods

In jaehyunjoo/poplin: LC/MS Metabolomics Data Processing Utilities

Description

Data normalization aims to reduce unwanted variations. The poplin package provides a few data-driven normalization methods. poplin_normalize is a wrapper for the following set of functions:

normalize_pqn:

PQN (probabilistic quotient normalization)

normalize_sum:

sum normalization

normalize_mean:

mean normalization

normalize_median:

median normalization

normalize_mad:

MAD (median absolute deviation) normalization

normalize_cyclicloess:

cyclic LOESS normalization

normalize_vsn:

VSN (variance stabilizing normalization)

normalize_scale:

feature-based scaling (e.g., auto, range, pareto, vast, level)

Usage

## S4 method for signature 'matrix'
poplin_normalize(
  x,
  method = c("pqn", "sum", "mean", "median", "mad", "cyclicloess", "vsn", "scale"),
  ...
)

## S4 method for signature 'poplin'
poplin_normalize(
  x,
  method = c("pqn", "sum", "mean", "median", "mad", "cyclicloess", "vsn", "scale"),
  xin,
  xout,
  ...
)

Arguments

x	A matrix or poplin object.
method	The normalization method to be used, defaulting to "pqn".
...	Arguments passed to a specific normalization method.
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.

Value

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

See Also

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

Examples

data(faahko_poplin)

## poplin object
poplin_normalize(faahko_poplin, method = "pqn",
                 xin = "knn", xout = "knn_pqn")

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

jaehyunjoo/poplin documentation built on Jan. 8, 2022, 1:13 a.m.