Aggregated Molecular Phenotype Scores: Enhancing Assessment and Visualization of Mass Spectrometry Imaging Data for Tissue-Based Diagnostics

Mass spectrometry imaging (MSI) has gained increasing
popularity
for tissue-based diagnostics due to its ability to identify and visualize
molecular characteristics unique to different phenotypes within heterogeneous
samples. Data from MSI experiments are often assessed and visualized
using various supervised and unsupervised statistical approaches.
However, these approaches tend to fall short in identifying and concisely
visualizing subtle, phenotype-relevant molecular changes. To address
these shortcomings, we developed aggregated molecular phenotype (AMP)
scores. AMP scores are generated using an ensemble machine learning
approach to first select features differentiating phenotypes, weight
the features using logistic regression, and combine the weights and
feature abundances. AMP scores are then scaled between 0 and 1, with
lower values generally corresponding to class 1 phenotypes (typically
control) and higher scores relating to class 2 phenotypes. AMP scores,
therefore, allow the evaluation of multiple features simultaneously
and showcase the degree to which these features correlate with various
phenotypes. Due to the ensembled approach, AMP scores are able to
overcome limitations associated with individual models, leading to
high diagnostic accuracy and interpretability. Here, AMP score performance
was evaluated using metabolomic data collected from desorption electrospray
ionization MSI. Initial comparisons of cancerous human tissues to
their normal or benign counterparts illustrated that AMP scores distinguished
phenotypes with high accuracy, sensitivity, and specificity. Furthermore,
when combined with spatial coordinates, AMP scores allow visualization
of tissue sections in one map with distinguished phenotypic borders,
highlighting their diagnostic utility.