ReviewTrends in genetics : TIG2018
Co-expression in Single-Cell Analysis: Saving Grace or Original Sin?
Review in Trends in genetics : TIG, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses 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
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Cross-expression meta-analysis of mouse brain slices reveals coordinated gene expression across spatially adjacent cells.Genome biology · 2025Pooled it
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data.bioRxiv : the preprint server for biology · 2026Article
- Large-scale, interpretable gene regulatory network inference through biologically informed matrix factorization.bioRxiv : the preprint server for biology · 2026Article
- Causal effect estimation from trans-regulatory single-cell CRISPR screens.Cell genomics · 2026Review
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- Cell type transcriptomic modules reveal shared molecular mechanisms in Alzheimer's and Parkinson's disease.GigaScience · 2026Article
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- Cell type heterogeneity in gene co-expression networks: implications for toxicological research.Briefings in bioinformatics · 2025Review
- Cross-expression meta-analysis of 695 brain samples reveals coordinated gene expression across spatially adjacent cells.bioRxiv : the preprint server for biology · 2025Article
- scNET: learning context-specific gene and cell embeddings by integrating single-cell gene expression data with protein-protein interactions.Nature methods · 2025Article
- A graph neural network that combines scRNA-seq and protein-protein interaction data.Nature methods · 2025Article
- Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics.PLoS computational biology · 2025Article
- Predictive biomarkers for embryotoxicity: a machine learning approach to mitigating multicollinearity in RNA-Seq.Archives of toxicology · 2024Article
- Single-cell network analysis reveals gene expression programs for Arabidopsis root development and metabolism.Plant communications · 2024Article
- SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer.Cell reports methods · 2024Article
- Stem-cell states converge in multistage cutaneous squamous cell carcinoma development.Science (New York, N.Y.) · 2024Article
- Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer's disease progression.Communications biology · 2024Article
- Identification ofFrontiers in immunology · 2024Article
- Transcription factor expression is the main determinant of variability in gene co-activity.Molecular systems biology · 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
2 authors.
Funding
Abstract
As a fundamental unit of life, the cell has rightfully been the subject of intense investigation throughout the history of biology. Technical innovations now make it possible to assay cellular features at genomic scale, yielding breakthroughs in our understanding of the molecular organization of tissues, and even whole organisms. As these data accumulate we will soon be faced with a new challenge: making sense of the plethora of results. Early investigations into the replicability of cell type profiles inferred from single-cell RNA sequencing data have indicated that this is likely to be surprisingly straightforward due to consistent gene co-expression. In this opinion article we discuss the evidence for this claim and its implications for interpreting cell type-specific gene expression.
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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.