# Improved Duplication-Transfer-Loss Reconciliation with
## 4 Results
### Results on Simulated Datasets
However, as Table 4 shows, the number of \(\mathbb{TX}\) events inferred at that support level by RANGER-DTLx with \(\mathbb{TX}\) cost 4 is relatively small. The small  
_Phylogenetic placement of \(\mathbb{TX}\) event donors._ Identifying the locations on the species tree of unsampled lineages that served as donors for \(\mathbb{TX}\) events can offer important insights regarding major unsampled species lineages and can help to better understand the evolutionary histories of gene families. By design, RANGER-DTLx identifies a specific edge/lineage in the species tree where any given \(\mathbb{TX}\) event originates from. However, ecceTERA handles \(\mathbb{TX}\) events by introducing a single unsampled branch existing outside of the species tree, which represents all unsampled lineages on the species tree and serves as the donor of all \(\mathbb{TX}\) events. This makes it difficult to infer the location of unsampled donor lineages on the species tree using ecceTERA. To assess the accuracy of RANGER-DTLx in identifying/placing the unsampled species donor on the species tree, we used all \(\mathbb{TX}\) events (across all 100 samples, regardless of support) inferred by RANGER-DTLx that mapped to the correct recipient species and computed the distance on the species tree between the inferred location of the donor and the actual location of the donor (as given by Zombi). This distance is defined as the number of edges between the inferred donor lineage and the true lineage on the full species tree (including extinct lineages) as simulated by Zombi. As Table 5 shows, RANGER-DTLx performs remarkably well at inferring the location of the unsampled species donor on the species tree, identifying the exact location for almost half the \(\mathbb{TX}\) events when the \(\mathbb{TX}\) event cost is 3. Results are worse for RANGER-DTLx with \(\mathbb{TX}\) cost 4, but this is likely due to small sample sizes. These results also show that even when the donor lineage is not identified exactly, the inferred placement is often close to \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & & \multicolumn{2}{c}{**Dataset-1**} & \multicolumn{3}{c}{**Dataset-2**} \\ \hline
**Support** & **RANGER-DTLx** & **RANGER-DTLx** & **ecceTERA** & **RANGER-DTLx** & **RANGER-DTLx** & **ecceTERA** \\  & \(\mathbb{PTx}=\textbf{4}\) & \(\mathbb{PTx}=\textbf{3}\) & & \(\mathbb{PTx}=\textbf{4}\) & \(\mathbb{PTx}=\textbf{3}\) & \\ \hline \(>\)0 & 14/77 & 857/9688 & 926/9285 & 34/128 & 1475/1434 & 1554/12,884 \\ \(\geq\)25\% & 12/61 & 818/7869 & 885/8825 & 27/84 & 1416/11,141 & 1511/12,252 \\ \(\geq\)50\% & 4/10 & 445/4036 & 725/6977 & 13/34 & 874/5807 & 1257/9492 \\ \(\geq\)75\% & 1/5 & 191/1018 & 242/1427 & 8/24 & 473/1932 & 582/2651 \\ \(=\)100\% & 1/5 & 91/503 & 199/1140 & 7/23 & 248/940 & 468/2088 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of \(\mathbb{TX}\) events inferred by the different methods, along with their precision, are shown for different minimum support value cutoffs. Results are aggregated across all 500 gene tree/species tree pairs in each dataset and presented in the form \(a/b\), where \(b\) is the total number of distinct \(\mathbb{TX}\) events inferred across 100 randomly sampled optimal reconciliations for RANGER-DTLx and across the entire optimal solution space for ecceTERA for each gene tree, and \(a\) is the number of these \(\mathbb{TX}\) events that are correct. Figure 5: Impact of increasing support values on \(\mathbb{TX}\) event accuracy. The support for an event is defined as the percentage of optimal solution space that the event appears in. the actual placement of the unsampled donor. For completeness, results are also shown for ecceTERA and, as expected, the performance of ecceTERA is much worse than that of RANGER-DTLx, both for exact matches and average distance.