Fig. 1

Genomic predictor simulation experiment. We created three frequency-based classes of genomic alterations in ductal carcinoma in situ (DCIS): low frequency (LF) (5 %), mid frequency (MF) (15 %), and high frequency (HF) (30 %), and four classes of differential frequencies between cases and controls: highly differential (HD) (alteration frequency is threefold higher in cases versus controls), moderately differential (MD) (alteration frequency is 1.5-fold higher in cases versus controls), low-level differential (LD) (alteration is 1.25-fold higher in cases versus controls), and nondifferential (ND) (alteration frequency is generated from the same distribution in cases and controls). For each of 2000 simulations, we used L1-regularized logistic regression to build a predictor and performed tenfold cross-validation to select the optimal value for the λ tuning parameter. For each simulation, we recorded which types of features in terms of frequency (LF, MF, HF) and differential status (ND, LD, MD, HD) were active in the model. The results are displayed in the figure, with the feature types along the X-axis and the proportion of simulations in which each feature type was active in the model along the Y-axis