EVA: Evaluation of Metabolic Feature Fidelity Using a Deep Learning Model Trained With Over 25000 Extracted Ion Chromatograms

Extracting metabolic features from
liquid chromatography–mass
spectrometry (LC-MS) data relies on the recognition of extracted ion
chromatogram (EIC) peak shapes using peak picking algorithms. Unfortunately,
all peak picking algorithms present a significant drawback of generating
a problematic number of false positives. In this work, we take advantage
of deep learning technology to develop a convolutional neural network
(CNN)-based program that can automatically recognize metabolic features
with poor EIC shapes, which are of low feature fidelity and more likely
to be false. Our CNN model was trained using 25095 EIC plots collected
from 22 LC-MS-based metabolomics projects of various sample types,
LC and MS conditions. Notably, we manually inspected all the EIC plots
to assign good or poor EIC quality for accurate model training. The
trained CNN model is embedded into a C#-based program, named EVA (short
for evaluation). The EVA Windows Application is a versatile platform
that can process metabolic features generated by LC-MS systems of
various vendors and processed using various data processing software.
Our comprehensive evaluation of EVA indicates that it achieves over
90% classification accuracy. EVA can be readily used in LC-MS-based
metabolomics projects and is freely available on the Microsoft Store
by searching “EVA Metabolomics”.