Integrated single cell data analysis reveals cell specific networks and novel coactivation markers.

ABSTRACT:

Background

Large scale single cell transcriptome profiling has exploded in recent years and has enabled unprecedented insight into the behavior of individual cells. Identifying genes with high levels of expression using data from single cell RNA sequencing can be useful to characterize very active genes and cells in which this occurs. In particular single cell RNA-Seq allows for cell-specific characterization of high gene expression, as well as gene coexpression.

Results

We offer a versatile modeling framework to identify transcriptional states as well as structures of coactivation for different neuronal cell types across multiple datasets. We employed a gamma-normal mixture model to identify active gene expression across cells, and used these to characterize markers for olf

SUBMITTER: Ghazanfar S 

PROVIDER: S-EPMC5249008 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature