Peak Alignment of GC-MS Data with Deep Learning

We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatogram-Mass Spectrometry (GC-MS) data. GC-MS is regarded as a gold standard in analysis of chemical composition in samples. However, due to the complexity of the instrument, a substance's retention time (RT) may not stay fixed across multiple GC-MS 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, consequently, they are unable to handle the complexity of GC-MS data from human breath. 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 show the model has very good true position rates (up to 99% for easy data sets and up to 92% for very complex data sets). We compared our model with the popular correlation optimized warping (COW) and show our model has much better overall performance. This method can easily be adapted to other similar data such as those from liquid chromatography.