Normalization Methods for Reducing Interbatch Effect Without Quality Control Samples in Liquid Chromatography-mass Spectrometry-based Studies

Overview

Journal Anal Bioanal Chem

Specialty Chemistry

Date 2021 Mar 24

PMID 33760933

Citations 7

Authors

Alisa O Tokareva

Vitaliy V Chagovets

Alexey S Kononikhin

Natalia L Starodubtseva

Eugene N Nikolaev

Vladimir E Frankevich

Affiliations

Soon will be listed here.

Abstract

Data normalization is an essential part of a large-scale untargeted mass spectrometry metabolomics analysis. Autoscaling, Pareto scaling, range scaling, and level scaling methods for liquid chromatography-mass spectrometry data processing were compared with the most common normalization methods, including quantile normalization, probabilistic quotient normalization, and variance stabilizing normalization. These methods were tested on eight datasets from various clinical studies. The efficiency of the data normalization was assessed by the distance between clusters corresponding to batches and the distance between clusters corresponding to clinical groups in the space of principal components, as well as by the number of features with a pairwise statistically significant difference between the batches and the number of features with a pairwise statistically significant difference between clinical groups. Autoscaling demonstrated the most effective reduction in interbatch variation and can be preferable to probabilistic quotient or quantile normalization in liquid chromatography-mass spectrometry data.

Citing Articles

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data.

Pakkir Shah A, Walter A, Ottosson F, Russo F, Navarro-Diaz M, Boldt J Nat Protoc. 2024; 20(1):92-162.

PMID: 39304763 DOI: 10.1038/s41596-024-01046-3.

Predicting prognosis outcomes of primary central nervous system lymphoma with high-dose methotrexate-based chemotherapeutic treatment using lipidomics.

Zhong Y, Zhou L, Xu J, Huang H Neurooncol Adv. 2024; 6(1):vdae119.

PMID: 39119277 PMC: 11306931. DOI: 10.1093/noajnl/vdae119.

Identification of Metabolomic Signatures for Ischemic Hypoxic Encephalopathy Using a Neonatal Rat Model.

Shevtsova Y, Eldarov C, Starodubtseva N, Goryunov K, Chagovets V, Ionov O Children (Basel). 2023; 10(10).

PMID: 37892356 PMC: 10605414. DOI: 10.3390/children10101693.

COVID-19 Infection during Pregnancy: Disruptions in Lipid Metabolism and Implications for Newborn Health.

Frankevich N, Tokareva A, Chagovets V, Starodubtseva N, Dolgushina N, Shmakov R Int J Mol Sci. 2023; 24(18).

PMID: 37762087 PMC: 10531385. DOI: 10.3390/ijms241813787.

Non-Invasive Differential Diagnosis of Cervical Neoplastic Lesions by the Lipid Profile Analysis of Cervical Scrapings.

Tokareva A, Chagovets V, Attoeva D, Starodubtseva N, Nazarova N, Gusakov K Metabolites. 2022; 12(9).

PMID: 36144287 PMC: 9506087. DOI: 10.3390/metabo12090883.

References

Bijlsma S, Bobeldijk I, Verheij E, Ramaker R, Kochhar S, Macdonald I . Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. Anal Chem. 2006; 78(2):567-74. DOI: 10.1021/ac051495j. View

De Livera A, Sysi-Aho M, Jacob L, Gagnon-Bartsch J, Castillo S, Simpson J . Statistical methods for handling unwanted variation in metabolomics data. Anal Chem. 2015; 87(7):3606-15. PMC: 4544854. DOI: 10.1021/ac502439y. View

Wang S, Kuo C, Tseng Y . Batch Normalizer: a fast total abundance regression calibration method to simultaneously adjust batch and injection order effects in liquid chromatography/time-of-flight mass spectrometry-based metabolomics data and comparison with current.... Anal Chem. 2012; 85(2):1037-46. DOI: 10.1021/ac302877x. View

Dieterle F, Ross A, Schlotterbeck G, Senn H . Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal Chem. 2006; 78(13):4281-90. DOI: 10.1021/ac051632c. View

Han R, Wang M, Fang X, Han X . Simulation of triacylglycerol ion profiles: bioinformatics for interpretation of triacylglycerol biosynthesis. J Lipid Res. 2013; 54(4):1023-32. PMC: 3605979. DOI: 10.1194/jlr.M033837. View

Du Y, Hu Y, Xia Y, Ouyang Z . Power Normalization for Mass Spectrometry Data Analysis and Analytical Method Assessment. Anal Chem. 2016; 88(6):3156-63. PMC: 8135100. DOI: 10.1021/acs.analchem.5b04418. View

Chawade A, Alexandersson E, Levander F . Normalyzer: a tool for rapid evaluation of normalization methods for omics data sets. J Proteome Res. 2014; 13(6):3114-20. PMC: 4053077. DOI: 10.1021/pr401264n. View

Lee J, Park J, Lim M, Seong S, Ju Seo J, Park S . Quantile normalization approach for liquid chromatography-mass spectrometry-based metabolomic data from healthy human volunteers. Anal Sci. 2012; 28(8):801-5. DOI: 10.2116/analsci.28.801. View

Yang Q, Wang Y, Zhang Y, Li F, Xia W, Zhou Y . NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data. Nucleic Acids Res. 2020; 48(W1):W436-W448. PMC: 7319444. DOI: 10.1093/nar/gkaa258. View

10.

Chen J, Zhang P, Lv M, Guo H, Huang Y, Zhang Z . Influences of Normalization Method on Biomarker Discovery in Gas Chromatography-Mass Spectrometry-Based Untargeted Metabolomics: What Should Be Considered?. Anal Chem. 2017; 89(10):5342-5348. DOI: 10.1021/acs.analchem.6b05152. View

11.

Ly-Verdu S, Groger T, Arteaga-Salas J, Brandmaier S, Kahle M, Neschen S . Combining metabolomic non-targeted GC×GC-ToF-MS analysis and chemometric ASCA-based study of variances to assess dietary influence on type 2 diabetes development in a mouse model. Anal Bioanal Chem. 2014; 407(1):343-54. DOI: 10.1007/s00216-014-8227-4. View

12.

Li B, Tang J, Yang Q, Li S, Cui X, Li Y . NOREVA: normalization and evaluation of MS-based metabolomics data. Nucleic Acids Res. 2017; 45(W1):W162-W170. PMC: 5570188. DOI: 10.1093/nar/gkx449. View

13.

Chagovets V, Wang Z, Kononikhin A, Starodubtseva N, Borisova A, Salimova D . A Comparison of Tissue Spray and Lipid Extract Direct Injection Electrospray Ionization Mass Spectrometry for the Differentiation of Eutopic and Ectopic Endometrial Tissues. J Am Soc Mass Spectrom. 2017; 29(2):323-330. DOI: 10.1007/s13361-017-1792-y. View

14.

Cook T, Ma Y, Gamagedara S . Evaluation of statistical techniques to normalize mass spectrometry-based urinary metabolomics data. J Pharm Biomed Anal. 2019; 177:112854. PMC: 6823157. DOI: 10.1016/j.jpba.2019.112854. View