The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.
Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, 94158, USA.
Genome Biol. 2021 Sep 1;22(1):252. doi: 10.1186/s13059-021-02469-x.
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.
由于数据稀疏和动态范围有限,检测单个细胞核 (sn)ATAC-seq 数据中的多联体是具有挑战性的。AMULET(ATAC-seq MULtiplet Estimation Tool)枚举基因组中具有两个以上唯一对齐读取的区域,以有效地检测多联体。我们通过在人类血液和胰岛样本中生成 snATAC-seq 数据来评估该方法。与其他方法相比,AMULET 在基于供体的多路复用方面具有高精度,在模拟多联体方面具有高召回率,并且在每个细胞核达到 25K 中位数有效读取量的特定读取深度时,能够最有效地识别多联体。