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From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome

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Abstract

The goal of lipidomic studies is to provide a broad characterization of cellular lipids present and changing in a sample of interest. Recent lipidomic research has significantly contributed to revealing the multifaceted roles that lipids play in fundamental cellular processes, including signaling, energy storage, and structural support. Furthermore, these findings have shed light on how lipids dynamically respond to various perturbations. Continued advancement in analytical techniques has also led to improved abilities to detect and identify novel lipid species, resulting in increasingly large datasets. Statistical analysis of these datasets can be challenging not only because of their vast size, but also because of the highly correlated data structure that exists due to many lipids belonging to the same metabolic or regulatory pathways. Interpretation of these lipidomic datasets is also hindered by a lack of current biological knowledge for the individual lipids. These limitations can therefore make lipidomic data analysis a daunting task. To address these difficulties and shed light on opportunities and also weaknesses in current tools, we have assembled this review. Here, we illustrate common statistical approaches for finding patterns in lipidomic datasets, including univariate hypothesis testing, unsupervised clustering, supervised classification modeling, and deep learning approaches. We then describe various bioinformatic tools often used to biologically contextualize results of interest. Overall, this review provides a framework for guiding lipidomic data analysis to promote a greater assessment of lipidomic results, while understanding potential advantages and weaknesses along the way.

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References

  1. Fahy E, Cotter D, Sud M, Subramaniam S. Lipid classification, structures and tools. Biochim Biophys Acta (BBA) - Mol Cell Biol Lipids. 2011;1811(11):637–47.

    CAS  Google Scholar 

  2. Wymann MP, Schneiter R. Lipid signalling in disease. Nat Rev Mol Cell Biol. 2008;9(2):162–76.

    Article  CAS  PubMed  Google Scholar 

  3. Carotti S, Aquilano K, Valentini F, Ruggiero S, Alletto F, Morini S, et al. An overview of deregulated lipid metabolism in nonalcoholic fatty liver disease with special focus on lysosomal acid lipase. Am J Physiol Gastrointest Liver Physiol. 2020;319(4):G469–80.

    Article  CAS  PubMed  Google Scholar 

  4. Bhargava S, De La Puente-Secades S, Schurgers L, Jankowski J. Lipids and lipoproteins in cardiovascular diseases: a classification. Trends Endocrinol Metab. 2022;33(6):409–23.

    Article  CAS  PubMed  Google Scholar 

  5. Eid S, Sas KM, Abcouwer SF, Feldman EL, Gardner TW, Pennathur S, et al. New insights into the mechanisms of diabetic complications: role of lipids and lipid metabolism. Diabetologia. 2019;62(9):1539–49.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Perry RJ, Samuel VT, Petersen KF, Shulman GI. The role of hepatic lipids in hepatic insulin resistance and type 2 diabetes. Nature. 2014;510(7503):84–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Luo X, Cheng C, Tan Z, Li N, Tang M, Yang L, et al. Emerging roles of lipid metabolism in cancer metastasis. Mol Cancer. 2017;16(1).

  8. Yadav RS, Tiwari NK. Lipid Integration in Neurodegeneration: An Overview of Alzheimer’s Disease. Mol Neurobiol. 2014;50(1):168–76.

    Article  CAS  PubMed  Google Scholar 

  9. Maradonna F, Carnevali O. Lipid Metabolism Alteration by Endocrine Disruptors in Animal Models: An Overview. Front Endocrinol (Lausanne). 2018;9:654.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Fahy E, Sud M, Cotter D, Subramaniam S. LIPID MAPS online tools for lipid research. Nucleic Acids Res. 2007;35(Web Server issue):W606-12.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Olshansky G, Giles C, Salim A, Meikle PJ. Challenges and opportunities for prevention and removal of unwanted variation in lipidomic studies. Prog Lipid Res. 2022;87:101177.

    Article  CAS  PubMed  Google Scholar 

  12. Xu T, Hu C, Xuan Q, Xu G. Recent advances in analytical strategies for mass spectrometry-based lipidomics. Anal Chim Acta. 2020;1137:156–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ni Z, Wölk M, Jukes G, Mendivelso Espinosa K, Ahrends R, Aimo L, et al. Guiding the choice of informatics software and tools for lipidomics research applications. Nat Methods. 2023;20(2):193–204.

    Article  CAS  PubMed  Google Scholar 

  14. Giles C, Takechi R, Lam V, Dhaliwal SS, Mamo JCL. Contemporary lipidomic analytics: opportunities and pitfalls. Prog Lipid Res. 2018;71:86–100.

