Identifying functional gene regulatory network phenotypes underlying single cell transcriptional variability.

ABSTRACT: Recent analysis of single-cell transcriptomic data has revealed a surprising organization of the transcriptional variability pervasive across individual neurons. In response to distinct combinations of synaptic input-type, a new organization of neuronal subtypes emerged based on transcriptional states that were aligned along a gradient of correlated gene expression. Individual neurons traverse across these transcriptional states in response to cellular inputs. However, the regulatory network interactions driving these changes remain unclear. Here we present a novel fuzzy logic-based approach to infer quantitative gene regulatory network models from highly variable single-cell gene expression data. Our approach involves developing an a priori regulatory network that is then trained against

SUBMITTER: Park J 

PROVIDER: S-EPMC4366310 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature