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scATAnno:用于单细胞ATAC测序数据的自动化单元类型注释

Yijia Jiang^1,2, Zhirui Hu³, Feng Lu^1,2

¹Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA.

Genomics, proteomics & bioinformatics

|November 24, 2025

概括

scATAnno使用参考地图集自动化单细胞ATAC测序 (scATAC-seq) 数据的细胞类型注释. 这个Python包可以准确地识别细胞类型,而无需RNA测序数据,性能优于现有的方法.

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

像scATAC-seq这样的单细胞表观基因组技术正在迅速发展.
从scATAC-seq数据中准确识别细胞类型对于生物学解释至关重要.
现有的方法可能需要补充数据或缺乏可靠性.

研究的目的:

介绍scATAnno,一个用于自动化scATAC-seq数据注释的Python包.
为了使用大规模的scATAC-seq参考图谱来实现细胞类型的识别.
为scATAC-seq参考构建和单元格注释提供一个强大的工具.

主要方法:

开发了scATAnno,这是一个Python包,将查询scATAC-seq数据与参考地图集成在一起.
从公开可用的 scATAC-seq 数据集生成参考地图.
整合了基于KNN和基于距离的加权不确定性得分,以提高准确性.
与其他五种单元格注释方法对比,对scATAnno进行了基准.

主要成果:

与现有方法相比,scATAnno在多个数据集和指标上表现出卓越的性能.
该工具准确地注释了外围血液单核细胞 (PBMC),三阴性乳腺癌 (TNBC) 和基底细胞癌 (BCC) 的细胞类型.
不确定性得分有效地识别了在参考数据中不存在的独特细胞群.

结论:

scATAnno为scATAC-seq细胞类型注释提供了准确有效的方法.
该软件包有助于scATAC-seq参考地图的构建和数据的解释.
scATAnno是使用scATAC-seq数据分析复杂生物系统的宝贵工具.

关键词:

单元格注释单元格注释参考地图集是一个参考地图集.单细胞表观遗传学单细胞表观遗传学不确定性得分不确定性得分这就是 scATAC-seqq.

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