Peak Detection On Data Independent Acquisition Mass Spectrometry Data With Semisupervised Convolutional Transformers
This addresses the problem of automated peak detection in proteomics for researchers, but it is incremental as it builds on existing architectures and techniques.
The paper tackles peak detection in Data Independent Acquisition mass spectrometry data by formulating it as a multivariate time series segmentation problem and proposes a novel approach using Transformers augmented with Convolutional Neural Networks, trained semisupervisedly, which outperforms baseline neural networks and is competitive with state-of-the-art methods.
Liquid Chromatography coupled to Mass Spectrometry (LC-MS) based methods are commonly used for high-throughput, quantitative measurements of the proteome (i.e. the set of all proteins in a sample at a given time). Targeted LC-MS produces data in the form of a two-dimensional time series spectrum, with the mass to charge ratio of analytes (m/z) on one axis, and the retention time from the chromatography on the other. The elution of a peptide of interest produces highly specific patterns across multiple fragment ion traces (extracted ion chromatograms, or XICs). In this paper, we formulate this peak detection problem as a multivariate time series segmentation problem, and propose a novel approach based on the Transformer architecture. Here we augment Transformers, which are capable of capturing long distance dependencies with a global view, with Convolutional Neural Networks (CNNs), which can capture local context important to the task at hand, in the form of Transformers with Convolutional Self-Attention. We further train this model in a semisupervised manner by adapting state of the art semisupervised image classification techniques for multi-channel time series data. Experiments on a representative LC-MS dataset are benchmarked using manual annotations to showcase the encouraging performance of our method; it outperforms baseline neural network architectures and is competitive against the current state of the art in automated peak detection.