Fig. 1: Analytic workflow of the study. | Nature Communications

Fig. 1: Analytic workflow of the study.

From: Multi-omics architecture of childhood obesity and metabolic dysfunction uncovers biological pathways and prenatal determinants

Fig. 1

a We integrated multi-omics data, including DNA methylation, miRNAs, transcript clusters, proteins and metabolites, from childhood blood samples from the HELIX population-based project. We applied similarity network fusion and spectral clustering to derive distinct multi-omics clusters in children from the Northern/Western European part and recapitulated these clusters in children of the Southern/Mediterranean part. b Using generalized regression models, we examined the association of the multi-omics clusters with several metabolic outcomes to characterize the clinical phenotype of each cluster. c We applied machine learning methods to derive SHapley Additive exPlanation (SHAP) values in order to identify the molecular features with high importance in cluster definition and then performed pathway analysis to characterize underlying biological pathways. d We examined how the prenatal environment affects cluster membership. We applied Least Absolute Shrinkage Selection Operator (LASSO) with Stability-enHanced Approaches using Resampling Procedures (SHARP) to identify the most important determinants among several prenatal factors. We then estimated the probability of cluster membership across levels of the identified determinants.

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