forest-fire-clustering
Clustering Method Inspired by Forest Fire Dynamics
Description
Forest Fire Clustering is an efficient and interpretable clustering method for discovering and validating cell types in single-cell sequencing analysis. Different than the existing methods, our clustering algorithm makes minimal prior assumptions about the data and provides point-wise posterior probabilities for internal validation. Additionally, it computes point-wise label entropies that can highlight novel transition cell types de novo along developmental pseudo-time manifolds. Lastly, our inductive algorithm is able to make robust inferences in an online-learning context.
Getting Started
Dependencies
- python >= 3.6
- numpy
- scipy
- scikit-learn
- numba
Installing
Estimated time: 2 mins
pip install forest-fire-clustering
Executing program
"X" is a [sample x feature] matrix
To generate clustering:
from forest_fire_clustering.forest_fire_clustering import FFC
cluster_obj = FFC()
cluster_obj.preprocess(X)
cluster_obj.fit(fire_temp=100)
cluster_obj.cluster_labels
To validate the results:
cluster_obj.validate()
cluster_obj.entropy()
cluster_obj.pval()
cluster_obj.entropy_list # list of entropies of the data point
cluster_obj.pval_list # list of posterior significance values
Authors
Zhanlin Chen, Jeremy Goldwasser, Philip Tuckman, Jing Zhang, Mark Gerstein
1 maintainer
Release files for forest-fire-clustering 0.0.25
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Source distribution (sdist)
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|---|---|---|---|---|
| forest_fire_clustering-0.0.25-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 11.1 kB
Release files / forest_fire_clustering-0.0.25.tar.gz
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