Extended Data Fig. 5. Scenario for ALL in dataset 2 and multi-class prediction and expansion of SL.

Main settings are identical to what is described in Fig. 2a. Here cases are samples derived from patients with ALL, while all other samples are controls (including AML). a, Scenario for the detection of ALL in dataset A2. The training sets are evenly distributed among the nodes with varying prevalence at the testing node. Data from independent clinical studies are samples to each node, as described for AML in Fig. 2d. b, Evaluation of scenario in a for test accuracy over 100 permutations with a prevalence ratio of 1:1. c, Evaluation using a test dataset with prevalence ratio of 10:100 over 100 permutations. d, Evaluation using a test dataset with prevalence ratio of 5:100 over 100 permutations. e, Evaluation using a test dataset with prevalence ratio of 1:100. f, Scenario for multi-class prediction of different types of leukaemia in dataset A2. Each node has a different prevalence. g, Test accuracy for the different types of leukaemia over 20 permutations. h, Scenario that simulates 32 small Swarm nodes. i, Evaluation of test accuracy for the 32 nodes and the Swarm over 10 permutations. j, Development of accuracy over training epochs with addition of new nodes. b–e, g, i, 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 test 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.