Summary of study ST001493

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, where it has been assigned Project ID PR001011. The data can be accessed directly via it's Project DOI: 10.21228/M8111X This work is supported by NIH grant, U2C- DK119886.


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Study IDST001493
Study TitleDynamic binning peak detection and assessment of various lipidomics liquid chromatography-mass spectrometry pre-processing platforms
Study SummaryLiquid chromatography-mass spectrometry (LC-MS) based lipidomics generate a large dataset, which requires high-performance data pre-processing tools for their interpretation such as XCMS, mzMine and Progenesis. These pre-processing tools rely heavily on accurate peak detection, which depends on setting the peak detection mass tolerance (PDMT) properly. The PDMT is usually set with a fixed value in either ppm or Da units. However, this fixed value may result in duplicates or missed peak detection. Therefore, we developed the dynamic binning method for accurate peak detection, which takes into account the peak broadening described by well-known physics laws of ion separation and set dynamically the value of PDMT as a function of m/z. Namely, in our method, the PDMT is proportional to for FTICR, to for Orbitrap, to m/z for Q-TOF and is a constant for Quadrupole mass analyzer, respectively. The dynamic binning method was implemented in XCMS. Our further goal was to compare the performance of different lipidomics pre-processing tools to find differential compounds. We have generated set samples with 43 lipids internal standards differentially spiked to aliquots of one human plasma lipid sample using Orbitrap LC-MS/MS. The performance of the various pipelines using aligned parameter sets was quantified by a quality score system which reflects the ability of a pre-processing pipeline to detect differential peaks spiked at various concentration levels. The quality score indicates that the dynamic binning method improves the performance of XCMS (maximum p-value 9.8·10-3 of two-sample Wilcoxon test). The modified XCMS software was further compared with mzMine and Progenesis. The results showed that modified XCMS and Progenesis had a similarly good performance in the aspect of finding differential compounds. In addition, Progenesis shows lower variability as indicated by lower CVs, followed by XCMS and mzMine. The lower variability of Progenesis improve the quantification, however, provide an incorrect quantification abundance order of spiked-in internal standards.
University of Groningen
Last NamePéter
First NameHorvatovich
AddressAntonius Deusinglaan 1, 9713 AV Groningen, The Netherlands
Phone+31 (0)50 363 3341
Submit Date2020-09-25
Num Groups6
Total Subjects1
Study CommentsDifferent concentrations of lipid standard mixture were added to the plasma lipid extract aliquots
PublicationsUnder review
Raw Data AvailableYes
Raw Data File Type(s).mzML
Analysis Type DetailOther
Release Date2020-10-13
Release Version1
Horvatovich Péter Horvatovich Péter application/zip

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Project ID:PR001011
Project DOI:doi: 10.21228/M8111X
Project Title:Dynamic binning
Project Type:MS analysis
Project Summary:Using dynamic binning theory to improve the peak detection in LC-MS based lipidomics
Institute:University of Groningen
Department:Department of Analytical Biochemistry
Last Name:Horvatovich
First Name:Péter
Address:Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands.
Phone:+31 (0)50 363 3341
Funding Source:China Scholarship Council grant No. 201708500094. This research was part of the Netherlands X-omics Initiative and partially funded by NWO, project 184.034.019.
Publications:Under review