ArticleGenome biology2018
UMI-count modeling and differential expression analysis for single-cell RNA sequencing.
Article in Genome biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 1 of them a synthesis that pooled it.
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.
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.
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Who cites it
68 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Compound models and Pearson residuals for single-cell RNA-seq data without UMIs.Genome biology · 2026Article
- scZGA: a novel model based on ZINB distribution and graph attention for scRNA-seq data clustering.BMC bioinformatics · 2026Article
- CSsingle: a unified tool for robust decomposition of bulk and spatial transcriptomic data across diverse single-cell references.Nucleic acids research · 2026Article
- Evaluating imputation methods for accurate estimation of cell population fractions in single-cell RNA sequencing.NAR genomics and bioinformatics · 2026Article
- scDesignPop generates realistic population-scale single-cell RNA-seq for power analysis, benchmarking, and privacy protection.bioRxiv : the preprint server for biology · 2026Article
- scDenorm: a denormalization tool for integrating single-cell transcriptomics data.GigaScience · 2026Article
- Bone Marrow Mesenchymal Stem Cell Hydrogel-Mediated Fibroblast Reprogramming Restores Intestinal Function in Adhesive Small Bowel Obstruction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Zinc and metallothionein dysregulation in B cells in multiple sclerosis, with disease-specific CXCR3Frontiers in immunology · 2026Article
- MRAS: Master Regulator Analysis of Alternative Splicing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data.Nucleic acids research · 2025Article
- Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders.Briefings in bioinformatics · 2025Article
- Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.Molecular systems biology · 2025Review
- Conditional Activation of c-MYC in Distinct Catecholaminergic Cells Drives Development of Neuroblastoma or Somatostatinoma.Cancer research · 2025Article
- Constraint-based modeling of bioenergetic differences between synaptic and non-synaptic components of dopaminergic neurons in Parkinson's disease.Frontiers in computational neuroscience · 2025Article
- Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Directly selecting cell-type marker genes for single-cell clustering analyses.Cell reports methods · 2024Article
- Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios.Genome biology · 2024Article
- Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine.Military Medical Research · 2024Review
- Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets.BMC genomics · 2024Review
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Read counting and unique molecular identifier (UMI) counting are the principal gene expression quantification schemes used in single-cell RNA-sequencing (scRNA-seq) analysis. By using multiple scRNA-seq datasets, we reveal distinct distribution differences between these schemes and conclude that the negative binomial model is a good approximation for UMI counts, even in heterogeneous populations. We further propose a novel differential expression analysis algorithm based on a negative binomial model with independent dispersions in each group (NBID). Our results show that this properly controls the FDR and achieves better power for UMI counts when compared to other recently developed packages for scRNA-seq analysis.
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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.