ArticleNucleic acids research2019
Cell-specific network constructed by single-cell RNA sequencing data.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 papers, 1 of them a synthesis that pooled it.
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
125 citing papers in PubMed, 1 synthesis or guideline pooled it, 199 citations in OpenAlex.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- CSCN: inference of cell-specific causal networks using single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2026Article
- HNPP: Higher-order network-based personalized PageRank for detecting critical phase in complex biological systems.PLoS computational biology · 2026Article
- CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data.Bioinformatics (Oxford, England) · 2026Article
- Cell line-specific gene network enrichment analysis for interpreting continuous phenotypes.Briefings in bioinformatics · 2026Article
- MicroSSNet: an R package for microbial network construction and analysis at the single-sample and aggregated levels.BMC bioinformatics · 2026Article
- Scalable cell-specific coexpression networks for granular regulatory pattern discovery with NeighbourNet.Genome research · 2026Article
- A gene interaction network-based molecular taxonomy of dilated cardiomyopathy highlighting immune and inflammatory heterogeneity.Clinical and experimental medicine · 2026Article
- Integrated spatial transcriptomics and pan-cancer XGBoost modeling uncover spatial drivers of immune exclusion and predict immunotherapy response.Cancer immunology, immunotherapy : CII · 2026Article
- Challenges and Opportunities in Single-Sample Network Modeling.bioRxiv : the preprint server for biology · 2026Article
- LnCeVar 2.0: an updated resource and web tools for genomic variations disrupting ceRNA networks from single-cell/spatial transcriptomics data.Nucleic acids research · 2026Article
- Spatially varying gene regulation network inference from spatial transcriptomics.Bioinformatics advances · 2026Article
- A squamous epithelial gene interaction perturbation network index for risk stratification in esophageal squamous cell carcinoma.Journal of Cancer · 2026Article
- Deciphering the interplay between inflammation and dysregulated autophagy in lupus nephritis through network analysis and experimental validation.Frontiers in cell and developmental biology · 2026Article
- The validation of quality attributes in Primary Human Hepatocytes Standard.Cell regeneration (London, England) · 2025Article
- Neuroactive network tissue based on dual-factor neuroregenerative bioactive coating scaffolds and neural stem cells for spinal cord injury repair.Materials today. Bio · 2025Article
- ScReNI: Single-cell Regulatory Network Inference Through Integrating scRNA-seq and scATAC-seq Data.Genomics, proteomics & bioinformatics · 2025Article
- Progress and applications of single-cell RNA sequencing and spatial transcriptome technology in acute kidney injury research.Molecular therapy. Nucleic acids · 2025Review
- Metabolic genes interaction perturbation network identified and validated CD24 as a novel prognostic gene in hepatocellular carcinoma.Discover oncology · 2025Article
- Non-coding molecular determinants of polycystic ovary syndrome: A future treatment potential.Journal of assisted reproduction and genetics · 2025Review
65 more citing papers are in PubMed but not listed here.
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA sequencing (scRNA-seq) is able to give an insight into the gene-gene associations or transcriptional networks among cell populations based on the sequencing of a large number of cells. However, traditional network methods are limited to the grouped cells instead of each single cell, and thus the heterogeneity of single cells will be erased. We present a new method to construct a cell-specific network (CSN) for each single cell from scRNA-seq data (i.e. one network for one cell), which transforms the data from 'unstable' gene expression form to 'stable' gene association form on a single-cell basis. In particular, it is for the first time that we can identify the gene associations/network at a single-cell resolution level. By CSN method, scRNA-seq data can be analyzed for clustering and pseudo-trajectory from network perspective by any existing method, which opens a new way to scRNA-seq data analyses. In addition, CSN is able to find differential gene associations for each single cell, and even 'dark' genes that play important roles at the network level but are generally ignored by traditional differential gene expression analyses. In addition, CSN can be applied to construct individual network of each sample bulk RNA-seq data. Experiments on various scRNA-seq datasets validated the effectiveness of CSN in terms of accuracy and robustness.
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.