    Article  CAS  PubMed  Google Scholar 

  15. Rubingh CM, Bijlsma S, Derks EPPA, Bobeldijk I, Verheij ER, Kochhar S, et al. Assessing the performance of statistical validation tools for megavariate metabolomics data. Metabolomics. 2006;2(2):53–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Floudas CA, Gounaris CE. A review of recent advances in global optimization. J Global Optim. 2009;45(1):3–38.

    Article  MathSciNet  Google Scholar 

  17. Wong G, Chan J, Kingwell BA, Leckie C, Meikle PJ. LICRE: unsupervised feature correlation reduction for lipidomics. Bioinformatics. 2014;30(19):2832–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Perez-Melo S, Kibria BMG. On Some Test Statistics for Testing the Regression Coefficients in Presence of Multicollinearity: A Simulation Study. Stats. 2020;3(1):40–55.

    Article  Google Scholar 

  19. Kanehisa M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004;32(90001):277D – 80.

    Article  Google Scholar 

  20. Sun J, **a Y. Pretreating and normalizing metabolomics data for statistical analysis. Genes Dis. 2023.

  21. Saccenti E, Hoefsloot HCJ, Smilde AK, Westerhuis JA, Hendriks MMWB. Reflections on univariate and multivariate analysis of metabolomics data. Metabolomics. 2014;10(3):361–74.

    Article  CAS  Google Scholar 

  22. Vinaixa M, Samino S, Saez I, Duran J, Guinovart JJ, Yanes O. A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data. Metabolites. 2012;2(4):775–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hines KM, Xu L. Lipidomic consequences of phospholipid synthesis defects in Escherichia coli revealed by HILIC-ion mobility-mass spectrometry. Chem Phys Lipids. 2019;219:15–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bifarin OO, Sah S, Gaul DA, Moore SG, Chen R, Palaniappan M, et al. Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer. J Proteome Res. 2023;22(6):2092–108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Vaz FM, Pras-Raves M, Bootsma AH, Van Kampen AHC. Principles and practice of lipidomics. J Inherit Metab Dis. 2015;38(1):41–52.

    Article  CAS  PubMed  Google Scholar 

  26. Peterson SJ, Foley S. Clinician’s Guide to Understanding Effect Size, Alpha Level, Power, and Sample Size. Nutr Clin Pract. 2021;36(3):598–605.

    Article  PubMed  Google Scholar 

  27. Fay MP, Proschan MA. Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Stat Surv. 2010;4(none):1–39.

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  28. Kujala M, Nevalainen J. A case study of normalization, missing data and variable selection methods in lipidomics. Stat Med. 2015;34(1):59–73.

    Article  MathSciNet  CAS  PubMed  Google Scholar 

  29. Ghasemi A, Zahediasl S. Normality Tests for Statistical Analysis: A Guide for Non-Statisticians. Int J Endocrinol Metab. 2012;10(2):486–9.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Zhou Y, Zhu Y, Wong WK. Statistical tests for homogeneity of variance for clinical trials and recommendations. Contemp Clin Trials Commun. 2023;33:101119.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Forstmeier W, Wagenmakers EJ, Parker TH. Detecting and avoiding likely false-positive findings – a practical guide. Biol Rev. 2017;92(4):1941–68.

    Article  PubMed  Google Scholar 

  32. Noble WS. How does multiple testing correction work? Nat Biotechnol. 2009;27(12):1135–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Simonsohn U, Nelson LD, Simmons JP. p-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results. Perspect Psychol Sci. 2014;9(6):666–81.

    Article  PubMed  Google Scholar 

  34. Alloghani M, Al-Jumeily D, Mustafina J, Hussain A, Aljaaf AJ. A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science. In: Berry M, Mohamed A, Yap B, editors. Supervised and Unsupervised Learning for Data Science. Springer, Cham; 2020. pp. 3–21.

  35. Bro R, Smilde AK. Principal component analysis. Anal Methods. 2014;6(9):2812–31.

    Article  CAS  Google Scholar 

  36. Wu Z, Bagarolo GI, Thoroe-Boveleth S, Jankowski J. “Lipidomics”: Mass spectrometric and chemometric analyses of lipids. Adv Drug Deliv Rev. 2020;159:294–307.

    Article  CAS  PubMed  Google Scholar 

  37. Shen Q, Wang Y, Gong L, Guo R, Dong W, Cheung H-Y. Shotgun Lipidomics Strategy for Fast Analysis of Phospholipids in Fisheries Waste and Its Potential in Species Differentiation. J Agric Food Chem. 2012;60(37):9384–93.

