Deep Neural Networks for Classification of LC-MS Spectral Peaks.

Clicks: 253
ID: 36149
2019
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Abstract
Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics has emerged as a valuable tool for biological discovery, capable of assaying thousands of diverse chemical entities in a single biospecimen. Processing of non-targeted LC-MS spectral data requires identification and isolation of true spectral features from the random, false noise peaks that comprise a significant portion of total signals, using inexact peak selection algorithms and time-consuming visual inspection of data. To increase the fidelity and speed of data processing, herein we establish, optimize and evaluate a machine learning pipeline employing deep neural networks as well as a simpler multiple logistic regression model for classification of spectral features from non-targeted LC-MS metabolomics data. Machine learning based approaches were found to remove up to 90% of false peaks from complex non-targeted LC-MS datasets without reducing true positive signals and exhibit excellent reproducibility across multiple datasets. Application of machine learning for non-targeted LC-MS based peak selection provides for robust and scalable peak classification and data filtering, enabling handling and processing of large scale, complex metabolomics datasets.
Reference Key
kantz2019deepanalytical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kantz, Edward;Tiwari, Saumya;Watrous, Jeramie D;Cheng, Susan;Jain, Mohit;
Journal Analytical chemistry
Year 2019
DOI
10.1021/acs.analchem.9b02983
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