Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data—Reviewing the State of the Art

Hauschild, Anne-Christin; Schneider, Till; Pauling, Josch; Rupp, Kathrin; Jang, Mi Jin; Baumbach, Jan

doi:10.3390/metabo2040733

Cited by 32 publications

(18 citation statements)

References 73 publications

(96 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recent work on preprocessing strategies for MCC-IMS data sets only includes the visual inspection of preprocessed data and the evaluation of differences in intensity of the referenced features of the studied spectra. 5,8 Outlook. The developed strategy can be used to analyze other large two-dimensional data sets.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Data Size Reduction Strategy for the Classification of Breath and Air Samples Using Multicapillary Column-Ion Mobility Spectrometry

et al. 2015

View full text Add to dashboard Cite

Ion mobility spectrometry combined with multicapillary column separation (MCC-IMS) is a wellknown technology for detecting volatile organic compounds (VOCs) in gaseous samples. Due to their large data size, processing of MCC-IMS spectra is still the main bottleneck of data analysis, and there is an increasing need for data analysis strategies in which the size of MCC-IMS data is reduced to enable further analysis. In our study, the first untargeted chemometric strategy is developed and employed in the analysis of MCC-IMS spectra from 264 breath and ambient air samples. This strategy does not comprise identification of compounds as a primary step but includes several preprocessing steps and a discriminant analysis. Data size is significantly reduced in three steps. Wavelet transform, mask construction, and sparse-partial least squares-discriminant analysis (s-PLS-DA) allow data size reduction with down to 50 variables relevant to the goal of analysis. The influence and compatibility of the data reduction tools are studied by applying different settings of the developed strategy. Loss of information after preprocessing is evaluated, e.g., by comparing the performance of classification models for different classes of samples. Finally, the interpretability of the classification models is evaluated, and regions of spectra that are related to the identification of potential analytical biomarkers are successfully determined. This work will greatly enable the standardization of analytical procedures across different instrumentation types promoting the adoption of MCC-IMS technology in a wide range of diverse application fields.

show abstract

Section: ■ Results and Discussionmentioning

confidence: 99%

Data Size Reduction Strategy for the Classification of Breath and Air Samples Using Multicapillary Column-Ion Mobility Spectrometry

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Below, a short description of these techniques is provided. They are described in more detail by Smolinska et al 67 and Hauschild et al 68 The merged peak cluster localization is present in the commercial software package Visual Now (B&S Analytik, Dortmund, Germany). MPCL is based on a procedure introduced by Bader et al, 69 in which points of the IMS chromatogram are firstly clustered with k-means clustering with Euclidean distance and then merged following a concept for image segmentation.…”

Section: Targeted Analyte Analysis Techniquesmentioning

confidence: 99%

Chemometrics for ion mobility spectrometry data: recent advances and future prospects

Szymańska

Davies

Buydens

2016

Analyst

View full text Add to dashboard Cite

Historically, advances in the field of ion mobility spectrometry have been hindered by the variation in measured signals between instruments developed by different research laboratories or manufacturers. This has triggered the development and application of chemometric techniques able to reveal and analyze precious information content of ion mobility spectra. Recent advances in multidimensional coupling of ion mobility spectrometry to chromatography and mass spectrometry has created new, unique challenges for data processing, yielding high-dimensional, megavariate datasets. In this paper, a complete overview of available chemometric techniques used in the analysis of ion mobility spectrometry data is given. We describe the current state-of-the-art of ion mobility spectrometry data analysis comprising datasets with different complexities and two different scopes of data analysis, i.e. targeted and non-targeted analyte analyses. Two main steps of data analysis are considered: data preprocessing and pattern recognition. A detailed description of recent advances in chemometric techniques is provided for these steps, together with a list of interesting applications. We demonstrate that chemometric techniques have a significant contribution to the recent and great expansion of ion mobility spectrometry technology into different application fields. We conclude that well-thought out, comprehensive data analysis strategies are currently emerging, including several chemometric techniques and addressing different data challenges. In our opinion, this trend will continue in the near future, stimulating developments in ion mobility spectrometry instrumentation even further.

show abstract

“…Monitoring can be done remotely and continuously in real time, beyond office visits, with patients staying at home [18,25,70]. Emergencies can be detected early, like with breath pillows for children or Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) that can give immediate information about the human health status or infection threats [47]. In the case of chronic illnesses, practitioners get early warning of conditions that would lead to unplanned hospitalizations and expensive emergency care [25,42,45,57].…”

Section: Healthcare and Pharmamentioning

confidence: 99%

The Internet of Things

Stolpe

2016

SIGKDD Explor. Newsl.

View full text Add to dashboard Cite

Nowadays, data is created by humans as well as automatically collected by physical things, which embed electronics, software, sensors and network connectivity. Together, these entities constitute the Internet of Things (IoT). The automated analysis of its data can provide insights into previously unknown relationships between things, their environment and their users, facilitating an optimization of their behavior. Especially the real-time analysis of data, embedded into physical systems, can enable new forms of autonomous control. These in turn may lead to more sustainable applications, reducing waste and saving resources. IoT's distributed and dynamic nature, resource constraints of sensors and embedded devices as well as the amounts of generated data are challenging even the most advanced automated data analysis methods known today. In particular, the IoT requires a new generation of distributed analysis methods. Many existing surveys have strongly focused on the centralization of data in the cloud and big data analysis, which follows the paradigm of parallel high-performance computing. However, bandwidth and energy can be too limited for the transmission of raw data, or it is prohibited due to privacy constraints. Such communication-constrained scenarios require decentralized analysis algorithms which at least partly work directly on the generating devices. After listing data-driven IoT applications, in contrast to existing surveys, we highlight the differences between cloudbased and decentralized analysis from an algorithmic perspective. We present the opportunities and challenges of research on communication-efficient decentralized analysis algorithms. Here, the focus is on the difficult scenario of vertically partitioned data, which covers common IoT use cases. The comprehensive bibliography aims at providing readers with a good starting point for their own work.

show abstract

Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data—Reviewing the State of the Art

Cited by 32 publications

References 73 publications

Data Size Reduction Strategy for the Classification of Breath and Air Samples Using Multicapillary Column-Ion Mobility Spectrometry

Data Size Reduction Strategy for the Classification of Breath and Air Samples Using Multicapillary Column-Ion Mobility Spectrometry

Chemometrics for ion mobility spectrometry data: recent advances and future prospects

The Internet of Things

Contact Info

Product

Resources

About