creNorm: Creatinine Normalisation

In kylebario/unorm: Urine Normalisation

Description

Creatinine Normalisation (CN) is a useful method much like region of interest normalisation that can normalise spectra based on the total area of the creatinine signal at the chemical shift 3.05ppm.

Usage

creNorm(X, ppm = NULL, cre3 = c(3, 3.1), cre4 = c(4, 4.1), err = 5)

Arguments

X	A numerical matrix with rows being the spectra and columns being the chemical shift variables.
ppm	A numerical array holding the chemical shift values of the X matrix.
cre3	A concatenated numerical value of the lower and upper ppm values where the creatinine peak at 3.05 starts and ends.
cre4	A concatenated numerical value of the lower and upper ppm values where the creatinine peak at 4.05 starts and ends.
err	The level of error given when calculating the creatinine peak ratios. interperted as a percentage (i.e., 5 = 5%)

Details

How It Works:

1. For each experimental spectra, the integral (total area) of the region defined by the bounds in the argument cre3 is calculated.

2. Each intensity in that spectra is then divided by the integral. This is iterated over for all spectra.

Advantages:

• CN is computationally light and is not as difficult as other normalisations like pqNorm()

• Can be useful for people who have not done any physical exercise recently or of similar age, gender and muscle mass.

Limitations:

• Complications arise from inter- and intra-individual creatinine signal fluctuation caused by exercise (which causes muscle breakdown and creatinine production) when using CN.

• CN relies on the idea that creatinine concentrations do not change in urine samples, the only changes to creatinine signal are created by effects of dilution but this is incorrect.

• Creatinine concentrations fluctuate depending on a person's physiology and behaviour so use CN consciously. Consider using in conjunction with a reference-based normalisation like pqNorm().

Value

A list of:

1. The normalised X matrix,

2. A numerical array of the corresponding dilution factors, and

3. The ratio of the creatinine signals (ideally should equal 0.667).

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

Author(s)

[e-mail]

See Also

More on the methodology of CN and issue with using it are outlined here: https://doi.org/10.1021/ac051632c

Other Attribute-Based: qNorm(), roiNorm(), taNorm(), vecNorm()

Examples

# When X contains multiple spectra, ppm is not required
data(X)
creNorm(X)
cat(dilf_cre)

# When X has only one spectrum, ppm is required
data(X, ppm)
creNorm(X[1,], ppm)
cat(dilf_cre)

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