The Aristotle Classifier: Using the Whole Glycomic Profile To Indicate a Disease State

“The
totality is not, as it were, a mere heap, but the whole
is something besides the parts.”Aristotle. We built
a classifier that uses the totality of the glycomic profile, not restricted
to a few glycoforms, to differentiate samples from two different sources.
This approach, which relies on using thousands of features, is a radical
departure from current strategies, where most of the glycomic profile
is ignored in favor of selecting a few features, or even a single
feature, meant to capture the differences in sample types. The classifier
can be used to differentiate the source of the material; applicable
sources may be different species of animals, different protein production
methods, or, most importantly, different biological states (disease
vs healthy). The classifier can be used on glycomic data in any form,
including derivatized monosaccharides, intact glycans, or glycopeptides.
It takes advantage of the fact that changing the source material can
cause a change in the glycomic profile in many subtle ways: some glycoforms
can be upregulated, some downregulated, some may appear unchanged,
yet their proportionwith respect to other forms presentcan
be altered to a detectable degree. By classifying samples using the
entirety of their glycan abundances, along with the glycans’
relative proportions to each other, the “Aristotle Classifier”
is more effective at capturing the underlying trends than standard
classification procedures used in glycomics, including PCA (principal
components analysis). It also outperforms workflows where a single,
representative glycomic-based biomarker is used to classify samples.
We describe the Aristotle Classifier and provide several examples
of its utility for biomarker studies and other classification problems
using glycomic data from several sources.