Signal Processing and Machine Learning for Metabolomics

Dear Colleagues,

Metabolomics has witnessed astonishing advances in the last decade, based in new instrumental developments in mass spectrometry and magnetic nucelar resonance, with profound implications in life sciences. These instruments provide rich and complex signals, where the relevant information is hidden among an incredible amount of noise related to other sources of variance (lifestyle, diet, genomics variablity) different from the source of interest (related to health condition, therary or toxic exposure). The analysis of these signals is particularly challenging in untargeted metabolomics, aiming to make an exhaustive analysis of the complete metabolome available in a certain body fluid. Metabolite identification with libraries and databases is hindered by noise both in mass spectrometry and nuclear magnetic resonance.

While the analysis of serum and urine is the main target, metabolomics is expanding to the analysis of other fluids such as breath, tears, sweat, faeces, intestinal gases, vaginal discharges and others. In these cases, signal analysis and interpretation is hindered by incomplete understanding of the metabolome, and additional sources of variability related to the absence of standarized methods for sample collection and storage prior to analysis.

Beyond the traditional mature analytical techniques many emerging instrumental techniques are also entering the metabolomics field. Examples of these technologies are chemical sensors, but also miniature spectrometers based on Ion Mobility, direct injection mass spectrometry, and spectroscopies, such as NIR, Visible or Raman. Recent technological advances allow that the principles of measurement, only available in desktops, in analytical lab instrumentation, are now handheld instruments, operating in point-of-care scenarios, with on-board signal and data processing. This novel instrumentation provides signals or spectral time-series that require online signal and data processing in order to obtain the final desired results.

A variety of signal processing problems appear in this domain and they require different signal and data processing tools. Most spectral signals require baseline correction, noise removal, peak alignment, peak dectection and peak deconvolution. Here there is a lot of interest in matrix factorization techniques such as NMF but also higher order tensorial methods, such as PARAFAC, Tucker 3, and others, with a variety of constraints related to prior information on the solution. Beyond that, systems require solutions for drift compensation, calibration transfer, or chemical noise (interferants) rejection. In terms of self-diagnostics, we need techniques for fault detection, identification, and diagnosis. After preprocessing, data is further analyzed using machine learning techniques mostly for classification but also regression problems. In addition, optimization techniques, required for optimal feature extraction or selection (biomarker discovery), are also needed during algorithmic development. The development of methodologies for system validation to ensure proper generalization to new samples is equally important. Last, but not least, the use of design of experiment techniques in system calibration or recalibration also require further research.

We solicit contributions focused on signal processing and machine learning for metabolomics in the following, or similar, areas:

• Signal pre-processing: Digital filters, baseline corrections, peak detection and alignment
• Peak deconvolution using matrix factorization techniques: NMF, MCR-ALS, Hard and soft-constraints
• Tensorial decomposition methods for N-way data: PARAFAC, PARAFAC2, Tucker3
• Orthogonal Projection Filters: Component Correction, Net Analyte Signal, Orthogonal Signal Correction,  OPLS, O2PLS
• Fault detection, identification and diagnosis
• Counteraction of instrumental shifts
• Feature Extraction and Selection
• Classification and Regression techniques
• Validation techniques
• Novelty detection
• Calibration transfer
• Development of applications with novel approaches for signal processing and machine learning
• Applications of metabolomics strategies in related areas such as Foodomics.
• Development of Software Packages in R, Python, MATLAB for signal processing and machine learning

Dr. Santiago Marco
Dr. Jesús Brezmes
Dr. Alexandre Perera Lluna
Guest Editors

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• Baseline Corrections
• Instrumental shift compensation
• Peak deconvolution
• Peak detection
• Metabolite identification