1
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Zhang Z, Yu H, Wong-Ma E, Dokouhaki P, Mostafa A, Shavadia JS, Wu F, Huan T. Reducing Quantitative Uncertainty Caused by Data Processing in Untargeted Metabolomics. Anal Chem 2024; 96:3727-3732. [PMID: 38395621 DOI: 10.1021/acs.analchem.3c04046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
Processing liquid chromatography-mass spectrometry-based metabolomics data using computational programs often introduces additional quantitative uncertainty, termed computational variation in a previous work. This work develops a computational solution to automatically recognize metabolic features with computational variation in a metabolomics data set. This tool, AVIR (short for "Accurate eValuation of alIgnment and integRation"), is a support vector machine-based machine learning strategy (https://github.com/HuanLab/AVIR). The rationale is that metabolic features with computational variation have a poor correlation between chromatographic peak area and peak height-based quantifications across the samples in a study. AVIR was trained on a set of 696 manually curated metabolic features and achieved an accuracy of 94% in a 10-fold cross-validation. When tested on various external data sets from public metabolomics repositories, AVIR demonstrated an accuracy range of 84%-97%. Finally, tested on a large-scale metabolomics study, AVIR clearly indicated features with computational variation and thus guided us to manually correct them. Our results show that 75.3% of the samples with computational variation had a relative intensity difference of over 20% after correction. This demonstrates the critical role of AVIR in reducing computational variation to improve quantitative certainty in untargeted metabolomics analysis.
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Affiliation(s)
- Zixuan Zhang
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver V6T 1Z1, BC, Canada
| | - Huaxu Yu
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver V6T 1Z1, BC, Canada
| | - Ethan Wong-Ma
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver V6T 1Z1, BC, Canada
| | - Pouneh Dokouhaki
- Department of Pathology and Laboratory Medicine, University of Saskatchewan and Saskatchewan Health Authority, Saskatoon S7M 0Z9, SK, Canada
| | - Ahmed Mostafa
- Department of Pathology and Laboratory Medicine, University of Saskatchewan and Saskatchewan Health Authority, Saskatoon S7M 0Z9, SK, Canada
| | - Jay S Shavadia
- Division of Cardiology, College of Medicine, University of Saskatchewan, Saskatoon S7N 5E5, SK, Canada
| | - Fang Wu
- Department of Pathology and Laboratory Medicine, University of Saskatchewan and Saskatchewan Health Authority, Saskatoon S7M 0Z9, SK, Canada
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver V6T 1Z1, BC, Canada
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2
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Zulfiqar M, Crusoe MR, König-Ries B, Steinbeck C, Peters K, Gadelha L. Implementation of FAIR Practices in Computational Metabolomics Workflows-A Case Study. Metabolites 2024; 14:118. [PMID: 38393009 PMCID: PMC10891576 DOI: 10.3390/metabo14020118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for metabolomics workflows using the Metabolome Annotation Workflow (MAW) as a case study. MAW is specified using the Common Workflow Language (CWL), allowing for the subsequent execution of the workflow on different workflow engines. MAW is registered using a CWL description on WorkflowHub. During the submission process on WorkflowHub, a CWL description is used for packaging MAW using the Workflow RO-Crate profile, which includes metadata in Bioschemas. Researchers can use this narrative discussion as a guideline to commence using FAIR practices for their bioinformatics or cheminformatics workflows while incorporating necessary amendments specific to their research area.
