# Improved Duplication-Transfer-Loss Reconciliation with
## 4 Results
### Results on Simulated Datasets
Second, we find that RANGER-DTLx with \(\mathbb{TX}\) cost 3 and ecceTERA, despite using effectively the same event costs, differ dramatically in the number of \(\mathbb{TX}\) events they infer. For example, for Dataset-1, ecceTERA infers only 413 \(\mathbb{TX}\) events across all 500 gene trees with recall and precision of 2.61% and 9.44%, respectively, and RANGER-DTLx with \(\mathbb{TX}\) cost 3 infers 4515 \(\mathbb{TX}\) events with recall and precision of 30.12% and 9.97%. Furthermore, third, these results show that there is a clear tradeoff between sensitivity and precision of \(\mathbb{TX}\) event inference and suggest that RANGER-DTLx with \(\mathbb{TX}\) cost 4 should be the method of choice for \(\mathbb{TX}\) event inference if precision is more important than recall, while RANGER-DTLx with \(\mathbb{TX}\) cost 3 should be used if recall is more important than precision. This experiment also highlights the utility of allowing for a user-specifiable cost for \(\mathbb{TX}\) events as permitted under our model but not under ecceTERA. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Dataset-1**} \\ \hline  & **Event Accuracy** & **Mapping Accuracy** & **Recipient Accuracy** \\ \hline RANGER-DTL & 0.961 & 0.937 & 0.682 \\ RANGER-DTLx, \(P_{\mathbb{TX}}=4\) & 0.961 & 0.939 & 0.697 \\ RANGER-DTLx, \(P_{\mathbb{TX}}=3\) & 0.917 & 0.895 & 0.716 \\ ecceTERA & 0.961 & 0.937 & 0.704 \\ \hline \hline  & **Dataset-2** & & \\ \hline  & **Event Accuracy** & **Mapping Accuracy** & **Recipient Accuracy** \\ \hline RANGER-DTL & 0.944 & 0.912 & 0.623 \\ RANGER-DTLx, \(P_{\mathbb{TX}}=4\) & 0.944 & 0.912 & 0.637 \\ RANGER-DTLx, \(P_{\mathbb{TX}}=3\) & 0.894 & 0.861 & 0.670 \\ ecceTERA & 0.945 & 0.910 & 0.650 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall reconciliation accuracy. Average reconciliation accuracy, in terms of mapping accuracy and event assignment accuracy, is shown for each method. Event/mapping accuracy for a given input gene tree is calculated as the total number of correct events/mappings divided by the total number of internal nodes in that gene tree. The table also shows the accuracy of inferred recipients for transfer events. This recipient accuracy for each gene tree is calculated as the total number of correctly identified recipients divided by the total number of correctly identified transfers. Results are averaged across the 500 gene tree/species tree pairs in each dataset. We also assessed if inferred \(\mathbb{TX}\) events that have higher support (i.e., are inferred in at least some fraction of all optimal reconciliations for that gene tree) are more likely to be correct (i.e., show higher precision). Figure 5 shows the results of this analysis. These results suggest that higher support values do tend to result in greater precision. More precisely, we find that RANGER-DTLx with \(\mathbb{TX}\) cost 3 and ecceTERA both shown similar trends in precision, with precision on Dataset-1 increasing from a low of about 0.1 for all inferred \(\mathbb{TX}\) events irrespective of support to a high of about 0.18 if only considering \(\mathbb{TX}\) events with 100% support, and precision on Dataset-2 increasing from about 0.1 for all inferred \(\mathbb{TX}\) events irrespective of support to a high of approximately 0.25 if only considering \(\mathbb{TX}\) events with 100% support. However, as expected, the increase in precision comes at the expense of recall (Table 4). For example, on Dataset-2, RANGER-DTLx with \(\mathbb{TX}\) cost 3 infers a total of 14,134 distinct \(\mathbb{TX}\) events with support \(>0\) among 100 optimal reconciliation samples but only 940 of these have 100% support, and the corresponding numbers for ecceTERA are 12,884 and 2088, respectively. Remarkably, as Figure 5 shows, RANGER-DTLx with \(\mathbb{TX}\) cost 4 achieves a precision of almost 50% on Dataset-1 and over 40% on Dataset-2 when using a support threshold of about 55%.