    Article  CAS  PubMed  Google Scholar 

  38. Hancock SE, Ding E, Johansson Beves E, Mitchell T, Turner N. FACS-assisted single-cell lipidome analysis of phosphatidylcholines and sphingomyelins in cells of different lineages. J Lipid Res. 2023;64(3):100341.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Da Costa E, Domingues P, Melo T, Coelho E, Pereira R, Calado R, et al. Lipidomic Signatures Reveal Seasonal Shifts on the Relative Abundance of High-Valued Lipids from the Brown Algae Fucus vesiculosus. Mar Drugs. 2019;17(6):335.

    Article  PubMed  PubMed Central  Google Scholar 

  40. van der Maaten LH, Geoffrey. Viualizing data using t-SNE. J Mach Learn Res. 2008;2008(9):2579–605.

    Google Scholar 

  41. Wang Z, Zhang Y, Tian R, Luo Z, Zhang R, Li X, et al. Data-Driven Deciphering of Latent Lesions in Heterogeneous Tissue Using Function-Directed <i>t</i>-SNE of Mass Spectrometry Imaging Data. Anal Chem. 2022;94(40):13927–35.

    Article  CAS  PubMed  Google Scholar 

  42. Niemela PS, Castillo S, Sysi-Aho M, Oresic M. Bioinformatics and computational methods for lipidomics. J Chromatogr B Analyt Technol Biomed Life Sci. 2009;877(26):2855–62.

    Article  PubMed  Google Scholar 

  43. Day WHE, Edelsbrunner H. Efficient algorithms for agglomerative hierarchical clustering methods. J Classif. 1984;1(1):7–24.

    Article  Google Scholar 

  44. Ran X, ** Y, Lu Y, Wang X, Lu Z. Comprehensive survey on hierarchical clustering algorithms and the recent developments. Artif Intell Rev. 2023;56(8):8219–64.

    Article  Google Scholar 

  45. Jiang T, Gradus JL, Rosellini AJ. Supervised Machine Learning: A Brief Primer. Behav Ther. 2020;51(5):675–87.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Dilhara M, Ketkar A, Dig D. Understanding Software-2.0. ACM Trans Softw Eng Methodol. 2021;30(4):1–42.

    Article  Google Scholar 

  47. Zhou J, Zhong L. Applications of liquid chromatography-mass spectrometry based metabolomics in predictive and personalized medicine. Front Mol Biosci. 2022;9:1049016.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Mosteller F, Tukey JW. Data analysis and regression: A second course in statistics. Addison-Wesley Publishing Company; 1977.

  49. Maleki F, Muthukrishnan N, Ovens K, Reinhold C, Forghani R. Machine Learning Algorithm Validation. Neuroimaging Clin North Am. 2020;30(4):433–45.

    Article  Google Scholar 

  50. Uddin S, Khan A, Hossain ME, Moni MA. Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Making. 2019;19(281).

  51. Lee LC, Liong C-Y, Jemain AA. Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps. Analyst. 2018;143(15):3526–39.

    Article  CAS  PubMed  Google Scholar 

  52. Maly M, Hajsl M, Bechynska K, Kucerka O, Sramek M, Suttnar J, et al. Lipidomic Analysis to Assess Oxidative Stress in Acute Coronary Syndrome and Acute Stroke Patients. Metabolites. 2021;11(7):412.

  53. Mi S, Shang K, Li X, Zhang C-H, Liu J-Q, Huang D-Q. Characterization and discrimination of selected China’s domestic pork using an LC-MS-based lipidomics approach. Food Control. 2019;100:305–14.

    Article  CAS  Google Scholar 

  54. Emmert-Streib F, Dehmer M. High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection. Mach Learn Knowl Extraction. 2019;1(1):359–83.

    Article  Google Scholar 

  55. Santoro AL, Drummond RD, Silva IT, Ferreira SS, Juliano L, Vendramini PH, et al. In Situ DESI-MSI Lipidomic Profiles of Breast Cancer Molecular Subtypes and Precursor Lesions. Cancer Res. 2020;80(6):1246–57.

    Article  CAS  PubMed  Google Scholar 

  56. Brereton RG, Lloyd GR. Support Vector Machines for classification and regression. Analyst. 2010;135(2):230–67.

    Article  CAS  PubMed  Google Scholar 

  57. Huang H, Ye G, Lai SQ, Zou HX, Yuan B, Wu QC, et al. Plasma Lipidomics Identifies Unique Lipid Signatures and Potential Biomarkers for Patients With Aortic Dissection. Front Cardiovasc Med. 2021;8:757022.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Biau G, Scornet E. A random forest guided tour. TEST. 2016;25(2):197–227.