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Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, 07743 Jena, Germany;
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany;
| | - Michael R. Crusoe
- ELIXIR (The European Life-Sciences Infrastructure for Biological Information) Germany, Institute of Bio- and Geosciences (IBG-5)—Computational Metagenomics, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany;
| | - Birgitta König-Ries
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany;
- Institute for Informatics, Friedrich Schiller University Jena, 07743 Jena, Germany
- iDiv—German Centre for Integrative Biodiversity Research, Halle-Jena-Leipzig, 04103 Leipzig, Germany;
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, 07743 Jena, Germany;
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany;
| | - Kristian Peters
- iDiv—German Centre for Integrative Biodiversity Research, Halle-Jena-Leipzig, 04103 Leipzig, Germany;
- Geobotany and Botanical Gardens, Martin-Luther University of Halle-Wittenberg, 06108 Halle, Germany
- Leibniz Institute of Plant Biochemistry, 06120 Halle, Germany
| | - Luiz Gadelha
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, 07743 Jena, Germany;
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany;
- Institute for Informatics, Friedrich Schiller University Jena, 07743 Jena, Germany
- German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
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3
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Zhao T, Wawryk NJP, Xing S, Low B, Li G, Yu H, Wang Y, Shen Q, Li XF, Huan T. ChloroDBPFinder: Machine Learning-Guided Recognition of Chlorinated Disinfection Byproducts from Nontargeted LC-HRMS Analysis. Anal Chem 2024. [PMID: 38294426 DOI: 10.1021/acs.analchem.3c05124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
High-resolution mass spectrometry (HRMS) is a prominent analytical tool that characterizes chlorinated disinfection byproducts (Cl-DBPs) in an unbiased manner. Due to the diversity of chemicals, complex background signals, and the inherent analytical fluctuations of HRMS, conventional isotopic pattern (37Cl/35Cl), mass defect, and direct molecular formula (MF) prediction are insufficient for accurate recognition of the diverse Cl-DBPs in real environmental samples. This work proposes a novel strategy to recognize Cl-containing chemicals based on machine learning. Our hierarchical machine learning framework has two random forest-based models: the first layer is a binary classifier to recognize Cl-containing chemicals, and the second layer is a multiclass classifier to annotate the number of Cl present. This model was trained using ∼1.4 million distinctive MFs from PubChem. Evaluated on over 14,000 unique MFs from NIST20, this machine learning model achieved 93.3% accuracy in recognizing Cl-containing MFs (Cl-MFs) and 92.9% accuracy in annotating the number of Cl for Cl-MFs. Furthermore, the trained model was integrated into ChloroDBPFinder, a standalone R package for the streamlined processing of LC-HRMS data and annotating both known and unknown Cl-containing compounds. Tested on existing Cl-DBP data sets related to aspartame chlorination in tap water, our ChloroDBPFinder efficiently extracted 159 Cl-containing DBP features and tentatively annotated the structures of 10 Cl-DBPs via molecular networking. In another application of a chlorinated humic substance, ChloroDBPFinder extracted 79 high-quality Cl-DBPs and tentatively annotated six compounds. In summary, our proposed machine learning strategy and the developed ChloroDBPFinder provide an advanced solution to identifying Cl-containing compounds in nontargeted analysis of water samples. It is freely available on GitHub (https://github.com/HuanLab/ChloroDBPFinder).
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Affiliation(s)
- Tingting Zhao
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada
| | - Nicholas J P Wawryk
- Division of Analytical and Environmental Toxicology, Department of Laboratory Medicine and Pathology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta T6G 2G3, Canada
| | - Shipei Xing
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada
| | - Brian Low
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada
| | - Gigi Li
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada
| | - Huaxu Yu
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada
| | - Yukai Wang
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada
| | - Qiming Shen
- Division of Analytical and Environmental Toxicology, Department of Laboratory Medicine and Pathology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta T6G 2G3, Canada
| | - Xing-Fang Li
- Division of Analytical and Environmental Toxicology, Department of Laboratory Medicine and Pathology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta T6G 2G3, Canada
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada
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4
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Fu T, Huan T, Rahman G, Zhi H, Xu Z, Oh TG, Guo J, Coulter S, Tripathi A, Martino C, McCarville JL, Zhu Q, Cayabyab F, Low B, He M, Xing S, Vargas F, Yu RT, Atkins A, Liddle C, Ayres J, Raffatellu M, Dorrestein PC, Downes M, Knight R, Evans RM. Paired microbiome and metabolome analyses associate bile acid changes with colorectal cancer progression. Cell Rep 2023; 42:112997. [PMID: 37611587 PMCID: PMC10903535 DOI: 10.1016/j.celrep.2023.112997] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 05/08/2023] [Accepted: 08/01/2023] [Indexed: 08/25/2023] Open
Abstract
Colorectal cancer (CRC) is driven by genomic alterations in concert with dietary influences, with the gut microbiome implicated as an effector in disease development and progression. While meta-analyses have provided mechanistic insight into patients with CRC, study heterogeneity has limited causal associations. Using multi-omics studies on genetically controlled cohorts of mice, we identify diet as the major driver of microbial and metabolomic differences, with reductions in α diversity and widespread changes in cecal metabolites seen in high-fat diet (HFD)-fed mice. In addition, non-classic amino acid conjugation of the bile acid cholic acid (AA-CA) increased with HFD. We show that AA-CAs impact intestinal stem cell growth and demonstrate that Ileibacterium valens and Ruminococcus gnavus are able to synthesize these AA-CAs. This multi-omics dataset implicates diet-induced shifts in the microbiome and the metabolome in disease progression and has potential utility in future diagnostic and therapeutic developments.
