Search

Additional file 1: Table S1. Method implementation details. Fig. S1. Overview of novel methods ssClustPA and kPCA for a single pathway example using simulated data based on the COVID dataset. Fig. S2. Performance of ssPA methods using the full pathway set (including redundancy) based on 3 randomly enriched pathways, derived using semi-synthetic data based on the IBD dataset. Fig. S3. Performance of ssPA methods using the full KEGG human pathway set (including redundancy) based on 3 randomly enriched pathways, derived using semi-synthetic data based on the COVID dataset. Fig. S4. ssPA method ability to rank highly the 3 randomly enriched pathways using the full set of Reactome pathways. Fig. S5. ssPA method performance in response to varying levels of pathway coverage. Fig. S6. ssPA method performance in response to varying levels of pathway coverage. Fig. S7. PCA scatter plots and density plots of PC1 scores obtained using the IBD data at the metabolite (upper panels) and pathway level using kPCA (lower panels). Fig. S8. Clustered heatmap of IBD data transformed to pathway scores using the kPCA method. Fig. S9. Pathway clusters derived using hierarchical clustering on COVID dataset transformed to pathway scores using kPCA (top 30 pathways). Table S2. Runtimes of ssPA methods, alongside GSEA* for comparison to conventional PA methods (average across 10 iterations). Table S3. Over-representation analysis results from IBD dataset. Table S4. Top 50 features in random forest model based on IBD data.

Additional file 1. R tutorial MWSL.

1H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum 1H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.

Metabolism is altered by genetics, diet, disease status, environment, and many other factors. Modeling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data, we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-adjusted projections to latent structures (CA-PLS) are exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS, we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques.

1H nuclear magnetic resonance (NMR) spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts.Statistical models to analyse such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS relies on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family-wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, calls for efficient prioritisation of spectral variables of interest.Using human serum 1H NMR spectroscopic profiles from 3,948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parametrisations and distributional features of the outcome. We propose both efficient visualisation methods, and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.