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Multivariate statistical analysis for metabolomic data: the key points in principal component analysis
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Ji-ye A*, Jun HE, Run-bin SUN
Acta Pharmaceutica Sinica | 2018, 53(6) : 929 - 937
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Acta Pharmaceutica Sinica | 2018, 53(6): 929-937
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Multivariate statistical analysis for metabolomic data: the key points in principal component analysis
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Ji-ye A*, Jun HE, Run-bin SUN
Affiliations
  • Jiangsu Province Key Laboratory of Drug Metabolism and Pharmacokinetics, Laboratory of Metabolomics, Jiangsu Key Laboratory of Drug Design and Optimization, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
Published: 2018-06-12 doi: 10.16438/j.0513-4870.2017-1288
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Metabolomics data contains multiple variables usually processed and evaluated by means of principal components analysis. The statistical analysis of the multivariate data is involved in abstract, elusory fitting for the model of hyperspace, complicated theoretical arithmetic and sophisticated transformation of the data matrix. It is crucially important to understand the arithmetic mechanism and the properties of the models fully. In this article, we reviewed the key and puzzling issues in principal components analysis of the metabolomics data, including the principal components, the scores and loadings of a principal components, scaling and weighting, partial least square projection to latent structures, partial least squares discriminant analysis, orthogonal projection to latent structure, orthogonal bidirectional projections to latent structures, S-plot, shared and unique structure plot, and the validation of the model. Hopefully, this article provides a better understanding of data processing mode, model selection, procedure standardization, and data interpretation for a reliable conclusion.

metabolomics  /  principal component analysis  /  system biology  /  multivariate statistical analysis  /  principal component
Ji-ye A, Jun HE, Run-bin SUN. Multivariate statistical analysis for metabolomic data: the key points in principal component analysis[J]. Acta Pharmaceutica Sinica, 2018 , 53 (6) : 929 -937 . DOI: 10.16438/j.0513-4870.2017-1288
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Article Info
doi: 10.16438/j.0513-4870.2017-1288
  • Receive Date：2017-12-25
  • Online Date：2026-01-15
  • Published：2018-06-12
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  • Received：2017-12-25
  • Revised：2018-03-25
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    Jiangsu Province Key Laboratory of Drug Metabolism and Pharmacokinetics, Laboratory of Metabolomics, Jiangsu Key Laboratory of Drug Design and Optimization, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
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表12种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
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Genus		 种数
Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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