ArticleNature communications2020
Flexible experimental designs for valid single-cell RNA-sequencing experiments allowing batch effects correction.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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Who cites it
13 citing papers in PubMed.
- Single-cell Transcriptomics in Autoimmune Diseases: Mechanistic Insights, Cross-disease Convergence, and Translational Challenges in Rheumatoid Arthritis, Systemic Lupus Erythematosus, Type 1 Diabetes, and Multiple Sclerosis.Clinical reviews in allergy & immunology · 2026Review
- Moderated designs can balance between batch-effect mitigation and cell loss due to hashtag-assisted pooling in single-cell experiments.Genome research · 2026Article
- Correcting spatial transcriptomics data affected by a prevalent transcript leakage problem across platforms, species, and tissues.bioRxiv : the preprint server for biology · 2026Article
- Recovery of biological signals lost in single-cell batch integration with CellANOVA.Nature biotechnology · 2025Article
- Moderated designs can balance between batch-effect mitigation and cell loss due to hashtag-assisted pooling in single-cell experiments.bioRxiv : the preprint server for biology · 2025Article
- Immune Profiling among Colorectal Cancer Subtypes using Dependent Mixture Models.Journal of the American Statistical Association · 2025Article
- The impact of dropouts in scRNAseq dense neighborhood analysis.Computational and structural biotechnology journal · 2025Article
- Cerebellar contributions to a brainwide network for flexible behavior in mice.Communications biology · 2023Article
- Applications of single-cell RNA sequencing in drug discovery and development.Nature reviews. Drug discovery · 2023Review
- EDClust: an EM-MM hybrid method for cell clustering in multiple-subject single-cell RNA sequencing.Bioinformatics (Oxford, England) · 2022Article
- Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision.Briefings in bioinformatics · 2021Article
- Applications of single-cell genomics and computational strategies to study common disease and population-level variation.Genome research · 2021Article
- RNA sequencing: new technologies and applications in cancer research.Journal of hematology & oncology · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite their widespread applications, single-cell RNA-sequencing (scRNA-seq) experiments are still plagued by batch effects and dropout events. Although the completely randomized experimental design has frequently been advocated to control for batch effects, it is rarely implemented in real applications due to time and budget constraints. Here, we mathematically prove that under two more flexible and realistic experimental designs-the reference panel and the chain-type designs-true biological variability can also be separated from batch effects. We develop Batch effects correction with Unknown Subtypes for scRNA-seq data (BUSseq), which is an interpretable Bayesian hierarchical model that closely follows the data-generating mechanism of scRNA-seq experiments. BUSseq can simultaneously correct batch effects, cluster cell types, impute missing data caused by dropout events, and detect differentially expressed genes without requiring a preliminary normalization step. We demonstrate that BUSseq outperforms existing methods with simulated and real data.
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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.