ReviewNature genetics2021
Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis.
Review in Nature genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 138 papers, 1 of them a synthesis that pooled it.
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Who cites it
138 citing papers in PubMed, 1 synthesis or guideline pooled it.
- PancrESS - a meta-analysis resource for understanding cell-type specific expression in the human pancreas.BMC genomics · 2024Pooled it
- RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.Nature communications · 2026Article
- Single-cell and spatial transcriptomics identify CAPG as a key driver of cisplatin resistance in bladder cancer.Functional & integrative genomics · 2026Article
- Lineage-aware stochastic modeling reveals gene-expression dynamics in development and disease.bioRxiv : the preprint server for biology · 2026Article
- Spatial co-expression and cell-cell communication inference from spatially resolved transcriptomics with CONCISE.bioRxiv : the preprint server for biology · 2026Article
- Compound models and Pearson residuals for single-cell RNA-seq data without UMIs.Genome biology · 2026Article
- ST-LDAW: A Topic-Model and Damped Weighted Least-Squares Method for Integrative Deconvolution of Single-Cell and Spatial Transcriptomics.Interdisciplinary sciences, computational life sciences · 2026Article
- High-Dimensional Sensitivity Analysis for Genomic Studies: An Adversarial Framework for Learning Worst-Case Latent Confounders.bioRxiv : the preprint server for biology · 2026Article
- Highly Constrained Kinetic Models for Single-Cell Gene Expression Analysis.bioRxiv : the preprint server for biology · 2026Article
- A Tree-Based Model for Addressing Sparsity and Taxa Covariance in Microbiome Compositional Count Data.Statistics in medicine · 2026Article
- Physiological architecture and evolutionary origins of cellular adaptability.bioRxiv : the preprint server for biology · 2026Article
- Quantifying stability via count splitting to guide model selection in RNA velocity analyses.bioRxiv : the preprint server for biology · 2026Article
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies.Nature communications · 2026Article
- Article
- Uncovering causal relationships in single-cell omic studies with causarray.Briefings in bioinformatics · 2026Article
- Distinguishing causal from tagging enhancers using single-cell multiome data.medRxiv : the preprint server for health sciences · 2026Article
- A Manifold-Based Measure of Transcriptional Entropy for Quantifying Aging in Single Cells.bioRxiv : the preprint server for biology · 2026Article
- A negative binomial latent factor model for paired microbiome sequencing data.BMC bioinformatics · 2026Article
- NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.Biostatistics (Oxford, England) · 2026Article
78 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
The high proportion of zeros in typical single-cell RNA sequencing datasets has led to widespread but inconsistent use of terminology such as dropout and missing data. Here, we argue that much of this terminology is unhelpful and confusing, and outline simple ideas to help to reduce confusion. These include: (1) observed single-cell RNA sequencing counts reflect both true gene expression levels and measurement error, and carefully distinguishing between these contributions helps to clarify thinking; and (2) method development should start with a Poisson measurement model, rather than more complex models, because it is simple and generally consistent with existing data. We outline how several existing methods can be viewed within this framework and highlight how these methods differ in their assumptions about expression variation. We also illustrate how our perspective helps to address questions of biological interest, such as whether messenger RNA expression levels are multimodal among cells.
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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.