Mass Spectrometry Analysis Using MALDIquant

Gibb, Sebastian; Strimmer, Korbinian

doi:10.1007/978-3-319-45809-0_6

Cited by 22 publications

(15 citation statements)

References 67 publications

(96 reference statements)

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“…All pre-processing components written in italics are methods and parameter choices that can be adjusted. To reflect the common practice, we have selected parameters provided in the official documentation ( Gibb, 2019 ). The reference peak detection deviates from the documentation, as the tolerance parameter had to be loosened from 0.002 to 0.004 in order to find common warping peaks among the dataset.…”

Section: Methodsmentioning

confidence: 99%

Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra

et al. 2020

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Motivation Microbial species identification based on matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has become a standard tool in clinical microbiology. The resulting MALDI-TOF mass spectra also harbour the potential to deliver prediction results for other phenotypes, such as antibiotic resistance. However, the development of machine learning algorithms specifically tailored to MALDI-TOF MS-based phenotype prediction is still in its infancy. Moreover, current spectral pre-processing typically involves a parameter-heavy chain of operations without analyzing their influence on the prediction results. In addition, classification algorithms lack quantification of uncertainty, which is indispensable for predictions potentially influencing patient treatment. Results We present a novel prediction method for antimicrobial resistance based on MALDI-TOF mass spectra. First, we compare the complex conventional pre-processing to a new approach that exploits topological information and requires only a single parameter, namely the number of peaks of a spectrum to keep. Second, we introduce PIKE, the peak information kernel, a similarity measure specifically tailored to MALDI-TOF mass spectra which, combined with a Gaussian process classifier, provides well-calibrated uncertainty estimates about predictions. We demonstrate the utility of our approach by predicting antibiotic resistance of three clinically highly relevant bacterial species. Our method consistently outperforms competitor approaches, while demonstrating improved performance and security by rejecting out-of-distribution samples, such as bacterial species that are not represented in the training data. Ultimately, our method could contribute to an earlier and precise antimicrobial treatment in clinical patient care. Availability and implementation We make our code publicly available as an easy-to-use Python package under https://github.com/BorgwardtLab/maldi_PIKE.

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Section: Methodsmentioning

confidence: 99%

Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra

et al. 2020

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“…(Statistics-sensitive Non-linear Iterative Peak-clipping algorithm, 100 iterations), intensity recalibration (total ion current), peak selection (MAD with half window size 8 and signalnoise ratio threshold 3), spectra alignment (quadratic warping function with 0.002 tolerance), averaging of technical replicates in main spectrum profiles (MSP), peak binning, and intensity matrix building, as recommended by Gibb & Strimmer [35].…”

Section: Plos Onementioning

confidence: 99%

Rapid diagnosis of periodontitis, a feasibility study using MALDI-TOF mass spectrometry

Antezack¹,

Chaudet²,

Tissot‐Dupont³

et al. 2020

PLoS ONE

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The aim of the present study was to assess the feasibility and diagnostic contribution of protein profiling using MALDI-TOF mass spectrometry applied to saliva, gingival crevicular fluid (GCF) and dental plaque from periodontitis and healthy subjects. We hypothesized that rapid routine and blinded MALDI-TOF analysis could accurately classify these three types of samples according to periodontal state. Materials and methods Unstimulated saliva, GCF and dental plaque, collected from periodontitis subjects and healthy controls, were analyzed by MALDI-TOF MS. Based on the differentially expressed peaks between the two groups, diagnostic decision trees were built for each sample. Results Among 141 patients (67 periodontitis and 74 healthy controls), the decision trees diagnosed periodontitis with a sensitivity = 70.3% (± 0.211) and a specificity = 77.8% (± 0.165) for saliva, a sensitivity = 79.6% (± 0.188) and a specificity = 75.7% (± 0.195) for GCF, and a sensitivity = 72.1% (± 0.202) and a specificity = 72.2% (± 0.195) for dental plaque. The sensitivity and specificity of the tests were improved to 100% (CI 95% = [0.91;1]) and 100% (CI 95% = [0.92;1]), respectively, when two samples were tested. Conclusion We developed, for the first time, diagnostic tests based on protein profiles of saliva, GCF and dental plaque between periodontitis patients and healthy subjects. When at least 2 of these samples were tested, the best results were obtained.

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“…Next, we performed TIC normalization, smoothing and baseline removal. Spectra were aligned to the stable peaks that are present in at least 80 % of all spectra [57]. Spectra, in which less than two stable peaks could be aligned, were removed.…”

Section: Case Studymentioning

confidence: 99%