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

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 meth...

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Detalles Bibliográficos
Autores principales: Thibodeau, Asa, Eroglu, Alper, McGinnis, Christopher S., Lawlor, Nathan, Nehar-Belaid, Djamel, Kursawe, Romy, Marches, Radu, Conrad, Daniel N., Kuchel, George A., Gartner, Zev J., Banchereau, Jacques, Stitzel, Michael L., Cicek, A. Ercument, Ucar, Duygu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8408950/
https://www.ncbi.nlm.nih.gov/pubmed/34465366
http://dx.doi.org/10.1186/s13059-021-02469-x
Descripción
Sumario: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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02469-x.