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Metabolites
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Development and Application of Statistical Methods for Analyzing Metabolomics Data...
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Development and Application of Statistical Methods for Analyzing Metabolomics Data Volume 2

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 3334

Special Issue Editor

Dr. Jos Hageman

E-Mail Website
Guest Editor
Biometris, Wageningen University and Research Centre, 6708 PB Wageningen, The Netherlands
Interests: metabolomics analytics; data mining statistics; food science; data science; data Analysis; data visualization; exploratory data analysis; computational statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, the field of metabolomics has developed tremendously. It is now possible to routinely measure a wide range of metabolites for many specimens at reduced cost. This has provided opportunities for exciting experiments such as time-resolved metabolomics, multi-sample and multi-species metabolomics, or cross-omics experiments, among others. Data analysis is a crucial step for extraction of meaningful information from the complex data acquired in this manner. As a result, the rapid developments in powerful metabolomics experiments must be matched with developments in statistical methodology for experimental analysis.  

This Special Issue is dedicated to the development or application of statistical methods for analyzing metabolomics data. We invite researchers to submit their manuscripts outlining novel data processing and data analysis methods for metabolomics. The scope of this Special Issue is not limited to this topic, but also includes experimental design, data acquisition methods, and applied metabolomics studies in which data analysis played an especially attractive role.

Dr. Jos Hageman
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • univariate/multivariate statistics
  • chemometrics
  • data analysis
  • experimental design

Published Papers (2 papers)

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Research

11 pages, 2176 KiB  
Article
Metabolic Profiling Early Post-Allogeneic Haematopoietic Cell Transplantation in the Context of CMV Infection
by Kirstine K. Rasmussen, Quenia dos Santos, Cameron Ross MacPherson, Adrian G. Zucco, Lars Klingen Gjærde, Emma E. Ilett, Isabelle Lodding, Marie Helleberg, Jens D. Lundgren, Susanne D. Nielsen, Susanne Brix, Henrik Sengeløv and Daniel D. Murray
Metabolites 2023, 13(9), 968; https://doi.org/10.3390/metabo13090968 - 22 Aug 2023
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Abstract
Immune dysfunction resulting from allogeneic haematopoietic stem cell transplantation (aHSCT) predisposes one to an elevated risk of cytomegalovirus (CMV) infection. Changes in metabolism have been associated with adverse outcomes, and in this study, we explored the associations between metabolic profiles and post-transplantation CMV [...] Read more.
Immune dysfunction resulting from allogeneic haematopoietic stem cell transplantation (aHSCT) predisposes one to an elevated risk of cytomegalovirus (CMV) infection. Changes in metabolism have been associated with adverse outcomes, and in this study, we explored the associations between metabolic profiles and post-transplantation CMV infection using plasma samples collected 7–33 days after aHSCT. We included 68 aHSCT recipients from Rigshospitalet, Denmark, 50% of whom experienced CMV infection between days 34–100 post-transplantation. First, we investigated whether 12 metabolites selected based on the literature were associated with an increased risk of post-transplantation CMV infection. Second, we conducted an exploratory network-based analysis of the complete metabolic and lipidomic profiles in relation to clinical phenotypes and biological pathways. Lower levels of trimethylamine N-oxide were associated with subsequent CMV infection (multivariable logistic regression: OR = 0.63; 95% CI = [0.41; 0.87]; p = 0.01). Explorative analysis revealed 12 clusters of metabolites or lipids, among which one was predictive of CMV infection, and the others were associated with conditioning regimens, age upon aHSCT, CMV serostatus, and/or sex. Our results provide evidence for an association between the metabolome and CMV infection post-aHSCT that is independent of known risk factors. Full article
(This article belongs to the Special Issue Development and Application of Statistical Methods for Analyzing Metabolomics Data Volume 2)
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15 pages, 1280 KiB  
Article
Bucket Fuser: Statistical Signal Extraction for 1D 1H NMR Metabolomic Data
by Michael Altenbuchinger, Henry Berndt, Robin Kosch, Iris Lang, Jürgen Dönitz, Peter J. Oefner, Wolfram Gronwald, Helena U. Zacharias and Investigators GCKD Study
Metabolites 2022, 12(9), 812; https://doi.org/10.3390/metabo12090812 - 29 Aug 2022
Viewed by 1819
Abstract
Untargeted metabolomics is a promising tool for identifying novel disease biomarkers and unraveling underlying pathomechanisms. Nuclear magnetic resonance (NMR) spectroscopy is particularly suited for large-scale untargeted metabolomics studies due to its high reproducibility and cost effectiveness. Here, one-dimensional (1D) 1H NMR experiments [...] Read more.
Untargeted metabolomics is a promising tool for identifying novel disease biomarkers and unraveling underlying pathomechanisms. Nuclear magnetic resonance (NMR) spectroscopy is particularly suited for large-scale untargeted metabolomics studies due to its high reproducibility and cost effectiveness. Here, one-dimensional (1D) 1H NMR experiments offer good sensitivity at reasonable measurement times. Their subsequent data analysis requires sophisticated data preprocessing steps, including the extraction of NMR features corresponding to specific metabolites. We developed a novel 1D NMR feature extraction procedure, called Bucket Fuser (BF), which is based on a regularized regression framework with fused group LASSO terms. The performance of the BF procedure was demonstrated using three independent NMR datasets and was benchmarked against existing state-of-the-art NMR feature extraction methods. BF dynamically constructs NMR metabolite features, the widths of which can be adjusted via a regularization parameter. BF consistently improved metabolite signal extraction, as demonstrated by our correlation analyses with absolutely quantified metabolites. It also yielded a higher proportion of statistically significant metabolite features in our differential metabolite analyses. The BF algorithm is computationally efficient and it can deal with small sample sizes. In summary, the Bucket Fuser algorithm, which is available as a supplementary python code, facilitates the fast and dynamic extraction of 1D NMR signals for the improved detection of metabolic biomarkers. Full article
(This article belongs to the Special Issue Development and Application of Statistical Methods for Analyzing Metabolomics Data Volume 2)
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