A Comparison of Aggregate P-Value Methods and Multivariate Statistics for Self-Contained Tests of Metabolic Pathway Analysis

Mitchell, Matthew

doi:10.1371/journal.pone.0125081

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Differences between cohorts within the entire lipid class were quantified using the cumulative p value calculated by Fisher’s method (Table S3). The cumulative p value has been used previously to describe cumulative changes in multiple gene expression data sets and can also be used for pathway analysis . The critical threshold of the cumulative p value was adjusted by BF to account for the different numbers of lipids in each lipid class (Table S3).…”

Section: Methodsmentioning

confidence: 99%

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Parallel Metabolomics and Lipidomics of a PSMA/GCPII Deficient Mouse Model Reveal Alteration of NAAG Levels and Brain Lipid Composition

Sedlák,

Kvasnička,

Marešová

et al. 2024

ACS Chem. Neurosci.

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Glutamate carboxypeptidase II (GCPII, also known as PSMA or FOLH1) is responsible for the cleavage of N-acetylaspartyl-glutamate (NAAG) to N-acetyl-aspartate and glutamate in the central nervous system and facilitates the intestinal absorption of folate by processing dietary folyl-poly-γ-glutamate in the small intestine. The physiological function of GCPII in other organs like kidneys is still not known. GCPII inhibitors are neuroprotective in various conditions (e.g., ischemic brain injury) in vivo; however, their utilization as potential drug candidates has not been investigated in regard to not yet known GCPII activities. To explore the GCPII role and possible side effects of GCPII inhibitors, we performed parallel metabolomic and lipidomic analysis of the cerebrospinal fluid (CSF), urine, plasma, and brain tissue of mice with varying degrees of GCPII deficiency (fully deficient in Folh1, −/−; one allele deficient in Folh1, +/−; and wild type, +/+). Multivariate analysis of metabolites showed no significant differences between wild-type and GCPII-deficient mice (except for NAAG), although changes were observed between the sex and age. NAAG levels were statistically significantly increased in the CSF, urine, and plasma of GCPII-deficient mice. However, no difference in NAAG concentrations was found in the whole brain lysate likely because GCPII, as an extracellular enzyme, can affect only extracellular and not intracellular NAAG concentrations. Regarding the lipidome, the most pronounced genotype-linked changes were found in the brain tissue. In brains of GCPII-deficient mice, we observed statistically significant enrichment in phosphatidylcholine-based lipids and reduction of sphingolipids and phosphatidylethanolamine plasmalogens. We hypothesize that the alteration of the NAA-NAAG axis by absent GCPII activity affected myelin composition. In summary, the absence of GCPII and thus similarly its inhibition do not have detrimental effects on metabolism, with just minor changes in the brain lipidome.

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Section: Methodsmentioning

confidence: 99%

“…The cumulative p value has been used previously to describe cumulative changes in multiple gene expression data sets and can also be used for pathway analysis. 67 The critical threshold of the cumulative p value was adjusted by BF to account for the different numbers of lipids in each lipid class (Table S3).…”

Section: Sample Preparationmentioning

confidence: 99%

Parallel Metabolomics and Lipidomics of a PSMA/GCPII Deficient Mouse Model Reveal Alteration of NAAG Levels and Brain Lipid Composition

Sedlák,

Kvasnička,

Marešová

et al. 2024

ACS Chem. Neurosci.

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A three-stage approach to identify biomarker signatures for cancer genetic data with survival endpoints

Wu,

Chen,

et al. 2024

Stat Methods Appl

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The identification of prognostic and predictive biomarker signatures is crucial for drug development and providing personalized treatment to cancer patients. However, the discovery process often involves high-dimensional candidate biomarkers, leading to inflated family-wise error rates (FWERs) due to multiple hypothesis testing. This is an understudied area, particularly under the survival framework. To address this issue, we propose a novel three-stage approach for identifying significant biomarker signatures, including prognostic biomarkers (main effects) and predictive biomarkers (biomarker-by-treatment interactions), using Cox proportional hazard regression with high-dimensional covariates. To control the FWER, we adopt an adaptive group LASSO for variable screening and selection. We then derive adjusted p-values through multi-splitting and bootstrapping to overcome invalid p values caused by the penalized approach’s restrictions. Our extensive simulations provide empirical evaluation of the FWER and model selection accuracy, demonstrating that our proposed three-stage approach outperforms existing alternatives. Furthermore, we provide detailed proofs and software implementation in R to support our theoretical contributions. Finally, we apply our method to real data from cancer genetic studies.

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A Comparison of Aggregate P-Value Methods and Multivariate Statistics for Self-Contained Tests of Metabolic Pathway Analysis

Cited by 2 publications

References 18 publications

Parallel Metabolomics and Lipidomics of a PSMA/GCPII Deficient Mouse Model Reveal Alteration of NAAG Levels and Brain Lipid Composition

Parallel Metabolomics and Lipidomics of a PSMA/GCPII Deficient Mouse Model Reveal Alteration of NAAG Levels and Brain Lipid Composition

A three-stage approach to identify biomarker signatures for cancer genetic data with survival endpoints

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