Figure 3. Derivation, validation, and biological characterization of 13 consensus CSF proteomic meta-programs.
(a) Meta-program discovery workflow. Within-cohort non-negative matrix factorization (NMF) and independent component analysis (ICA) across ranks K=3–15, together with scale-free shared-nearest-neighbour (SSN) analysis across multiple resolutions, generated 28,795 candidate programs. Sequential filtering retained 10,805 robust and 634 non-redundant programs, of which 247 were assigned by iterative community detection to 13 consensus meta-programs (MP1–MP13). (b) Pairwise Jaccard similarity among the 247 assigned programs, ordered by hierarchical clustering. Marginal bars, consensus meta-program. (c) Leave-one-study-out recovery across 35 held-out datasets. Receiver operating characteristic and precision–recall curves compare correlations between predicted and observed MP activity with sample-size-matched Gaussian null correlations. Right, MP-specific AUROC and AUPRC; points and bars, estimates ± 95% CI. (d) Study-level ceiling-normalized recapitulation ratio (Ri) in 33 evaluable cohorts, calculated as the cross-validated fraction of variance explained (CV-FVE) by the MPs relative to a PCA ceiling using the number of active MPs in each study. Teal, recapitulated variance; grey, residual; dashed and solid lines, median and sample-size-weighted mean Ri, respectively. (e) Integrated biological characterization. Alluvial links assign MPs to six biological domains; the heatmap shows scaled enrichment scores for representative annotations, and right-hand annotations summarize core-proteome composition and detection properties. (f) Bipartite network linking the 13 MPs to member proteins. Protein-node size, number of MP memberships; Grey edges, membership; edge width, proportional to the protein–MP Spearman correlation; coloured labels, ten most positively correlated proteins for each MP.