    Article  MathSciNet  Google Scholar 

  59. Phan Q, Tomasino E. Untargeted lipidomic approach in studying pinot noir wine lipids and predicting wine origin. Food Chem. 2021;355:129409.

    Article  CAS  PubMed  Google Scholar 

  60. Chappel JR, King ME, Fleming J, Eberlin LS, Reif DM, Baker ES. Aggregated Molecular Phenotype Scores: Enhancing Assessment and Visualization of Mass Spectrometry Imaging Data for Tissue-Based Diagnostics. Anal Chem. 2023;95(34):12913–12922.

  61. Chen X, Chen H, Dai M, Ai J, Li Y, Mahon B, et al. Plasma lipidomics profiling identified lipid biomarkers in distinguishing early-stage breast cancer from benign lesions. Oncotarget. 2016;7(24):36622–31.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Lim DK, Long NP, Mo C, Dong Z, Cui L, Kim G, et al. Combination of mass spectrometry-based targeted lipidomics and supervised machine learning algorithms in detecting adulterated admixtures of white rice. Food Res Int. 2017;100(Pt 1):814–21.

    Article  CAS  PubMed  Google Scholar 

  63. Liu Z-C, Wu W-H, Huang S, Li Z-W, Li X, Shui G-H, et al. Plasma lipids signify the progression of precancerous gastric lesions to gastric cancer: a prospective targeted lipidomics study. Theranostics. 2022;12(10):4671–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Wataya T, Nakanishi K, Suzuki Y, Kido S, Tomiyama N. Introduction to deep learning: minimum essence required to launch a research. Jpn J Radiol. 2020;38(10):907–21.

    Article  PubMed  Google Scholar 

  65. Lekadir K, Galimzianova A, Betriu A, Del Mar Vila M, Igual L, Rubin DL, et al. A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound. IEEE J Biomed Health Inform. 2017;21(1):48–55.

    Article  PubMed  Google Scholar 

  66. Cui S, Li C, Chen Z, Wang J, Yuan J. Research on Risk Prediction of Dyslipidemia in Steel Workers Based on Recurrent Neural Network and LSTM Neural Network. IEEE Access. 2020;8:34153–61.

    Article  Google Scholar 

  67. Sen P, Lamichhane S, Mathema VB, McGlinchey A, Dickens AM, Khoomrung S, et al. Deep learning meets metabolomics: a methodological perspective. Brief Bioinform. 2021;22(2):1531–42.

    Article  PubMed  Google Scholar 

  68. Parker C. On measuring the performance of binary classifiers. Knowl Inf Syst. 2013;35(1):131–52.

    Article  Google Scholar 

  69. Rajamanickam V, Babel H, Montano-Herrera L, Ehsani A, Stiefel F, Haider S, et al. About Model Validation in Bioprocessing. Processes. 2021;9(6):961.

    Article  Google Scholar 

  70. Niaz NU, Shahariar KMN, Patwary MJA. Class Imbalance Problems in Machine Learning: A Review of Methods And Future Challenges. ICCA 2022:485–490.

  71. Ye X, Zhu B, Chen Y, Wang Y, Wang D, Zhao Z, et al. Integrated Metabolomics and Lipidomics Approach for the Study of Metabolic Network and Early Diagnosis in Cerebral Infarction. J Proteome Res. 2022;21(11):2635–46.

    Article  CAS  PubMed  Google Scholar 

  72. Wickham H, Çetinkaya-Rundel M, Grolemund G, EBSCOhost. R for data science: import, tidy, transform, visualize, and model data. 2nd ed. O'Reilly Media; 2023.

  73. McKinney W. Python for data analysis: Data wrangling with pandas, numpy, and jupyter. 2nd ed. O'Reilly Media; 2017.

  74. Howell A, Yaros C. Downloading and Analysis of Metabolomic and Lipidomic Data from Metabolomics Workbench Using MetaboAnalyst 5.0. Methods Mol Biol. 2023;2625:313–21.

