The clustering based on the cutoff 0.007 seems robust to data subsampling, as the three estimated nAUC are quite high (e.g. > 0.75).
The clustering based on the cutoff 0.007 seems robust to data subsampling, as the three estimated nAUC are quite high (e.g. > 0.75).
However, the same clustering seems less robust to data perturbation, as the 2.5% CI are quite small for the silhouette and the first Wallace coefficient, e.g. < 0.4.
However, the same clustering seems less robust to data perturbation, as the 2.5% CI are quite small for the silhouette and the first Wallace coefficient, e.g. < 0.4.
**Searching optimal clustering**
**Searching for optimal clustering**
An optimal MST-based clustering can be defined by the cutoff value that maximizes the different estimated statistics.
An optimal MST-based clustering can be defined by the cutoff value that maximizes the different estimated statistics.
By considering every branch length from the minimum spanning tree in _data.graphml_ (see above) as a putative cutoff, _MSTclust_ can be used to display in standard output these statistics in a convenient tab-delimited format (option `-t`):
By considering every branch length from the minimum spanning tree in _data.graphml_ (see above) as a putative cutoff, _MSTclust_ can be used to display in standard output these statistics in a convenient tab-delimited format (option `-t`):