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Affiliation(s)
- Ting Fu
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Tao Huan
- Department of Chemistry, UBC Faculty of Science, Vancouver Campus, Vancouver, BC V6T 1Z4, Canada
| | - Gibraan Rahman
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Hui Zhi
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhenjiang Xu
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Tae Gyu Oh
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jian Guo
- Department of Chemistry, UBC Faculty of Science, Vancouver Campus, Vancouver, BC V6T 1Z4, Canada
| | - Sally Coulter
- Storr Liver Centre, Westmead Institute for Medical Research and Sydney Medical School, University of Sydney, Westmead, NSW 2145, Australia
| | - Anupriya Tripathi
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Cameron Martino
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Justin L McCarville
- Molecular and Systems Physiology Laboratory, Gene Expression Laboratory, NOMIS Center for Immunobiology and Microbial Pathogenesis, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Qiyun Zhu
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Fritz Cayabyab
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Brian Low
- Department of Chemistry, UBC Faculty of Science, Vancouver Campus, Vancouver, BC V6T 1Z4, Canada
| | - Mingxiao He
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Shipei Xing
- Department of Chemistry, UBC Faculty of Science, Vancouver Campus, Vancouver, BC V6T 1Z4, Canada
| | - Fernando Vargas
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ruth T Yu
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Annette Atkins
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Christopher Liddle
- Storr Liver Centre, Westmead Institute for Medical Research and Sydney Medical School, University of Sydney, Westmead, NSW 2145, Australia
| | - Janelle Ayres
- Molecular and Systems Physiology Laboratory, Gene Expression Laboratory, NOMIS Center for Immunobiology and Microbial Pathogenesis, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Manuela Raffatellu
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Chiba University-UC San Diego Center for Mucosal Immunity, Allergy, and Vaccines (CU-UCSD cMAV), La Jolla, CA 92093, USA
| | - Pieter C Dorrestein
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Department of Engineering, University of California, San Diego, La Jolla, CA 92093, USA; Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Michael Downes
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Rob Knight
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Department of Engineering, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Ronald M Evans
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
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5
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Quantitative challenges and their bioinformatic solutions in mass spectrometry-based metabolomics. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.117009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
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6
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Peralbo-Molina Á, Solà-Santos P, Perera-Lluna A, Chicano-Gálvez E. Data Processing and Analysis in Mass Spectrometry-Based Metabolomics. Methods Mol Biol 2023; 2571:207-239. [PMID: 36152164 DOI: 10.1007/978-1-0716-2699-3_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.
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Affiliation(s)
- Ángela Peralbo-Molina
- IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides, Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba (UCO), Córdoba, Spain.
