Semi-parametric Empirical Bayes Method for Multiplet Detection in snATAC-seq with Probabilistic Multi-omic Integration

Abstract Multiplets, formed when multiple cells are captured in a droplet, produce hybrid molecular profiles that confound single-cell analyses. Detecting multiplets in single-nucleus ATAC-seq (snATAC-seq) data is particularly challenging due to sparsity and overdispersion of chromatin accessibility measurements. We introduce SEBULA, a semi-parametric empirical Bayes model that yields well-calibrated posterior probabilities for multiplet detection, enabling principled false discovery rate control. SEBULA also integrates probabilistic evidence with complementary signals from other modalities, such as scRNA-seq. Benchmarking on simulations and seven annotated trimodal DOGMA-seq datasets demonstrates SEBULA’s superior performance. The open-source software is computationally efficient. Competing Interest Statement JEG has served as a consultant to AbbVie, Eli Lilly, Almirall, Celgene, BMS, Janssen, Prometheus, TimberPharma, Galderma, Novatis, MiRagen, AnaptysBio and has received research support from AbbVie, SunPharma, Eli Lilly, Kyowa Kirin, Almirall, Celgene, BMS,Janssen, Prometheus, and TimberPharma. LCT has received support from Galderma and Janssen.