pqNorm: Probabilistic Quotient Normalisation

In kylebario/unorm: Urine Normalisation

Description

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

Usage

pqNorm(
  X,
  noi,
  use_ta = F,
  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

How It Works:

PQN works by normalising experimental spectra in the provided X matrix in relation to a reference spectrum.

1. pqNorm() creates the reference automatically by calculating the median spectrum of X as outlined in the initial methods paper (see 'See also')

2. pqNorm() derives a quotient for each ppm value within the limits of shift in a experimental spectrum by dividing it's intensities with that of the reference spectrum's.

3. The most frequently occurring (your choice of median or mode) quotient is calculated and is said to be the dilution coefficient (dilf) of that spectrum.

4. The sample is then scaled with this dilf and will be comparable with all other spectra normalised with the reference spectrum.

Background Information:

• PQN is currently the gold standard for normalising NMR spectra.

• Multiple studies have tested its validity.

• PQN reliably normalises spectra and handles large amounts of noise.

• Using pqNorm() on urine spectra will give best results out of the all methods except xfNorm()

Advantages:

• PQN is not impacted by outlining signals in the way that total area normalisation (taNorm()) is because it aims to find the most frequently occuring (median) dilf.

• PQN is not reliant on one signal such as creatinine normalisation (creNorm()) meaning that any factors that impact that one signal have less weight and don't affect the calculation of the dilf as much.

Limitations:

• PQN's biggest limitation is that it operates on the assumption that the majority of the spectra will stay constant so hugely varying spectra may be suboptimally normalised.

• PQN is also vulnerable to peak shift because it compares intensities of the same ppm variable. If a peak is left- or right-shifted, the apex of one signal will not be compared against the apex of another and thus produce convoluted results.

Value

The output of this function is a list containing:

1. The normalised version of X in the first element and

2. A numerical array of the corresponding dilution factors calculated by the function.

• Following the example below will extract the results quickly and easily.

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(), qNorm2(), xfNorm()

Examples

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

kylebario/unorm documentation built on Dec. 21, 2021, 8:45 a.m.