Name
Abstract | ||
|---|---|---|
Motivation: New technologies allow for the elaborate measurement of different traits of single cells under genetic perturbations. These interventional data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous. Results: We developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular subpopulations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1093/bioinformatics/bty602 | BIOINFORMATICS |
Field | DocType | Volume |
Data mining,Computer science,Network analysis | Journal | 34 |
Issue | ISSN | Citations |
17 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 16 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Pirkl | 1 | 4 | 1.78 |
| Niko Beerenwinkel | 2 | 11 | 6.13 |