Fig. 2From: The poly-omics of ageing through individual-based metabolic modellingk-means clustering with age. We propose and investigate an average age-based silhouette value a. This is calculated using the chronological age, and clustering data from both hierarchical and k-means clustering. The silhouette values are calculated by averaging all the individuals’ silhouette scores, for a number of clusters ranging from 2 to 30. k-means clustering performs consistently better than hierarchical clustering. In both types of clustering, fluxomic data clusters better with chronological age than transcriptomic data. The pairwise distance of clusters with chronological age is visualised in scatter plots for both transcriptomic e and fluxomic c data. Clusters are annotated with different shapes, while age is shown with colour. Individual clusters are plotted against transcriptomic and chronological age for both transcriptomic d and fluxomic b data. Note that since transcriptomic age was calculated from transcriptomic data, we would expect to see more distinction in transcriptomic age between clusters for the transcriptomic dataBack to article page