Fig. 5 ∣. Stabl’s performance on a triple-omic data integration task.

a, Clinical case study 3: prediction of the time to labor from longitudinal assessment of plasma proteomic (SomaLogic), metabolomic (untargeted mass spectrometry) and single-cell mass cytometry data in two independent cohorts of pregnant individuals. b, Sparsity performances (number of features selected across CV iterations, median and IQR) for Stabl_L (left), Stabl_EN (middle) and Stabl_AL (right) compared to their respective SRM (late-fusion data integration method) across n = 100 CV iterations. c,d, Predictivity performances as squared error (SE) on the training (n = 150 samples, c) and validation (n = 27 samples, d) datasets for Stabl_L (left), Stabl_EN (middle) and Stabl_AL (right). Stabl_SRM performances are shown using MX knockoffs. Results using random permutations are shown for Stabl_L in Supplementary Table 5. Median and IQR values comparing Stabl_SRM performances to their cognate SRMs are listed in Supplementary Table 5. e–g, UMAP visualization (upper) and stability path (lower) of the metabolomic (e), plasma proteomic (f) and single-cell mass cytometry (g) datasets. UMAP node size and color are proportional to the strength of association with the outcome. Stability path graphs denote features selected by Stabl_L. The data-driven reliability threshold $\theta$ is computed for each individual omic dataset and is indicated by a dotted line. Significance of the association with the outcome was calculated using Pearson’s correlation. Box plots indicate median and IQR; whiskers indicate 1.5× IQR.