treecut | Find nodes in hierarchical clustering | Machine Learning library

treecut is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning applications. treecut has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can install using 'pip install treecut' or download it from GitHub, PyPI.

Hierarchical clustering is an important tool in mining useful relationships among multivariate biological data. However, there is no obvious way to define a set of useful, non-overlapping groups from the identified hierarchy. Most efforts have focused on different cut-off values, evaluate the relative strengths of intra- versus inter- group variances and then heuristically determine a "good" cutoff. This study introduces a more dynamic approach that extracts clades that are significantly enriched or different from other clades. Incorporating phylogenetic information removes the false positives observed in a conventional analysis thus improves the prediction of trait association. The algorithm takes two inputs, a tree model and some mapping of values for all the terminal branches. Briefly, the algorithm performs independent statistical tests on all the internal branches, and calculates the P-values for each node. At exploratory stage, the statistical tests are: 1) for quantitative values, test the difference of two groups separated by each node (student's t-test); 2) for categorical values, test the association of a particular category for the descendants of each internal node (Fisher's exact test). The candidate nodes are determined using the following rule: the P-value for the candidate node v has to be the smallest among all root-to-leaf paths that pass v. In other words, the group rooted at node v should contain the largest level of association, thus avoiding redundant clades.