ArticleNature biotechnology2021
Bayesian inference of gene expression states from single-cell RNA-seq data.
Article in Nature biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Article
- Coordinated dysregulation of modular gene activity in human neuropathologies.bioRxiv : the preprint server for biology · 2026Article
- Bonsai reconstructs tree representations for distortion-free visualization and exploration of high-dimensional data.Nature biotechnology · 2026Article
- Compound models and Pearson residuals for single-cell RNA-seq data without UMIs.Genome biology · 2026Article
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- scDenorm: a denormalization tool for integrating single-cell transcriptomics data.GigaScience · 2026Article
- Convergent mutation trajectories convert functional self-tolerance in IGHV4-34 B cells to genetic tolerance encoded in the antibody.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Reconstructing Waddington's landscape from data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Multi-cellular phenotypic dynamics during the progression of an immunocompetent breast cancer model.iScience · 2025Article
- A fibroblast-centric network drives cold fibrosis in the tumor microenvironment of lung squamous cell carcinoma.bioRxiv : the preprint server for biology · 2025Article
- Cold and hot fibrosis define clinically distinct cardiac pathologies.Cell systems · 2025Article
- Principled PCA separates signal from noise in omics count data.bioRxiv : the preprint server for biology · 2025Article
- Diffusion Smart-seq3 of breast cancer spheroids to explore spatial tumor biology and test evolutionary principles of tumor heterogeneity.Scientific reports · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Cell-specific priors rescue differential gene expression in spatial spot-based technologies.Briefings in bioinformatics · 2024Article
- Scalable identification of lineage-specific gene regulatory networks from metacells with NetID.Genome biology · 2024Article
- Physical modeling of embryonic transcriptomes identifies collective modes of gene expression.bioRxiv : the preprint server for biology · 2024Article
- Deconstructing Intratumoral Heterogeneity through Multiomic and Multiscale Analysis of Serial Sections.Cancers · 2024Article
- Identifying cell states in single-cell RNA-seq data at statistically maximal resolution.PLoS computational biology · 2024Article
- A Systematic Overview of Single-Cell Transcriptomics Databases, their Use cases, and Limitations.ArXiv · 2024Article
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3 authors.
Funding
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Abstract
Despite substantial progress in single-cell RNA-seq (scRNA-seq) data analysis methods, there is still little agreement on how to best normalize such data. Starting from the basic requirements that inferred expression states should correct for both biological and measurement sampling noise and that changes in expression should be measured in terms of fold changes, we here derive a Bayesian normalization procedure called Sanity (SAmpling-Noise-corrected Inference of Transcription activitY) from first principles. Sanity estimates expression values and associated error bars directly from raw unique molecular identifier (UMI) counts without any tunable parameters. Using simulated and real scRNA-seq datasets, we show that Sanity outperforms other normalization methods on downstream tasks, such as finding nearest-neighbor cells and clustering cells into subtypes. Moreover, we show that by systematically overestimating the expression variability of genes with low expression and by introducing spurious correlations through mapping the data to a lower-dimensional representation, other methods yield severely distorted pictures of the data.
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