Single cell network analysis with a mixture of Nested Effects Models.

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 t

SUBMITTER: Pirkl M 

PROVIDER: S-EPMC6129288 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature