A unified model-based framework for doublet or multiplet detection in single-cell multiomics data

Haoran Hu, Xinjun Wang, Site Feng, Zhongli Xu, Jing Liu, Elisa Heidrich-O’Hare, Yanshuo Chen, Molin Yue, Lang Zeng, Ziqi Rong, Tianmeng Chen, Timothy Billiar, Ying Ding, Heng Huang, Richard H. Duerr () and Wei Chen ()
Additional contact information
Haoran Hu: University of Pittsburgh
Xinjun Wang: Memorial Sloan Kettering Cancer Center
Site Feng: University of Pittsburgh
Zhongli Xu: Tsinghua University
Jing Liu: University of Pittsburgh
Elisa Heidrich-O’Hare: University of Pittsburgh
Yanshuo Chen: University of Maryland
Molin Yue: University of Pittsburgh
Lang Zeng: University of Pittsburgh
Ziqi Rong: University of Michigan
Tianmeng Chen: University of Pittsburgh
Timothy Billiar: University of Pittsburgh
Ying Ding: University of Pittsburgh
Heng Huang: University of Maryland
Richard H. Duerr: University of Pittsburgh
Wei Chen: University of Pittsburgh

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-49448-x

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