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

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      Author(s)
      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.
      Çiçek, A. Ercüment
      Ucar, Duygu
      Date
      2021-12
      Source Title
      Genome Biology
      Print ISSN
      1474-7596
      Electronic ISSN
      1474-760X
      Publisher
      BioMed Central Ltd.
      Volume
      22
      Issue
      1
      Pages
      1 - 19
      Language
      English
      Type
      Article
      Item Usage Stats
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      41
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      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.
      Keywords
      Multiplets
      Doublets
      Single nucleus ATAC-seq
      snATAC-seq
      Permalink
      http://hdl.handle.net/11693/77279
      Published Version (Please cite this version)
      https://doi.org/10.1186/s13059-021-02469-x
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      • Department of Computer Engineering 1561
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