Maximum Entropy
Hub

The hub of maximum-entropy null models
for network randomization

by Maria Mircea, Mazène Hochane, Xueying Fan, 

Susana M. Chuva de Sousa Lopes, Diego Garlaschelli and Stefan Semrau

Language: R
Last update: February 2022
Networks: correlation matrices, multivariate time series
Null models: Wishart Ensemble
Github: https://github.com/semraulab/phiclust
Zenodo (GNU General Public License V3.0): https://zenodo.org/record/5785793#.Ybs5wn3MK3I 

Paper: Mircea et al. Genome Biology (2022) 23:18
Notes: a clusterability measure derived from random matrix theory that can be used to identify clusters with non-random substructure (testably leading to the discovery of previously overlooked phenotypes in single-cell transcriptomics data).

PhiClust:
a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations

The ability to discover new cell phenotypes by unsupervised clustering of single-cell transcriptomes has revolutionized biology. Currently, there is no principled way to decide whether a cluster of cells contains meaningful subpopulations that should be further resolved. Here, we present phiclust (φclust), a clusterability measure derived from random matrix theory that can be used to identify cell clusters with non- random substructure, testably leading to the discovery of previously overlooked phenotypes.

Mircea et al. Genome Biology (2022) 23:18,

https://doi.org/10.1186/s13059-021-02590-x