| | - Pol Solà-Santos
- B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Alexandre Perera-Lluna
- B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Eduardo Chicano-Gálvez
- IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides, Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba (UCO), Córdoba, Spain
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7
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Guo J, Yu H, Xing S, Huan T. Addressing big data challenges in mass spectrometry-based metabolomics. Chem Commun (Camb) 2022; 58:9979-9990. [PMID: 35997016 DOI: 10.1039/d2cc03598g] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size. Therefore, in the "omics" era, we are faced with new challenges, the big data challenges of how to accurately and efficiently process the raw data, extract the biological information, and visualize the results from the gigantic amount of collected data. Although important, proposing solutions to address these big data challenges requires broad interdisciplinary knowledge, which can be challenging for many metabolomics practitioners. Our laboratory in the Department of Chemistry at the University of British Columbia is committed to combining analytical chemistry, computer science, and statistics to develop bioinformatics tools that address these big data challenges. In this Feature Article, we elaborate on the major big data challenges in metabolomics, including data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation. We also introduce our recently developed bioinformatics solutions for these challenges. Notably, all of the bioinformatics tools and source codes are freely available on GitHub (https://www.github.com/HuanLab), along with revised and regularly updated content.
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Affiliation(s)
- Jian Guo
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Huaxu Yu
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Shipei Xing
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Tao Huan
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
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8
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Maldonado-Reina A, López-Ruiz R, Romero-González R, Martínez Vidal JL, Garrido-Frenich A. Assessment of Co-Formulants in Marketed Plant Protection Products by LC-Q-Orbitrap-MS: Application of a Hybrid Data Treatment Strategy Combining Suspect Screening and Unknown Analysis. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:7302-7313. [PMID: 35670466 PMCID: PMC9204808 DOI: 10.1021/acs.jafc.2c01152] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/18/2022] [Accepted: 05/25/2022] [Indexed: 06/15/2023]
Abstract
The aim of this study was the determination of co-formulants in 15 different chlorantraniliprole- and difenoconazole-based plant protection products (PPPs) belonging to different formulations. Samples were analyzed by ultrahigh-performance liquid chromatography coupled to Q-Orbitrap high-resolution mass accuracy spectrometry (UHPLC-Q-Orbitrap-MS), operating in full-scan MS and data-dependent acquisition (ddMS2) modes. A total of 78 co-formulants were tentatively identified by a combination of suspect screening and unknown analysis. Nine of them were later confirmed by analytical standards. Finally, the analytical method was successfully validated and co-formulants were quantified. Linear alkyl ethoxylates (LAS) were the most common type of co-formulant, followed by sodium alkylbenzene sulfonates. Moreover, sodium dodecyl benzene sulfonate had the highest concentration of any co-formulant (up to 32.33 g/L). In all, an innovative identification of co-formulants in a large number of PPPs is presented, which will give room for future studies delving into the composition of PPPs or determining these co-formulants in environmental or agricultural samples.
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9
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Yu H, Huan T. MAFFIN: Metabolomics Sample Normalization Using Maximal Density Fold Change with High-Quality Metabolic Features and Corrected Signal Intensities. Bioinformatics 2022; 38:3429-3437. [PMID: 35639662 DOI: 10.1093/bioinformatics/btac355] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/18/2022] [Accepted: 05/19/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Post-acquisition sample normalization is a critical step in comparative metabolomics to remove the variation introduced by sample amount or concentration difference. Previously reported approaches are either specific to one sample type or built on strong assumptions on data structure, which are limited to certain levels. This encouraged us to develop MAFFIN, an accurate and robust post-acquisition sample normalization workflow that works universally for metabolomics data collected on mass spectrometry (MS) platforms. RESULTS MAFFIN calculates normalization factors using maximal density fold change (MDFC) computed by a kernel density-based approach. Using both simulated data and 20 metabolomics data sets, we showcased that MDFC outperforms four commonly used normalization methods in terms of reducing the intragroup variation among samples. Two essential steps, overlooked in conventional methods, were also examined and incorporated into MAFFIN. (1) MAFFIN uses multiple orthogonal criteria to select high-quality features for normalization factor calculation, which minimizes the bias caused by abiotic features or metabolites with poor quantitative performance. (2) MAFFIN corrects the MS signal intensities of high-quality features using serial quality control (QC) samples, which guarantees the accuracy of fold change calculations. MAFFIN was applied to a human saliva metabolomics study and led to better data separation in principal component analysis (PCA) and more confirmed significantly altered metabolites. AVAILABILITY AND IMPLEMENTATION The MAFFIN algorithm was implemented in an R package named MAFFIN. Package installation, user instruction, and demo data are available at https://github.com/HuanLab/MAFFIN. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Huaxu Yu
