Inferring parameters of cancer evolution in chronic lymphocytic leukemia
Fig 2
Accuracy of parameter inferences from simulated data.
We simulated tumor growth by performing a Monte Carlo simulation, which simulates the birth, death, and accumulation of mutations in the individual cells that make up a tumor, and generates the mutation frequency and tumor size data used by the estimates. Simulations are of fast-growing tumors with (a) single driver subclone and mutation rate u = 1, (b) single driver subclone and u = 3, (c) two nested driver subclones with u = 1, and (d) two sibling driver subclones with u = 1. Mean percent errors (MPEs) of estimates are shown in black above the plots, and mean absolute percent errors (MAPEs) are shown in gray. Boxes contain 25th-75th quartiles, with median indicated by thick horizontal black line. Whiskers of boxplots indicate 2.5 and 97.5 percentiles. Violins are smoothed density estimates of the percent error data points. Complete parameter values and number of runs are included in S1 Table.