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Liquid Chromatography coupled to Mass Spectrometry (LC-MS) based methods arecommonly 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-MSproduces data in the form of a two-dimensional time series spectrum, with themass to charge ratio of analytes (m/z) on one axis, and the retention time fromthe chromatography on the other. The elution of a peptide of interest produceshighly specific patterns across multiple fragment ion traces (extracted ionchromatograms, or XICs). In this paper, we formulate this peak detectionproblem as a multivariate time series segmentation problem, and propose a novelapproach 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 contextimportant to the task at hand, in the form of Transformers with ConvolutionalSelf-Attention. We further train this model in a semisupervised manner byadapting state of the art semisupervised image classification techniques formulti-channel time series data. Experiments on a representative LC-MS datasetare benchmarked using manual annotations to showcase the encouragingperformance of our method; it outperforms baseline neural network architecturesand is competitive against the current state of the art in automated peakdetection.

Peak Detection On Data Independent Acquisition Mass Spectrometry Data With Semisupervised Convolutional Transformers