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
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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for forest_fire_clustering-0.0.25.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 070c63a1c6ef24401986fc4d4a91c4f2dbc9e61aeb2b3e063144c7d89c85a800 |
|
MD5 | ee132b1c1b10199683c79544d09793c4 |
|
BLAKE2b-256 | b8fddf300163f7ca7e6950f01be1bae851335882fe59d141144013f509d3c413 |
Hashes for forest_fire_clustering-0.0.25-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 238c8c9d90ba2cf0bf911665b98e6eac0804df5565e93b8354215f46c056ae90 |
|
MD5 | 3f79d3a0684e83a792f0bfc309dbc128 |
|
BLAKE2b-256 | 10d55681ccded786ef221ff5fb84ddab29dfc973538c1fad6abb1a96724a365e |