ArticleCell2024
Method of moments framework for differential expression analysis of single-cell RNA sequencing data.
Article in Cell, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Improving reproducibility of differentially expressed genes in single-cell transcriptomic studies of neurodegenerative diseases through meta-analysis.Nature communications · 2025Pooled it
- Quantifying distribution shifts in single-cell data with scXMatch.Bioinformatics (Oxford, England) · 2026Article
- HopRatio: Profiling single-sample transcriptomic dysregulation using stable gene-ordering relationships.iScience · 2026Article
- Article
- From gene correlations to cell clusters: COTAN improved scRNA-seq analysis.NAR genomics and bioinformatics · 2026Article
- Decoding Immune Regulation: From Genetic Variation to Mechanism Through Single-Cell Genomics.Immune network · 2026Review
- Differential expression analysis in single-cell and spatial RNA-seq without model assumptions.Cell reports methods · 2026Article
- scMarkerGene: an interpretable neural network framework for cell-type-specific marker gene discovery.Briefings in bioinformatics · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Phase II trial with nivolumab and sorafenib in HCC identified enrichment of immunosuppressive monocytes in patients with Child-Pugh B liver dysfunction.JHEP reports : innovation in hepatology · 2026Article
- Characterizing gene perturbations in single cells via network divergence analysis.Nature communications · 2026Article
- Beyond the mean: genetic control of gene expression fidelity and dispersion.bioRxiv : the preprint server for biology · 2026Article
- Single-cell transcriptomic profiling combined with Mendelian randomization illuminates molecular drivers of bladder cancer.Molecular genetics and genomics : MGG · 2026Article
- OTMODE: an optimal transport theory-based framework for identifying differential features in single-cell multi-omics data.Bioinformatics (Oxford, England) · 2026Article
- Identification and experimental validation of CD74, PGLYRP1, and TXN as potential biomarkers in rheumatoid arthritis: an integrative bulk and ScRNA-seq study.Frontiers in immunology · 2026Article
- Differential expression analysis in single cell and spatial RNASeq without model assumptions.bioRxiv : the preprint server for biology · 2025Article
- Article
- Multidimensional decoding of colorectal cancer heterogeneity: Artificial intelligence-enabled precision exploration of single-cell and spatial transcriptomics.World journal of gastrointestinal oncology · 2025Review
- Ad hoc, post hoc and intrinsic-hoc in bioinformatics.Nature biotechnology · 2025Article
- ARTP Mutagenesis ofJournal of microbiology and biotechnology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Differential expression analysis of single-cell RNA sequencing (scRNA-seq) data is central for characterizing how experimental factors affect the distribution of gene expression. However, distinguishing between biological and technical sources of cell-cell variability and assessing the statistical significance of quantitative comparisons between cell groups remain challenging. We introduce Memento, a tool for robust and efficient differential analysis of mean expression, variability, and gene correlation from scRNA-seq data, scalable to millions of cells and thousands of samples. We applied Memento to 70,000 tracheal epithelial cells to identify interferon-responsive genes, 160,000 CRISPR-Cas9 perturbed T cells to reconstruct gene-regulatory networks, 1.2 million peripheral blood mononuclear cells (PBMCs) to map cell-type-specific quantitative trait loci (QTLs), and the 50-million-cell CELLxGENE Discover corpus to compare arbitrary cell groups. In all cases, Memento identified more significant and reproducible differences in mean expression compared with existing methods. It also identified differences in variability and gene correlation that suggest distinct transcriptional regulation mechanisms imparted by perturbations.
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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.