ArticleCell systems2021
Single-cell co-expression analysis reveals that transcriptional modules are shared across cell types in the brain.
Article in Cell systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- The misplaced mouse Pax6 interneuron subclass: A cross-species transcriptomic reassignment.iScience · 2026Article
- Deciphering global transcriptional dynamics coordinated by gene-gene regulatory interactions using single-cell data.Molecular systems biology · 2026Article
- A consensus atlas of human brain development defines cell type-specific maturation trajectories across the lifespan.bioRxiv : the preprint server for biology · 2026Article
- Evaluation of analysis modes for RNA coexpression in single-cell and bulk tissue.bioRxiv : the preprint server for biology · 2026Article
- DataXflowGen for GenAI-driven model generation.Scientific reports · 2026Article
- Analyzing Co-expression Networks with Network Skeleton Extraction.Research square · 2026Article
- Gene module-trait network analysis uncovers cell type specific systems and genes relevant to Alzheimer's Disease.Acta neuropathologica communications · 2025Article
- ANTIPODE Provides a Global View of Cell Type Homology and Transcriptomic Divergence in the Developing Mammalian Brain.bioRxiv : the preprint server for biology · 2025Article
- LaGrACE: estimating gene program dysregulation with latent regulatory network.Molecular systems biology · 2025Article
- Cell type heterogeneity in gene co-expression networks: implications for toxicological research.Briefings in bioinformatics · 2025Review
- Inferring gene regulatory networks by hypergraph generative model.Cell reports methods · 2025Article
- Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics.PLoS computational biology · 2025Article
- scNET: learning context-specific gene and cell embeddings by integrating single-cell gene expression data with protein-protein interactions.Nature methods · 2025Article
- COME: contrastive mapping learning for spatial reconstruction of single-cell RNA sequencing data.Bioinformatics (Oxford, England) · 2025Article
- Comparing gene-gene co-expression network approaches for the analysis of cell differentiation and specification on scRNAseq data.Computational and structural biotechnology journal · 2025Article
- Exploring Lignin Biosynthesis Genes in Rice: Evolution, Function, and Expression.International journal of molecular sciences · 2024Article
- Gene regulatory networks in disease and ageing.Nature reviews. Nephrology · 2024Review
- Individual variation in preference behavior in sailfin fish refines the neurotranscriptomic pathway for mate preference.Ecology and evolution · 2023Article
- scHumanNet: a single-cell network analysis platform for the study of cell-type specificity of disease genes.Nucleic acids research · 2023Article
- The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest.Nucleic acids research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Gene-gene relationships are commonly measured via the co-variation of gene expression across samples, also known as gene co-expression. Because shared expression patterns are thought to reflect shared function, co-expression networks describe functional relationships between genes, including co-regulation. However, the heterogeneity of cell types in bulk RNA-seq samples creates connections in co-expression networks that potentially obscure co-regulatory modules. The brain initiative cell census network (BICCN) single-cell RNA sequencing (scRNA-seq) datasets provide an unparalleled opportunity to understand how gene-gene relationships shape cell identity. Comparison of the BICCN data (500,000 cells/nuclei across 7 BICCN datasets) with that of bulk RNA-seq networks (2,000 mouse brain samples across 52 studies) reveals a consistent topology reflecting a shared co-regulatory signal. Differential signals between broad cell classes persist in driving variation at finer levels, indicating that convergent regulatory processes affect cell phenotype at multiple scales.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.