ArticleProceedings of the National Academy of Sciences of the United States of America2021
Constructing local cell-specific networks from single-cell data.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- Cochlear nucleus spatial transcriptomes of normal and hearing loss mice reveal a critical role of Spp1 in bushy cells.Cell research · 2026Article
- CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data.Bioinformatics (Oxford, England) · 2026Article
- A unified framework for selecting and evaluating cell-type-specific gene co-expressions in single-cell data.Briefings in bioinformatics · 2026Article
- Representation Methods of Transcriptomics with Applications in Neuroimmune Biology.bioRxiv : the preprint server for biology · 2026Article
- Scalable cell-specific coexpression networks for granular regulatory pattern discovery with NeighbourNet.Genome research · 2026Article
- Spatially varying gene regulation network inference from spatial transcriptomics.Bioinformatics advances · 2026Article
- Copula modeling of gene coexpression in single-cell RNA sequencing data.bioRxiv : the preprint server for biology · 2025Article
- Genetic Convergence Analysis of CRISPR Perturbations Deciphers Gene Functional Similarity.bioRxiv : the preprint server for biology · 2025Article
- Gene module-trait network analysis uncovers cell type specific systems and genes relevant to Alzheimer's Disease.Acta neuropathologica communications · 2025Article
- Cell-type-agnostic differential gene expression uncovers conserved principles of cellular regulation.bioRxiv : the preprint server for biology · 2025Article
- Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene coexpression networks.Genome research · 2025Article
- GSNCASCR: An R Package to Identify Differentially Co-Expressed Curated Gene Sets with Single-Cell RNA-Seq Data.International journal of molecular sciences · 2025Article
- A spectral framework to map QTLs affecting joint differential networks of gene co-expression.PLoS computational biology · 2025Article
- scMUG: deep clustering analysis of single-cell RNA-seq data on multiple gene functional modules.Briefings in bioinformatics · 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
- GEEES: inferring cell-specific gene-enhancer interactions from multi-modal single-cell data.Bioinformatics (Oxford, England) · 2024Article
- Shrinkage estimation of gene interaction networks in single-cell RNA sequencing data.BMC bioinformatics · 2024Article
- Modelling cell type-specific lncRNA regulatory network in autism with Cycle.BMC bioinformatics · 2024Article
- Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data.BMC biology · 2024Article
- Sex-Differential Gene Expression in Developing Human Cortex and Its Intersection With Autism Risk Pathways.Biological psychiatry global open science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Gene coexpression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene networks. We develop an approach, locCSN, that estimates cell-specific networks (CSNs) for each cell, preserving information about cellular heterogeneity that is lost with other approaches. LocCSN is based on a nonparametric investigation of the joint distribution of gene expression; hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of networks, which reveal gene communities better than traditional measures. Additionally, we propose downstream analysis methods using CSNs to utilize more fully the information contained within them. Repeated estimates of gene networks facilitate testing for differences in network structure between cell groups. Notably, with this approach, we can identify differential network genes, which typically do not differ in gene expression, but do differ in terms of the coexpression networks. These genes might help explain the etiology of disease. Finally, to further our understanding of autism spectrum disorder, we examine the evolution of gene networks in fetal brain cells and compare the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene coexpression.
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.