Fig. 2: Deeper coverage and stronger drivers improve predictions.

In a, using 541 simulations of tumor growth under a birth-and death model, we show the absolute median distance \(\widetilde{ \left| D \right| }\) as in “absolute number of ordered mutations” between predicted and simulated driver for sequencing depths. With the exception of k = 2 for 100× (two-tailed t test P = 0.015), we were able to detect the driver’s presence (P < 0.005). Blue line represents the random \(\widetilde{ \left| D \right| }\) as derived by selecting a random mutation from each simulation and calculate the absolute distance to the simulated driver. Dotted lines represent the 2 × σ deviation from \(\widetilde{ \left| D \right| }\) while capped bars the median’s standard error. For convenience, we only show bars for k = 2. In b, Using the same simulations, lower coverage provides less accurate k predictions with a lower effect. Capped bars represent the standard error of the median effect prediction. The three lines represent simulations with simulated effect of 2–4. In c, using the “Williams et al. 2018” algorithm, we simulated 360 nonneutral and 140 neutral tumors for 10,000 cells. Then, we adjusted our effect predictions for n* equal to 1,000,000. In addition, we also adjusted the simulated selection coefficient s* for the same populations. Pearson’s r between the simulated adjusted coefficient “1 + s*” against adjusted predicted k* was 0.6. In d, after ranking s* for every nonneutral simulation, we used a sliding window of 20 simulations to estimate \(\widetilde{ \left| D \right| }\) (and 2 × σ) between the simulated and predicted driver within every window. Dotted lines represent 2 × σ deviation. When s* > 0.05 our driver detection became highly accurate. Blue line represents \(\widetilde{ \left| D \right| }\) for random predictions (444.5), while black lines represent median standard error (24.5). Simulated s* have been projected for n* = 1,000,000. In e, using Kingman’s coalescent theory, we show that growth estimator r̂ remains qualitatively unchanged even for non g-hitchhikers. As mutational density δ_n increases with n, and hence with time, r̂ estimator is predicted to take positive values for both constant and varying populations. Similarly, for negative growth, δ_n decreases with time. We let α > 1 corresponding to a decreasing and α < 1 corresponding to an increasing population.