Figure 8.

Agreement of supervised and unsupervised scores (silhouette, Calinski–Harabasz, Davies-Bouldin). For datasets with a clear ground truth graph structure (binary tree with 3 branching points) MST trajectory inference algorithm is applied. Constructed tree is compared with ground truth with adjusted Rand index based score and also scored by unsupervised score (not requiring the ground truth). (By the methodologies described above). One can see that all methods suggest that choice of parameter in the 20-40 nodes is reasonable choice. So supervised and unsupervised scores agree at this example. Thus unsupervised scores may provide an indication how to choose parameters for GBDA algorithms. Each point of the plot obtained by averaging scores on hundred simulated datasets. See notebook “SimulationUnsupervisedScoresForTI” for further details.