Clustering method based on Forest Fire Dynamics
Project description
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
or
pip install -u forest-fire-clustering
Executing program
"X" is a [sample x feature] matrix
To generate clustering:
import forest_fire_clustering.FFC as FFC
cluster_obj = FFC(X, sigma=0.1, k=300, num_permute=int(X.shape[0]/10))
cluster_obj.preprocess()
cluster_obj.fit(fire_temp=100)
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
Project details
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