MassIVE MSV000090220

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GNPS - 220827_JH_HowLow_GreenTea_smallDataset

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Description

Untargeted mass spectrometry metabolomics is an increasingly popular approach for characterizing complex mixtures. Recent studies have highlighted the impact of data pre-processing for determining the quality of metabolomics data analysis. The first step in data processing with untargeted metabolomics requires that signal thresholds be selected for which features (detected ions) are included in the dataset. Analysts face the challenge of knowing where to set these thresholds; setting them too high could mean missing relevant features but setting them too low could result in a complex and unwieldy dataset. This study compared data interpretation for an example metabolomics dataset when intensity thresholds were set at a range of feature heights. The main observations were that low signal thresh-olds 1) improved limit of detection, 2) increased the number of features detected with an associated isotope pattern and/or MS-MS fragmentation spectrum and 3) increased the number of in-source clusters and fragments detected for known analytes of interest. When the settings of parameters differing in intensities were applied on a set of 39 samples to discriminate the samples through principal component analyses (PCA), similar results were obtained with both low and high-intensity thresholds. We conclude that the most information-rich datasets can be obtained by setting low-intensity thresholds. However, in cases where only a qualitative comparison of samples with PCA is to be performed, it may be sufficient to set high thresholds and thereby reduce the complexity of the data processing and amount of computational time required. [doi:10.25345/C5QZ22N2R] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: Mass spectrometry metabolomics ; Pre-processing ; Feature height intensity cut-offs ; Natural products

Contact

Principal Investigators:
(in alphabetical order) 		Nadja B. Cech, University of North Carolina at Greensboro, United States of America
Submitting User: joelle79

Publications

Joelle Houriet, Warren S. Vidar, Preston K. Manwill, Daniel A. Todd, Nadja B. Cech.
How Low Can You Go? Selecting Intensity Thresholds for Untar-geted Metabolomics Data Pre-Processing.
Accepted, in preparation.

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GNPS content goes here (MSV000090220 [task=5c90b972cdfc4424a9756ecef74cc3cd])
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