Figure 3

Breakpoint distributions. To assess the ability of the proposed method to delineate relevant regions, determined its breakpoint distributions for different simulation parameters (Materials and methods). This figure (π = 0.1) represents an excerpt from the full set of results (Additional file 2). Key observations are as follows. (1) The distributions of the proposed method exhibit significantly higher 'peaks' around the true breakpoints (vertical dotted lines). This signifies that, given that the proposed method detects a breakpoint, the probability that it is a true breakpoint is higher. (2) The distributions for the proposed methods exhibit markedly 'scooped' centers, that is, there is less distributional mass (fewer breakpoints) inside the relevant segment. Thus, the method detects fewer false breakpoints inside relevant regions, even when the region is large. This improvement is a result of the use of multiple regularization parameter values (Materials and methods). (3) As in Figure 2, the improvements were particularly pronounced under expression data-like conditions. In this test, T _μ = 0.5·SNR (similar results for other reasonable values).