ArticleGenome biology2016
Exploiting single-cell expression to characterize co-expression replicability.
Article in Genome biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 3 of them syntheses that pooled it.
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Who cites it
46 citing papers in PubMed, 3 syntheses or guidelines pooled it, 94 citations in OpenAlex.
- Meta-analysis of single-cell RNA sequencing co-expression in human neural organoids reveals their high variability in recapitulating primary tissue.PLoS biology · 2024Pooled it
- Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data.Genome biology · 2023Pooled it
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Context-dependent correlations mislead transcriptomic network inference in bulk and single-cell data.bioRxiv : the preprint server for biology · 2026Article
- A unified framework for selecting and evaluating cell-type-specific gene co-expressions in single-cell data.Briefings in bioinformatics · 2026Article
- Informed spatially aware patterns for multiplexed immunofluorescence data.Scientific reports · 2026Article
- MINI-EX Version 2: Cell-Type-Specific Gene Regulatory Network Inference Using an Integrative Single-Cell Transcriptomics Approach.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Cell type heterogeneity in gene co-expression networks: implications for toxicological research.Briefings in bioinformatics · 2025Review
- GSNCASCR: An R Package to Identify Differentially Co-Expressed Curated Gene Sets with Single-Cell RNA-Seq Data.International journal of molecular sciences · 2025Article
- Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics.PLoS computational biology · 2025Article
- Myelin regulatory factor (MYRF) is a critical early regulator of retinal pigment epithelial development.PLoS genetics · 2025Article
- Gene set optimization for single cell transcriptomics.Computational intelligence methods for bioinformatics and biostatistics : ... international meeting, CIBB ... : revised selected papers. CIBB (Meeting) · 2025Article
- Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers.Nature communications · 2024Article
- Leveraging cell type-specificity for gene set analysis of single cell transcriptomics.bioRxiv : the preprint server for biology · 2024Article
- Massively integrated coexpression analysis reveals transcriptional regulation, evolution and cellular implications of the yeast noncanonical translatome.Genome biology · 2024Article
- Stem-cell states converge in multistage cutaneous squamous cell carcinoma development.Science (New York, N.Y.) · 2024Article
- Preservation of co-expression defines the primary tissue fidelity of human neural organoids.bioRxiv : the preprint server for biology · 2023Article
- Cell-type-specific co-expression inference from single cell RNA-sequencing data.Nature communications · 2023Article
- Cell-type-specific co-expression inference from single cell RNA-sequencing data.bioRxiv : the preprint server for biology · 2022Article
- SpaceX: gene co-expression network estimation for spatial transcriptomics.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundCo-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.
resultsWe 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.
conclusionsTechnical 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.