ArticleNature communications2024
A unified model-based framework for doublet or multiplet detection in single-cell multiomics data.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Semi-parametric empirical bayes method for multiplet detection in snATAC-seq with probabilistic multi-omic integration.PLoS computational biology · 2026Article
- Single-cell transcriptomics of granulocytes in asthma and atopic diseases.Frontiers in molecular biosciences · 2026Review
- Soluble fermentable dietary fiber attenuates age-related cognitive impairment via neuroimmune and antioxidant modulation: evidence from multilevel analyses in populations and aging mouse models.Frontiers in immunology · 2026Article
- YBX1 regulation of alveolar type II epithelial cells in idiopathic pulmonary fibrosis: mechanistic insights and small-molecule drug screening.Journal of translational medicine · 2025Article
- Semi-parametric Empirical Bayes Method for Multiplet Detection in snATAC-seq with Probabilistic Multi-omic Integration.bioRxiv : the preprint server for biology · 2025Article
- Article
- OmniDoublet: a method for doublet detection in multimodal single-cell sequencing data.Briefings in bioinformatics · 2025Article
- Biomimetic targeted self-adaptive nanodrug for inflammation optimization and AT2 cell modulation in precise ARDS therapy.Science advances · 2025Article
- Robust self-supervised machine learning for single cell embeddings and annotations.bioRxiv : the preprint server for biology · 2025Article
- Epigenetic control of antigen presentation failure in osteosarcoma: from single-cell chromatin maps to therapeutic strategies.Frontiers in immunology · 2025Review
- Article
- A unified model-based framework for doublet or multiplet detection in single-cell multiomics data.Nature communications · 2024Article
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Authors and funding
16 authors.
Funding
Abstract
Droplet-based single-cell sequencing techniques rely on the fundamental assumption that each droplet encapsulates a single cell, enabling individual cell omics profiling. However, the inevitable issue of multiplets, where two or more cells are encapsulated within a single droplet, can lead to spurious cell type annotations and obscure true biological findings. The issue of multiplets is exacerbated in single-cell multiomics settings, where integrating cross-modality information for clustering can inadvertently promote the aggregation of multiplet clusters and increase the risk of erroneous cell type annotations. Here, we propose a compound Poisson model-based framework for multiplet detection in single-cell multiomics data. Leveraging experimental cell hashing results as the ground truth for multiplet status, we conducted trimodal DOGMA-seq experiments and generated 17 benchmarking datasets from two tissues, involving a total of 280,123 droplets. We demonstrated that the proposed method is an essential tool for integrating cross-modality multiplet signals, effectively eliminating multiplet clusters in single-cell multiomics data-a task at which the benchmarked single-omics methods proved inadequate.
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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.