Exploiting single-cell expression to characterize co-expression replicability

Crow, M., Paul, A., Ballouz, S., Huang, Z. J., Gillis, J. (2016) Exploiting single-cell expression to characterize co-expression replicability. Genome Biol, 17 (1). p. 101. ISSN 1474-760X (Electronic)1474-7596 (Linking)

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URL: http://www.ncbi.nlm.nih.gov/pubmed/27165153
DOI: 10.1186/s13059-016-0964-6

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 derived from bulk data, with functional variation within cell types strongly resembling that also occurring across cell types. To identify features and analysis practices that contribute to this connectivity, we perform our own single-cell RNA-seq experiment of 126 cortical interneurons in an experimental design targeted to co-expression. By assessing network replicability, semantic similarity and overall functional connectivity, we identify technical factors influencing co-expression and suggest how they can be controlled for. Many of the technical effects we identify are expression-level dependent, making expression level itself highly predictive of network topology. We show this occurs generally through re-analysis of the BrainSpan RNA-seq data. CONCLUSIONS: Technical properties of single-cell RNA-seq data create confounds in co-expression networks which can be identified and explicitly controlled for in any supervised analysis. This is useful both in improving co-expression performance and in characterizing single-cell data in generally applicable terms, permitting cross-laboratory comparison within a common framework.

Item Type: Paper
Uncontrolled Keywords: Autism Brain Co-expression Interneuron Meta-analysis Network Normalization RNA-seq Single cell
Subjects: bioinformatics
diseases & disorders > mental disorders > personality disorders > autism
organs, tissues, organelles, cell types and functions > organs types and functions > brain
organs, tissues, organelles, cell types and functions > cell types and functions > cell types > interneurons
organs, tissues, organelles, cell types and functions > cell types and functions > cell types > interneurons
organs, tissues, organelles, cell types and functions > cell types and functions > cell types > interneurons

Investigative techniques and equipment > assays > RNA-seq
CSHL Authors:
Communities: CSHL labs > Gillis Lab
CSHL labs > Huang lab
Depositing User: Matt Covey
Date Deposited: 29 Jul 2016 16:31
Last Modified: 07 Sep 2017 14:37
PMCID: PMC4862082
Related URLs:
Dataset ID:
URI: http://repository.cshl.edu/id/eprint/33074

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