- Department of Chemistry, Faculty of Science, The University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
| | - Tao Huan
- Department of Chemistry, Faculty of Science, The University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
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10
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Fakouri Baygi S, Kumar Y, Barupal DK. IDSL.IPA Characterizes the Organic Chemical Space in Untargeted LC/HRMS Data Sets. J Proteome Res 2022; 21:1485-1494. [PMID: 35579321 DOI: 10.1021/acs.jproteome.2c00120] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Generating comprehensive and high-fidelity metabolomics data matrices from LC/HRMS data remains to be extremely challenging for population-scale large studies (n > 200). Here, we present a new data processing pipeline, the Intrinsic Peak Analysis (IDSL.IPA) R package (https://ipa.idsl.me), to generate such data matrices specifically for organic compounds. The IDSL.IPA pipeline incorporates (1) identifying potential 12C and 13C ion pairs in individual mass spectra; (2) detecting and characterizing chromatographic peaks using a new sensitive and versatile approach to perform mass correction, peak smoothing, baseline development for local noise measurement, and peak quality determination; (3) correcting retention time and cross-referencing peaks from multiple samples by a dynamic retention index marker approach; (4) annotating peaks using a reference database of m/z and retention time; and (5) accelerating data processing using a parallel computation of the peak detection and alignment steps for larger studies. This pipeline has been successfully evaluated for studies ranging from 200 to 1600 samples. By specifically isolating high quality and reliable signals pertaining to carbon-containing compounds in untargeted LC/HRMS data sets from larger studies, IDSL.IPA opens new opportunities for discovering new biological insights in the population-scale metabolomics and exposomics projects. The package is available in the R CRAN repository at https://cran.r-project.org/package=IDSL.IPA.
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Affiliation(s)
- Sadjad Fakouri Baygi
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Yashwant Kumar
- Non-communicable Diseases Division, Translational Health Science and Technology Institute, Faridabad, Haryana 121001, India
| | - Dinesh Kumar Barupal
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
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11
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Eilertz D, Mitterer M, Buescher JM. automRm: An R Package for Fully Automatic LC-QQQ-MS Data Preprocessing Powered by Machine Learning. Anal Chem 2022; 94:6163-6171. [PMID: 35412809 PMCID: PMC9047440 DOI: 10.1021/acs.analchem.1c05224] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Preprocessing of liquid chromatography-mass spectrometry (LC-MS) raw data facilitates downstream statistical and biological data analyses. In the case of targeted LC-MS data, consistent recognition of chromatographic peaks is a main challenge, in particular, for low abundant signals. Fully automatic preprocessing is faster than manual peak review and does not depend on the individual operator. Here, we present the R package automRm for fully automatic preprocessing of LC-MS data recorded in MRM mode. Using machine learning (ML) for detection of chromatographic peaks and quality control of reported results enables the automatic recognition of complex patterns in raw data. In addition, this approach renders automRm generally applicable to a wide range of analytical methods including hydrophilic interaction liquid chromatography (HILIC), which is known for sample-to-sample variations in peak shape and retention time. We demonstrate the impact of the choice of training data set, of the applied ML algorithm, and of individual peak characteristics on automRm's ability to correctly report chromatographic peaks. Next, we show that automRm can replicate results obtained by manual peak review on published data. Moreover, automRm outperforms alternative software solutions regarding the variation in peak integration among replicate measurements and the number of correctly reported peaks when applied to a HILIC-MS data set. The R package is freely available from gitlab (https://gitlab.gwdg.de/joerg.buescher/automrm).
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Affiliation(s)
- Daniel Eilertz
- Metabolomics Core Facility, Max Planck Institute of Immunobiology and Epigenetics, Stübeweg 51, 79108 Freiburg, Germany
| | - Michael Mitterer
- Metabolomics Core Facility, Max Planck Institute of Immunobiology and Epigenetics, Stübeweg 51, 79108 Freiburg, Germany
| | - Joerg M Buescher
- Metabolomics Core Facility, Max Planck Institute of Immunobiology and Epigenetics, Stübeweg 51, 79108 Freiburg, Germany
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