Single cell network analysis with a mixture of Nested Effects Models.
Bioinformatics
; 34(17): i964-i971, 2018 09 01.
Article
em En
| MEDLINE
| ID: mdl-30423100
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. Availability and implementation The mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https//github.com/cbg-ethz/mnem/. Supplementary information Supplementary data are available at Bioinformatics online.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Análise de Célula Única
Limite:
Humans
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
Suíça