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Abstract:
Ion mobility spectrometry with pre-separation by multi-capillary columns
(MCC/IMS) has become an established inexpensive, non-invasive bioanalytics
technology for detecting volatile organic compounds (VOCs) with various
metabolomics applications in medical research. To pave the way for this
technology towards daily usage in medical practice, different steps still have
to be taken. With respect to modern biomarker research, one of the most
important tasks is the automatic classification of patient-specific data sets
into different groups, healthy or not, for instance. Although sophisticated
machine learning methods exist, an inevitable preprocessing step is reliable
and robust peak detection without manual intervention. In this work we evaluate
four state-of-the-art approaches for automated IMS-based peak detection: local
maxima search, watershed transformation with IPHEx, region-merging with
VisualNow, and peak model estimation (PME).We manually generated Metabolites
2013, 3 278 a gold standard with the aid of a domain expert (manual) and
compare the performance of the four peak calling methods with respect to two
distinct criteria. We first utilize established machine learning methods and
systematically study their classification performance based on the four peak
detectors� results. Second, we investigate the classification variance and
robustness regarding perturbation and overfitting. Our main finding is that the
power of the classification accuracy is almost equally good for all methods,
the manually created gold standard as well as the four automatic peak finding
methods. In addition, we note that all tools, manual and automatic, are
similarly robust against perturbations. However, the classification performance
is more robust against overfitting when using the PME as peak calling
preprocessor. In summary, we conclude that all methods, though small
differences exist, are largely reliable and enable a wide spectrum of
real-world biomedical applications.
