NEMix: single-cell nested effects models for probabilistic pathway stimulation.

ABSTRACT: Nested effects models have been used successfully for learning subcellular networks from high-dimensional perturbation effects that result from RNA interference (RNAi) experiments. Here, we further develop the basic nested effects model using high-content single-cell imaging data from RNAi screens of cultured cells infected with human rhinovirus. RNAi screens with single-cell readouts are becoming increasingly common, and they often reveal high cell-to-cell variation. As a consequence of this cellular heterogeneity, knock-downs result in variable effects among cells and lead to weak average phenotypes on the cell population level. To address this confounding factor in network inference, we explicitly model the stimulation status of a signaling pathway in individual cells. We extend the fra

SUBMITTER: Siebourg-Polster J 

PROVIDER: S-EPMC4400057 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature