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Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Fig 2

Assessment of H-CBN2 on simulated data.

A Box plots of the difference between true (ϵ) and estimated () error rate (y-axis) for each of the evaluated poset sizes (x-axis). B Box plots of the relative median absolute error (RMAE; y-axis) of the estimated rate parameters . C Average run time of the MCEM/EM step (y-axis, logarithmic scale) for different poset sizes (x-axis, logarithmic scale). The blue dotted line corresponds to linear scaling, whereas the red line corresponds to quadratic scaling. In panels A to C, different colors indicate different importance sampling schemes and we show results of 100 simulated data sets for each of combination of the simulation settings. The true error rate is ϵ = 0.05, the number of samples drawn from the proposal distribution is set to L = 1000 unless specified otherwise and we run 100 iterations of the MCEM/EM algorithm. D Error in the estimation of the log-likelihood, . E Box plots of F₁ scores for reconstructed network edges. In panels D and E, we show results of 20 different networks with 16 mutations and an error rate of 5%. We fix the ideal acceptance rate to 1/p, and run 25,000 iterations of the simulated annealing algorithm. The initial temperature is set to Θ₀ = 50 for all runs, and for adaptive simulated annealing, three adaptation rates are evaluated (a_r = 0.1, 0.3, 0.5). Comparison of H-CBN2 to MC-CBN methods in terms of F the difference in normalized log-likelihood and G F₁ scores for two poset sizes and various error rates. For the H-CBN2 results shown in panels F and G, we employ the ASA algorithm. SA: simulated annealing, ASA: adaptive simulated annealing, +: with additional new moves.

doi: https://doi.org/10.1371/journal.pcbi.1008363.g002