ArticleCell2024
Private information leakage from single-cell count matrices.
Article in Cell, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards a consensus atlas of human and mouse adipose tissue at single-cell resolution.Nature metabolism · 2025Guideline
- Benchmarking biomedical foundation models.Nature methods · 2026Review
- Clifti-GPT: privacy-preserving federated fine-tuning and transferable inference of foundation models on clinical single-cell data.BioData mining · 2026Article
- Cancer drug response and resistance: molecular mechanisms and combating strategies.Signal transduction and targeted therapy · 2026Review
- scDesignPop generates realistic population-scale single-cell RNA-seq for power analysis, benchmarking, and privacy protection.bioRxiv : the preprint server for biology · 2026Article
- SwarmMAP: swarm learning for decentralized cell type annotation in single cell sequencing data.NPJ systems biology and applications · 2026Article
- Leveraging Single-Cell Technologies to Advance Understanding of Myocardial Disease.Circulation research · 2026Review
- FedscGen: privacy-preserving federated batch effect correction of single-cell RNA sequencing data.Genome biology · 2025Article
- Application of single-cell and spatial omics in deciphering cellular hallmarks of cancer drug response and resistance.Journal of hematology & oncology · 2025Review
- Data standards for single-cell RNA-sequencing of paediatric cancer.Clinical & translational immunology · 2025Review
- Ethical considerations on the use of big data and artificial intelligence in kidney research from the ERA ethics committee.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2025Review
- SwarmMAP: Swarm Learning for Decentralized Cell Type Annotation in Single Cell Sequencing Data.bioRxiv : the preprint server for biology · 2025Article
- Single-cell sequencing in molecular diagnostics: Transformative yet untapped potential.Frontiers in genetics · 2025Review
- Privacy of single-cell gene expression data.Patterns (New York, N.Y.) · 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
7 authors.
Funding
Abstract
The increase in publicly available human single-cell datasets, encompassing millions of cells from many donors, has significantly enhanced our understanding of complex biological processes. However, the accessibility of these datasets raises significant privacy concerns. Due to the inherent noise in single-cell measurements and the scarcity of population-scale single-cell datasets, recent private information quantification studies have focused on bulk gene expression data sharing. To address this gap, we demonstrate that individuals in single-cell gene expression datasets are vulnerable to linking attacks, where attackers can infer their sensitive phenotypic information using publicly available tissue or cell-type-specific expression quantitative trait loci (eQTLs) information. We further develop a method for genotype prediction and genotype-phenotype linking that remains effective without relying on eQTL information. We show that variants from one study can be exploited to uncover private information about individuals in another study.
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What Socratic 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 Socratic graph.