SparseCodePicking: feature extraction in mass spectrometry using sparse coding algorithms

TL;DR

This paper introduces a novel peak picking method for mass spectrometry data using sparse coding with elastic-net regularization, demonstrating improved performance over traditional methods through simulations and real datasets.

Contribution

The paper proposes a new sparse coding-based peak picking algorithm with elastic-net regularization for mass spectrometry data analysis, enhancing feature extraction accuracy.

Findings

Outperforms mean spectrum-based methods in simulations

Effective on real-life mass spectrometry datasets

Flexible parameter settings improve peak detection

Abstract

Mass spectrometry (MS) is an important technique for chemical profiling which calculates for a sample a high dimensional histogram-like spectrum. A crucial step of MS data processing is the peak picking which selects peaks containing information about molecules with high concentrations which are of interest in an MS investigation. We present a new procedure of the peak picking based on a sparse coding algorithm. Given a set of spectra of different classes, i.e. with different positions and heights of the peaks, this procedure can extract peaks by means of unsupervised learning. Instead of an $l_1$-regularization penalty term used in the original sparse coding algorithm we propose using an elastic-net penalty term for better regularization. The evaluation is done by means of simulation. We show that for a large region of parameters the proposed peak picking method based on the sparse coding features outperforms a mean spectrum-based method. Moreover, we demonstrate the procedure applying it to two real-life datasets.