metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics
Liquid chromatography–high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from com...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/14/2/125 |
| _version_ | 1797297494316548096 |
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| author | Hani Habra Jennifer L. Meijer Tong Shen Oliver Fiehn David A. Gaul Facundo M. Fernández Kaitlin R. Rempfert Thomas O. Metz Karen E. Peterson Charles R. Evans Alla Karnovsky |
| author_facet | Hani Habra Jennifer L. Meijer Tong Shen Oliver Fiehn David A. Gaul Facundo M. Fernández Kaitlin R. Rempfert Thomas O. Metz Karen E. Peterson Charles R. Evans Alla Karnovsky |
| author_sort | Hani Habra |
| collection | DOAJ |
| description | Liquid chromatography–high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a “primary” feature list is used as a template for matching compounds in “target” feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution’s in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App. |
| first_indexed | 2024-03-07T22:22:03Z |
| format | Article |
| id | doaj.art-ab4466185f434c298b44e5983418504f |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-03-07T22:22:03Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-ab4466185f434c298b44e5983418504f2024-02-23T15:27:07ZengMDPI AGMetabolites2218-19892024-02-0114212510.3390/metabo14020125metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS MetabolomicsHani Habra0Jennifer L. Meijer1Tong Shen2Oliver Fiehn3David A. Gaul4Facundo M. Fernández5Kaitlin R. Rempfert6Thomas O. Metz7Karen E. Peterson8Charles R. Evans9Alla Karnovsky10Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USADepartment of Medicine, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USAWest Coast Metabolomics Center, University of California, Davis, CA 95616, USAWest Coast Metabolomics Center, University of California, Davis, CA 95616, USASchool of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive, Atlanta, GA 30332, USASchool of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive, Atlanta, GA 30332, USABiological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USABiological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USADepartment of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI 48109, USADepartment of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USADepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USALiquid chromatography–high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a “primary” feature list is used as a template for matching compounds in “target” feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution’s in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.https://www.mdpi.com/2218-1989/14/2/125metabolomicsLC-MSalignmentchromatographyR packagesoftware |
| spellingShingle | Hani Habra Jennifer L. Meijer Tong Shen Oliver Fiehn David A. Gaul Facundo M. Fernández Kaitlin R. Rempfert Thomas O. Metz Karen E. Peterson Charles R. Evans Alla Karnovsky metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics Metabolites metabolomics LC-MS alignment chromatography R package software |
| title | metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics |
| title_full | metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics |
| title_fullStr | metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics |
| title_full_unstemmed | metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics |
| title_short | metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics |
| title_sort | metabcombiner 2 0 disparate multi dataset feature alignment for lc ms metabolomics |
| topic | metabolomics LC-MS alignment chromatography R package software |
| url | https://www.mdpi.com/2218-1989/14/2/125 |
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