ArticleGenome biology2016
A statistical approach for identifying differential distributions in single-cell RNA-seq experiments.
Article in Genome biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 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
134 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Fail-Closed Validation of Lineage-Associated Transcript-Count Diversity After IFN-β Stimulation in a Public Human PBMC Dataset.International journal of molecular sciences · 2026Article
- Integrated Genomic and Proteomic Analysis Reveals T-B Lymphocyte Signatures in the MYCN Driven "Immune Desert" of Specific Neuroblastoma Subtypes.CNS neuroscience & therapeutics · 2026Article
- DEAPLOG: Differential Expression Analysis and Pseudo-Temporal Locating and Ordering of Genes in Single-Cell Transcriptomic Data.Interdisciplinary sciences, computational life sciences · 2026Article
- EMD-HVG: a normalization-independent method for highly variable gene selection based on Earth mover's distance.BMC bioinformatics · 2026Article
- Decoding Immune Regulation: From Genetic Variation to Mechanism Through Single-Cell Genomics.Immune network · 2026Review
- Article
- scRDiT: Generating Single-cell RNA-seq Data by Diffusion Transformers and Accelerating Sampling.Interdisciplinary sciences, computational life sciences · 2026Article
- GDSim: accurate simulation for single-cell transcriptomes based on the guided diffusion model.Briefings in bioinformatics · 2026Article
- Evaluation of statistical differential analysis methods for identification of senescent cells using single-cell transcriptomics.Cell reports methods · 2026Article
- MicroRNA gene dynamics in immune cell subpopulations during aging and atherosclerosis disease development at single-cell resolution.Genome medicine · 2025Article
- Differential detection workflows for multi-sample single-cell RNA-seq data.BMC genomics · 2025Article
- scDETECT: a novel statistical model accounting for cell type correlation in single-cell RNA-seq differential expression analysis.Briefings in bioinformatics · 2025Article
- Cluster-independent multiscale marker identification in single-cell RNA-seq data using localized marker detector (LMD).Communications biology · 2025Article
- cytoKernel: robust kernel embeddings for assessing differential expression of single-cell data.Bioinformatics (Oxford, England) · 2025Article
- DiSC: a statistical tool for fast differential expression analysis of individual-level single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2025Article
- DeepExDC interprets genomic compartmentalization changes in single-cell Hi-C data.Briefings in bioinformatics · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Method of moments framework for differential expression analysis of single-cell RNA sequencing data.Cell · 2024Article
- Theoretical framework for the difference of two negative binomial distributions and its application in comparative analysis of sequencing data.Genome research · 2024Article
74 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. We demonstrate that this framework can detect differential expression patterns under a wide range of settings. Compared to existing approaches, this method has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and can characterize those differences. The freely available R package scDD implements the approach.
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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.