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

Author:

Hu HaoranORCID,Wang XinjunORCID,Feng SiteORCID,Xu ZhongliORCID,Liu Jing,Heidrich-O’Hare Elisa,Chen Yanshuo,Yue Molin,Zeng Lang,Rong ZiqiORCID,Chen TianmengORCID,Billiar TimothyORCID,Ding YingORCID,Huang Heng,Duerr Richard H.ORCID,Chen WeiORCID

Abstract

AbstractDroplet-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.

Funder

National Science Foundation

Foundation for the National Institutes of Health

Leona M. and Harry B. Helmsley Charitable Trust

Memorial Sloan-Kettering Cancer Center

Publisher

Springer Science and Business Media LLC

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