Extended Data Fig. 9. Scenario with reduced prevalence in training and test datasets and multi-centre scenario at a four-node setting.

Main settings as in Fig. 2a. a, Scenario with prevalences from 10% at node 1 to 3% at node 4. There are three test datasets (b–d) with decreasing prevalence and increasing total sample size. b, Evaluation of scenario in a with 111:100 ratio over 50 permutations. c, Evaluation of scenario in a with 1:4 ratio and increased sample number of the test dataset over 50 permutations. d, Evaluation of scenario in a with 1:10 prevalence and increased sample number of the test dataset over 50 permutations. e, Dataset properties for the participating cities E1–E8, indicating case:control ratio and demographic properties. f, AUC, accuracy, sensitivity, specificity and F1 score over 20 permutations for scenario that uses E1–E6 as training nodes and E7 as external test node. g, Evaluation of a multi-city scenario where a medical centre (in each row) serves as a test node. The AUC for each training node and the SL is shown for 20 permutations. h, Multi-city scenario. Only three nodes (E1–E3) are used for training and the external test node E4 uses data from a different sequencing facility. AUC, accuracy, sensitivity and specificity as well as the confusion matrix for one prediction. b–d, f, g, Box plots show performance of all permutations performed for the training nodes individually as well as the results obtained by SL. All samples are biological replicates. Centre dot, mean; box limits, 1st and 3rd quartiles; whiskers, minimum and maximum values. Performance measures are defined for the independent fourth node used for testing only. Statistical differences between results derived by SL and all individual nodes including all permutations performed were calculated with one-sided Wilcoxon signed rank test with continuity correction; *P < 0.05, exact P values listed in Supplementary Table 5.