pqNorm: Probabilistic Quotient Normalisation

In kylebario/concentr8r: MRS Data Preprocessing and Normalisation

Probabilistic Quotient Normalisation

Description

PQN is currently the gold standard method used to normalise NMR spectra.

Usage

pqNorm(
  X,
  noi,
  use_ta = FALSE,
  uv_used = "mode",
  calc_region = c(0.5, 9.5),
  bin_width = 0.01
)

Arguments

X	The numerical matrix containing the NMR data you wish to normalise. This should be a preprocessed matrix with baseline correction, tsp calibration and non-quantitative region removal performed on it. The rows must contain information of one whole spectrum and the columns contain the specific chemical shift variables.
noi	Takes an array that is row matched to the X matrix you are normalising with the values equaling the maximum noise estimation for each spectra respectively.
use_ta	Requires a boolean TRUE or FALSE if total area normalisation should be performed on the spectra before PQN is.
uv_used	PQN utilises finding the median or the mode, which are both Univariate methods. Recognises either the string 'median' or 'mode' to instruct which method to use. Default = 'mode'
calc_region	The lower and upper bounds of the spectrum that will be used to calculate the dilution coeficient
bin_width	The width of the bin when the spectra are binned

Details

pqNorm() functions by calculating the median value for each column and divides each element of a spectrum by its corresponding median. The most frequently occurring quotient (result of division) is then called the dilution factor for that spectrum and all elements within that spectrum are divided by it, scaling the spectrum.

Value

This function assigns the normalised X argument (as X_pqn) and the calculated dilution factors (as dilf_pqn) to the global environment.

Author(s)

[e-mail]

See Also

The methods paper first describing PQN can be found here: https://doi.org/10.1021/ac051632c

Other Reference-Based: hmNorm(), q2Norm(), xfNorm()

Examples

data(X, noi)
pqNorm(X, noi)
cat(dilf_pqn)

kylebario/concentr8r documentation built on Nov. 9, 2022, 12:47 a.m.