Peak Alignment of Gas Chromatography-Mass Spectrometry Data with Deep Learning

TL;DR

ChromAlignNet is a deep learning model that effectively aligns GC-MS data peaks across samples, outperforming existing methods especially on complex datasets, and requires no user-defined parameters.

Contribution

The paper introduces ChromAlignNet, a deep learning approach for GC-MS peak alignment that surpasses traditional methods in accuracy and ease of use.

Findings

Achieves high AUC (~1) on simple datasets

Maintains good performance (~0.85 AUC) on complex datasets

Outperforms existing algorithms on complex data sets

Abstract

We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's retention time (RT) may not stay fixed across multiple chromatograms. To use GC-MS data for biomarker discovery requires alignment of identical analyte's RT from different samples. Current methods of alignment are all based on a set of formal, mathematical rules. We present a solution to GC-MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC-MS data sets of various complexities and analysed the alignment results quantitatively. We show the model has very good performance (AUC $\sim 1$ for simple data sets and AUC $\sim 0.85$ for very complex data sets). Further, our model easily outperforms existing algorithms on complex data sets. Compared with existing methods, ChromAlignNet is very easy to use as it requires no user input of reference chromatograms and parameters. This method can easily be adapted to other similar data such as those from liquid chromatography. The source code is written in Python and available online.