ArticleCell systems2018
Rare Cell Detection by Single-Cell RNA Sequencing as Guided by Single-Molecule RNA FISH.
Article in Cell systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
What it found
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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.
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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.
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Who cites it
76 citing papers in PubMed.
- Single-cell Transcriptomics in Autoimmune Diseases: Mechanistic Insights, Cross-disease Convergence, and Translational Challenges in Rheumatoid Arthritis, Systemic Lupus Erythematosus, Type 1 Diabetes, and Multiple Sclerosis.Clinical reviews in allergy & immunology · 2026Review
- scMagnifier: Resolving fine-grained cell subtypes via GRN-informed perturbations and consensus clustering.PLoS computational biology · 2026Article
- Highly Constrained Kinetic Models for Single-Cell Gene Expression Analysis.bioRxiv : the preprint server for biology · 2026Article
- Base editing and nanoparticle transfection of airway cell types essential for treatment of cystic fibrosis.JCI insight · 2026Article
- Article
- Imputing missing values in single-cell RNA-sequencing data: a statistical and machine learning-based approach.Briefings in bioinformatics · 2026Article
- Base editing and nanoparticle transfection of airway cell types essential for treatment of cystic fibrosis.bioRxiv : the preprint server for biology · 2025Article
- mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis.Nature communications · 2025Article
- scPerb: Predict single-cell perturbation via style transfer-based variational autoencoder.Journal of advanced research · 2025Article
- Mitigating Cell Cycle Effects in Multi-Omics Data: Solutions and Analytical Frameworks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Cell type heterogeneity in gene co-expression networks: implications for toxicological research.Briefings in bioinformatics · 2025Review
- Deep learning identifies heterogeneous subpopulations in breast cancer cell lines.npj biomedical innovations · 2025Article
- Single-cell RNA sequencing algorithms underestimate changes in transcriptional noise compared to single-molecule RNA imaging.Cell reports methods · 2024Article
- Gene choice in cancer cells is exclusive in ion transport but concurrent in DNA replication.Computational and structural biotechnology journal · 2024Article
- Method of moments framework for differential expression analysis of single-cell RNA sequencing data.Cell · 2024Article
- Leveraging gene correlations in single cell transcriptomic data.BMC bioinformatics · 2024Article
- Answering open questions in biology using spatial genomics and structured methods.BMC bioinformatics · 2024Review
- Quantitative comparison of single-cell RNA sequencing versus single-molecule RNA imaging for quantifying transcriptional noise.bioRxiv : the preprint server for biology · 2024Article
- Feature selection followed by a novel residuals-based normalization that includes variance stabilization simplifies and improves single-cell gene expression analysis.BMC bioinformatics · 2024Article
- Optimal sequencing budget allocation for trajectory reconstruction of single cells.Bioinformatics (Oxford, England) · 2024Article
16 more citing papers are in PubMed but not listed here.
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9 authors.
Funding
Abstract
Although single-cell RNA sequencing can reliably detect large-scale transcriptional programs, it is unclear whether it accurately captures the behavior of individual genes, especially those that express only in rare cells. Here, we use single-molecule RNA fluorescence in situ hybridization as a gold standard to assess trade-offs in single-cell RNA-sequencing data for detecting rare cell expression variability. We quantified the gene expression distribution for 26 genes that range from ubiquitous to rarely expressed and found that the correspondence between estimates across platforms improved with both transcriptome coverage and increased number of cells analyzed. Further, by characterizing the trade-off between transcriptome coverage and number of cells analyzed, we show that when the number of genes required to answer a given biological question is small, then greater transcriptome coverage is more important than analyzing large numbers of cells. More generally, our report provides guidelines for selecting quality thresholds for single-cell RNA-sequencing experiments aimed at rare cell analyses.
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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.