Software Open Access
Phiclust: A clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations
Maria Mircea;
Mazène Hochane;
Xueying Fan;
Susana M. Chuva de Sousa Lopes;
Diego Garlaschelli;
Stefan Semrau
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, 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.
Files
(81.8 MB)
| Name | Size | |
|---|---|---|
|
semraulab/phiclust-v0.0.1.zip
md5:c4d38a0205b3877e1ba12cb3e278c1ef |
81.8 MB | Download |
79
2
views
downloads
| All versions | This version | |
|---|---|---|
| Views | 79 | 79 |
| Downloads | 2 | 2 |
| Data volume | 163.7 MB | 163.7 MB |
| Unique views | 65 | 65 |
| Unique downloads | 2 | 2 |
Available in
Indexed in
Indexed in
- Publication date:
- December 16, 2021
- DOI:
-
- Keyword(s):
- clusterability scRNA-seq random matrix theory
- Related identifiers:
- Supplement to
- Communities:
- License (for files):
- GNU General Public License v3.0 or later