ArticleComputational and structural biotechnology journal2023
scDemultiplex: An iterative beta-binomial model-based method for accurate demultiplexing with hashtag oligos.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A High-Fiber, Plant-Based Diet in Myeloma Precursor Disorders: Results from the NUTRIVENTION Clinical Trial and Preclinical Vk*MYC Model.Cancer discovery · 2026Article
- A Multispecies, Modality-Agnostic Scalable In Vivo Mosaic Screening Platform for Therapeutic Target Discovery.bioRxiv : the preprint server for biology · 2026Article
- Methodologies for Sample Multiplexing and Computational Deconvolution in Single-Cell Sequencing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Mapping CMV-related immune signatures in blood, aorta and perivascular mediastinal adipose tissue.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Intelligent biology and medicine: Accelerating innovative computational approaches.Computational and structural biotechnology journal · 2025Article
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Authors and funding
7 authors.
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Abstract
Single-cell sequencing have been widely used to characterize cellular heterogeneity. Sample multiplexing where multiple samples are pooled together for single-cell experiments, attracts wide attention due to its benefits of increasing capacity, reducing costs, and minimizing batch effects. To analyze multiplexed data, the first crucial step is to demultiplex, the process of assigning cells to individual samples. Inaccurate demultiplexing will create false cell types and result in misleading characterization. We propose scDemultiplex, which models hashtag oligo (HTO) counts with beta-binomial distribution and uses an iterative strategy for further refinement. Compared with seven existing demultiplexing approaches, scDemultiplex achieved great performance in both high-quality and low-quality data. Additionally, scDemultiplex can be combined with other approaches to improve their performance.
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Registered trials
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