Single-cell transcriptomics unveils gene regulatory network plasticity.

ABSTRACT:

Background

Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies.

Results

We devise a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We develop correlation metrics that are specifically tailored to single-cell data, and then generate, validate, and interpret single-cell-derived regulatory networks from organs and perturbed systems, such as diabetes and Alzheimer's disease. Using tools from graph theory, we compute an unbiased quantification of a gene's biological

SUBMITTER: Iacono G 

PROVIDER: S-EPMC6547541 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature