  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.
