This is a deterministic implementation of PhenoGraph. You can now provide a seed to the Louvain-function to ensure the exact same results. The PhenoGraph code was largely adopted from "tfmodisco"
PhenoGraph is a clustering method designed for high-dimensional single-cell data. It works by creating a graph ("network") representing phenotypic similarities between cells and then identifying communities in this graph.
This software package includes compiled binaries that run community detection based on C++ code written by E. Lefebvre and J.-L. Guillaume in 2008 ("Louvain method"). The code has been altered to interface more efficiently with the Python code here. It should work on reasonably current Linux, Mac and Windows machines.
To run basic clustering (default seed=1234) :
from phenograph.cluster import cluster
communities, graph, Q = cluster(data)
Another example where we change the seed:
from phenograph.cluster import cluster
communities, graph, Q = cluster(data, k=20, primary_metric='minkowski', seed=20220204, n_jobs= 1)
For a dataset of N rows, communities will be a length N vector of integers specifying a community assignment for each row in the data. Any rows assigned -1 were identified as outliers and should not be considered as a member of any community. graph is a N x N scipy.sparse matrix representing the weighted graph used for community detection.
Q is the modularity score for communities as applied to graph.
- The leiden algorithm is not implemented in this version.
- Multiprocessing doesn't work at the moment
If you use PhenoGraph in work you publish, please cite our publication:
@article{Levine_PhenoGraph_2015,
doi = {10.1016/j.cell.2015.05.047},
url = {http://dx.doi.org/10.1016/j.cell.2015.05.047},
year = {2015},
month = {jul},
publisher = {Elsevier {BV}},
volume = {162},
number = {1},
pages = {184--197},
author = {Jacob H. Levine and Erin F. Simonds and Sean C. Bendall and Kara L. Davis and El-ad D. Amir and Michelle D. Tadmor and Oren Litvin and Harris G. Fienberg and Astraea Jager and Eli R. Zunder and Rachel Finck and Amanda L. Gedman and Ina Radtke and James R. Downing and Dana Pe'er and Garry P. Nolan},
title = {Data-Driven Phenotypic Dissection of {AML} Reveals Progenitor-like Cells that Correlate with Prognosis},
journal = {Cell}
}