AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data

• Main Authors: Asa Thibodeau, Alper Eroglu, Christopher S. McGinnis, Nathan Lawlor, Djamel Nehar-Belaid, Romy Kursawe, Radu Marches, Daniel N. Conrad, George A. Kuchel, Zev J. Gartner, Jacques Banchereau, Michael L. Stitzel, A. Ercument Cicek, Duygu Ucar
• Format: Article
• Language: English
• Published: BMC 2021-09-01
• Series: Genome Biology
• Subjects: Multiplets; Doublets; Single nucleus ATAC-seq; snATAC-seq
• Online Access: https://doi.org/10.1186/s13059-021-02469-x
• ISSN: 1474-760X

Abstract Detecting multiplets in single nucleus (sn)ATAC-seq data is challenging due to data sparsity and limited dynamic range. AMULET (ATAC-seq MULtiplet Estimation Tool) enumerates regions with greater than two uniquely aligned reads across the genome to effectively detect multiplets. We evaluate the method by generating snATAC-seq data in the human blood and pancreatic islet samples. AMULET has high precision, estimated via donor-based multiplexing, and high recall, estimated via simulated multiplets, compared to alternatives and identifies multiplets most effectively when a certain read depth of 25K median valid reads per nucleus is achieved.