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

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 crossmodality 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.