Fig. 3

Aneuploidy patterns in CCLs and primary tumors are shaped by similar features. A The ML scheme for analysis of aneuploidy patterns in CCLs. The gain and loss models that were trained on aneuploidy patterns in primary tumors were applied to aneuploidy patterns in CCLs. Performance was measured using tenfold cross-validation. Gain model (gradient boosting): auROC = 83%, auPRC = 49% (expected 15%). Loss model (XGBoost): auROC = 76%, auPRC = 45% (expected 11%). B The average absolute contribution of the ten topmost features to the gain model (see legend of Fig. 2A). The order and directionality of the features generally agree with the gain model in primary tumors. C A detailed view of the contribution of the ten topmost features to the gain model (see legend of Fig. 2B). D Same as B for the loss model. The order and directionality of the features generally agree with the loss model in primary tumors. E Same as panel C for the loss model. F The correlations between top contributing features and the frequencies of chromosome-arm gains and losses, as measured by Spearman correlation. p-values were adjusted for multiple hypothesis testing using Benjamini–Hochberg procedure. Negative correlation between TSG density and gain frequency (ρ = − 0.37, adjusted p = 0.04). Positive correlation between TSG density and loss frequency (ρ = 0.17, adjusted p = 0.32). Positive correlation between OG density and gain frequency (ρ = 0.44, adjusted p = 0.012). Negative correlation between OG density and loss frequency (ρ = − 0.28, adjusted p = 0.13). Positive correlation between CCL expression and gain frequency (ρ = 0.53, adjusted p = 0.002). Negative correlation between CCL expression and loss frequency (ρ = − 0.6, adjusted p = 0.0006). Positive correlation between essential gene density and gain frequency (ρ = 0.18, adjusted p = 0.33). Negative correlation between essential gene density and loss frequency (ρ = − 0.17, adjusted p = 0.32)