ArticleCurrent Metabolomics
PCA as a practical indicator of OPLS-DA model reliability.
Article in Current Metabolomics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 183 papers, 1 of them a synthesis that pooled it.
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Who cites it
183 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- NMR metabolomic profiling of cerebrospinal fluid from dogs with meningoencephalitis of unknown origin demonstrates metabolic similarities to multiple sclerosis.Metabolomics : Official journal of the Metabolomic Society · 2026Article
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123 more citing papers are in PubMed but not listed here.
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2 authors.
Funding
Abstract
backgroundPrincipal Component Analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) are powerful statistical modeling tools that provide insights into separations between experimental groups based on high-dimensional spectral measurements from NMR, MS or other analytical instrumentation. However, when used without validation, these tools may lead investigators to statistically unreliable conclusions. This danger is especially real for Partial Least Squares (PLS) and OPLS, which aggressively force separations between experimental groups. As a result, OPLS-DA is often used as an alternative method when PCA fails to expose group separation, but this practice is highly dangerous. Without rigorous validation, OPLS-DA can easily yield statistically unreliable group separation.
methodsA Monte Carlo analysis of PCA group separations and OPLS-DA cross-validation metrics was performed on NMR datasets with statistically significant separations in scores-space. A linearly increasing amount of Gaussian noise was added to each data matrix followed by the construction and validation of PCA and OPLS-DA models.
resultsWith increasing added noise, the PCA scores-space distance between groups rapidly decreased and the OPLS-DA cross-validation statistics simultaneously deteriorated. A decrease in correlation between the estimated loadings (added noise) and the true (original) loadings was also observed. While the validity of the OPLS-DA model diminished with increasing added noise, the group separation in scores-space remained basically unaffected.
conclusionSupported by the results of Monte Carlo analyses of PCA group separations and OPLS-DA cross-validation metrics, we provide practical guidelines and cross-validatory recommendations for reliable inference from PCA and OPLS-DA models.
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