Implementation of a wrapper which creates unsupervised projections using LANDMark and UMAP.
The LANDMark package is needed for TreeOrdination to work. It is available at: https://github.com/jrudar/LANDMark
From PyPI:
pip install TreeOrdination
From source:
git clone https://github.com/jrudar/TreeOrdination.git
cd TreeOrdination
pip install .
# or create a virtual environment
python -m venv venv
source venv/bin/activate
pip install . from TreeOrdination import TreeOrdination
from sklearn.datasets import make_classification
#Create the dataset
X, y = make_classification(n_samples = 200, n_informative = 20)
#Give features a name
f_names = ["Feature %s" %str(i) for i in range(X.shape[0])]
tree_ord = TreeOrdination(feature_names = f_names).fit(X, y)
#This is the LANDMark embedding of the dataset. This dataset is used to train the supervised model ('supervised_clf' parameter)
landmark_embedding = tree_ord.LM_emb
#This is the UMAP projection of the LANDMark embedding
umap_projection = tree_ord.UMAP_emb
#This is the PCA projetion of the UMAP embedding
pca_projection = tree_ord.PCA_emb
Comming Soon.
When available, examples of how to use TreeOrdination will be found here.
An overview of the API can be found here.
To contribute to the development of TreeOrdination please read our contributing guide
Rudar, J., Porter, T.M., Wright, M., Golding G.B., Hajibabaei, M. LANDMark: an ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data. BMC Bioinformatics 23, 110 (2022). https://doi.org/10.1186/s12859-022-04631-z
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011;12:2825–30.
Geurts P, Ernst D, Wehenkel L. Extremely Randomized Trees. Machine Learning. 2006;63(1):3–42.
Rudar, J., Golding, G.B., Kremer, S.C., Hajibabaei, M. (2023). Decision Tree Ensembles Utilizing Multivariate Splits Are Effective at Investigating Beta Diversity in Medically Relevant 16S Amplicon Sequencing Data. Microbiology Spectrum e02065-22.