    Article  CAS  PubMed  Google Scholar 

  75. Mohamed A, Hill MM. LipidSuite: interactive web server for lipidomics differential and enrichment analysis. Nucleic Acids Res. 2021;49(W1):W346–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Lin W-J, Shen P-C, Liu H-C, Cho Y-C, Hsu M-K, Lin IC, et al. LipidSig: a web-based tool for lipidomic data analysis. Nucleic Acids Res. 2021;49(W1):W336–45.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Stevens R. Ontology Based Document Enrichment in Bioinformatics. Comp Funct Genomics. 2002;3(1):42–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Clair G, Reehl S, Stratton KG, Monroe ME, Tfaily MM, Ansong C, Kyle JE. Lipid Mini-On: mining and ontology tool for enrichment analysis of lipidomic data. Bioinformatics. 2019;35(21):4507–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Liebisch G, Fahy E, Aoki J, Dennis EA, Durand T, Ejsing CS, et al. Update on LIPID MAPS classification, nomenclature, and shorthand notation for MS-derived lipid structures. J Lipid Res. 2020;61(12):1539–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Molenaar MR, Jeucken A, Wassenaar TA, van de Lest CHA, Brouwers JF, Helms JB. LION/web: a web-based ontology enrichment tool for lipidomic data analysis. Gigascience. 2019;8(6):giz061.

  81. More P, Bindila L, Wild P, Andrade-Navarro M, Fontaine JF. LipiDisease: associate lipids to diseases using literature mining. Bioinformatics. 2021;37(21):3981–2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Garcia-Campos MA, Espinal-Enriquez J, Hernandez-Lemus E. Pathway Analysis: State of the Art. Front Physiol. 2015;6:383.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Gaud C, Sousa BC, Nguyen A, Fedorova M, Ni Z, O’Donnell VB, et al. BioPAN: a web-based tool to explore mammalian lipidome metabolic pathways on LIPID MAPS. F1000Research. 2021;10:4.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Kipp ZA, Martinez GJ, Bates EA, Maharramov AB, Flight RM, Moseley HNB, et al. Bilirubin Nanoparticle Treatment in Obese Mice Inhibits Hepatic Ceramide Production and Remodels Liver Fat Content. Metabolites. 2023;13(2):215.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Kutmon M, Van Iersel MP, Bohler A, Kelder T, Nunes N, Pico AR, et al. PathVisio 3: An Extendable Pathway Analysis Toolbox. PLoS Comput Biol. 2015;11(2):e1004085.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Otasek D, Morris JH, Bouças J, Pico AR, Demchak B. Cytoscape Automation: empowering workflow-based network analysis. Genome Biol. 2019;20(1):1758–64.

  87. Martens M, Ammar A, Riutta A, Waagmeester A, Slenter DN, Hanspers K, et al. WikiPathways: connecting communities. Nucleic Acids Res. 2021;49(D1):D613–21.

    Article  CAS  PubMed  Google Scholar 

  88. Haw R, Hermjakob H, D’Eustachio P, Stein L. Reactome pathway analysis to enrich biological discovery in proteomics data sets. Proteomics. 2011;11(18):3598–613.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O, Anwar N, et al. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res. 2011;39(Database issue):D685-90.

    Article  CAS  PubMed  Google Scholar 

  90. Odenkirk MT, Zin PPK, Ash JR, Reif DM, Fourches D, Baker ES. Structural-based connectivity and omic phenotype evaluations (SCOPE): a cheminformatics toolbox for investigating lipidomic changes in complex systems. Analyst. 2020;145(22):7197–209.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Marella C, Torda AE, Schwudke D. The LUX Score: A Metric for Lipidome Homology. PLoS Comput Biol. 2015;11(9):e1004511.

    Article  PubMed  PubMed Central  Google Scholar 

  92. Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N, Peng Y, et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol. 2016;34(8):828–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Gorrochategui E, Jaumot J, Tauler R. ROIMCR: a powerful analysis strategy for LC-MS metabolomic datasets. BMC Bioinformatics. 2019;20(1):256.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Lubiana T, Lopes R, Medeiros P, Silva JC, Goncalves ANA, Maracaja-Coutinho V, et al. Ten quick tips for harnessing the power of ChatGPT in computational biology. PLoS Comput Biol. 2023;19(8):e1011319.

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Funding

This work was funded by grants from the National Institute of Environmental Health Sciences (P42 ES027704, P42 ES031009), the National Institute of General Medical Sciences (R01 GM141277 and RM1 GM145416), and a cooperative agreement with the Environmental Protection Agency (STAR RD 84003201). The views expressed in this manuscript do not reflect those of the funding agencies.

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Authors and Affiliations

  1. Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC, 27606, USA

    Jessie R. Chappel

  2. Immunity, Inflammation, and Disease Laboratory, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, 27709, USA

    Kaylie I. Kirkwood-Donelson

  3. Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, 27709, USA

    David M. Reif

  4. Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA

    Erin S. Baker

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  1. Jessie R. Chappel
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Chappel, J.R., Kirkwood-Donelson, K.I., Reif, D.M. et al. From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome. Anal Bioanal Chem 416, 2189–2202 (2024). https://doi.org/10.1007/s00216-023-04991-2

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