Exploiting single-cell expression to characterize co-expression replicability.

ABSTRACT:

Background

Co-expression networks have been a useful tool for functional genomics, providing important clues about the cellular and biochemical mechanisms that are active in normal and disease processes. However, co-expression analysis is often treated as a black box with results being hard to trace to their basis in the data. Here, we use both published and novel single-cell RNA sequencing (RNA-seq) data to understand fundamental drivers of gene-gene connectivity and replicability in co-expression networks.

Results

We perform the first major analysis of single-cell co-expression, sampling from 31 individual studies. Using neighbor voting in cross-validation, we find that single-cell network connectivity is less likely to overlap with known functions than co-expression deriv

SUBMITTER: Crow M 

PROVIDER: S-EPMC4862082 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature