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AMULET:一种基于读取计数的新型方法,用于从单个细胞核 ATAC-seq 数据中有效检测多聚体。

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

机构信息

The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.

Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, 94158, USA.

出版信息

Genome Biol. 2021 Sep 1;22(1):252. doi: 10.1186/s13059-021-02469-x.

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cea2/8408950/4d94bdaa42fe/13059_2021_2469_Fig1_HTML.jpg

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