4 research outputs found
Data Normalization of <sup>1</sup>H NMR Metabolite Fingerprinting Data Sets in the Presence of Unbalanced Metabolite Regulation
Data normalization is an essential
step in NMR-based metabolomics.
Conducted properly, it improves data quality and removes unwanted
biases. The choice of the appropriate normalization method is critical
and depends on the inherent properties of the data set in question.
In particular, the presence of unbalanced metabolic regulation, where
the different specimens and cohorts under investigation do not contain
approximately equal shares of up- and down-regulated features, may
strongly influence data normalization. Here, we demonstrate the suitability
of the Shapiro–Wilk test to detect such unbalanced regulation.
Next, employing a Latin-square design consisting of eight metabolites
spiked into a urine specimen at eight different known concentrations,
we show that commonly used normalization and scaling methods fail
to retrieve true metabolite concentrations in the presence of increasing
amounts of glucose added to simulate unbalanced regulation. However,
by learning the normalization parameters on a subset of nonregulated
features only, Linear Baseline Normalization, Probabilistic Quotient
Normalization, and Variance Stabilization Normalization were found
to account well for different dilutions of the samples without distorting
the true spike-in levels even in the presence of marked unbalanced
metabolic regulation. Finally, the methods described were applied
successfully to a real world example of unbalanced regulation, namely,
a set of plasma specimens collected from patients with and without
acute kidney injury after cardiac surgery with cardiopulmonary bypass
use
Significant results from gender specific multiple regression analyses on the relation between VAT and serum bins.
<p>Significant results from gender specific multiple regression analyses on the relation between VAT and serum bins.</p
Significant associations between measures of obesity and urinary metabolite levels.
<p>Significant associations between measures of obesity and urinary metabolite levels.</p
Characteristics of the study population by sex.
<p>Characteristics of the study population by sex.</p