ArticleNature communications2026
Efficient differential expression analysis of large-scale single-cell transcriptomics data using Dreamlet.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Single-nucleus atlas of cell-type specific genetic regulation in the human brain.Nature genetics · 2026Article
- Single-cell analysis of chromatin accessibility in the human intestine identifies regulatory programs and clarifies genetic associations in Crohn's disease.Nature genetics · 2026Article
- A single-nuclei multiomics resource across four brain regions prioritises human neural cell types influencing brain-related traits.bioRxiv : the preprint server for biology · 2026Article
- Fast, flexible analysis of differences in cellular composition with crumblr.Nature communications · 2026Article
- Protocol for classifying cellular senescence from single-cell and single-nucleus RNA sequencing data.STAR protocols · 2026Article
- Single-nucleus analysis of the adult human olfactory epithelium uncovers shared neurogenesis programs with the brain.Nature communications · 2026Article
- Single-cell transcriptomic atlas of frontoinsular cortex reveals molecular correlates of selective neuronal vulnerability in FTD.bioRxiv : the preprint server for biology · 2026Article
- Developmental signaling reveals functionally enriched human-specific gene regulation in telencephalic progenitors.bioRxiv : the preprint server for biology · 2026Article
- Scalable, fast and accurate differential gene expression testing from millions of cells of multiple patients.Nature communications · 2026Article
- Charting the human-specific properties of gene expression networks in the infant prefrontal cortex.Science advances · 2026Article
- Differential expression analysis in single-cell and spatial RNA-seq without model assumptions.Cell reports methods · 2026Article
- Single-nucleus profiling reveals age-associated remodeling opposed by parity in the postmenopausal human ovary.bioRxiv : the preprint server for biology · 2026Article
- Gene regulatory programs of cognitive resilience and pathogenesis in Alzheimer's disease.medRxiv : the preprint server for health sciences · 2026Article
- MEF2C controls lysosomal and lipid clearance programs linked to Alzheimer's disease risk in macrophages.Research square · 2026Article
- ModelingbioRxiv : the preprint server for biology · 2026Article
- Beyond the mean: genetic control of gene expression fidelity and dispersion.bioRxiv : the preprint server for biology · 2026Article
- Single-cell spatial transcriptomic analysis of human skin anatomy.Nature genetics · 2026Article
- Mucosal Inflammation Shapes Human Neutrophil States in Tissue and Circulation.bioRxiv : the preprint server for biology · 2026Article
- Systematic evaluation of single-cell multimodal data integration enhances cell type resolution and discovery of clinically relevant states in complex tissues.Genome biology · 2026Article
- Single-cell transcriptomics of multi-site cell therapy in osteoarthritis: Tissue-specific treatment correlations.Molecular therapy. Nucleic acids · 2026Article
Corrections and comments
- Update of
- Update of
Authors and funding
15 authors.
Funding
Abstract
Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet ( DiseaseNeurogenomics.github.io/dreamlet ) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4 M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.
Indexed as
Identifiers
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.