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Putting BASIL in a BLT: A Bayesian filtering method for estimating the fitness effects of nascent adaptive mutations
Fig 2
Observed lineage read counts deviate from those predicted by equation (1) both in simulated and real data.
A. Rescaled average read count 
at the next time point (ordinate) plotted against the read count observed at the previous time point rk–1 (abscissa) for the weak-selection simulation. Light and dark gray points represent early (tk = 16 generations) and late (tk = 160 generations) time intervals. Lines represent least-squares best fits within the linear regime (see Materials and Methods). B. Error in the inferred mean fitness (inferred minus true) as a function of the read count of lineages that are used as the neutral reference. Shades are the same as in panel A. C. Same as panel A but for the strong selection regime. Early time interval is at tk = 16 generations and late time interval is at tk = 64 generations. D. Same as panel B but for the strong selection regime. E–G. Same as panel A but for three real BLT datasets: Levy 2015 R1 (panel E), Li 2019 Evo1D R2 (panel F) and Venkataram 2023 Co-evolution R5 (panel G). Early time interval is at tk = 16, 7, 20 generations and late time interval is at tk = 112, 133, 86 generations in panels E, F, G, respectively.
doi: https://doi.org/10.1371/journal.pcbi.1013946.g002