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A unified model-based framework for doublet or multiplet detection in single-cell multiomics data.
Hu, Haoran; Wang, Xinjun; Feng, Site; Xu, Zhongli; Liu, Jing; Heidrich-O'Hare, Elisa; Chen, Yanshuo; Yue, Molin; Zeng, Lang; Rong, Ziqi; Chen, Tianmeng; Billiar, Timothy; Ding, Ying; Huang, Heng; Duerr, Richard H; Chen, Wei.
Afiliación
  • Hu H; Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
  • Wang X; Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
  • Feng S; Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
  • Xu Z; School of Medicine, Tsinghua University, 100084, Beijing, China.
  • Liu J; School of Medicine, Tsinghua University, 100084, Beijing, China.
  • Heidrich-O'Hare E; Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, 15224, USA.
  • Chen Y; Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, 15224, USA.
  • Yue M; Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
  • Zeng L; Department of Computer Science, University of Maryland, College Park, MD, 20742, USA.
  • Rong Z; Center of Bioinformatics and Computational Biology, College Park, MD, 20740, USA.
  • Chen T; Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
  • Billiar T; Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
  • Ding Y; School of Information, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Huang H; Department of Surgery, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
  • Duerr RH; Department of Surgery, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
  • Chen W; Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
Nat Commun ; 15(1): 5562, 2024 Jul 02.
Article en En | MEDLINE | ID: mdl-38956023
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Análisis de la Célula Individual Límite: Animals / Humans Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Análisis de la Célula Individual Límite: Animals / Humans Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido