提高了使用相互信息的染色体可访问性数据中关联性和可重复性的质量指标
Cullen Roth1, Vrinda Venu2, Vanessa Job3
1Los Alamos National Laboratory, Genomics and Bioanalytics, Los Alamos, NM, USA. croth@lanl.gov.
BMC bioinformatics
|November 22, 2023
概括
这项研究表明,由于零值,常见的相关性指标对分析ATAC-seq数据不可靠. 删除这些零区域显著改善了相关性估计,相互信息和R平方是表观遗传学分析中最准确的措施.
科学领域:
- 基因组学就是基因组学.
- 表观基因组学是指表观基因组学.
- 计算生物学 计算生物学
背景情况:
- 相关度量经常在基因组学中使用,但通常不考虑基本数据假设,如正常性和同性多样性.
- 通过测序 (ATAC-seq) 测定转化酶可访问染色质的数据通常表现为非正常和零值,使标准相关性分析复杂化.
- 很少有研究将相关统计数据的性能与ATAC-seq数据进行了基准比较,特别是在可重复性和异常值效应方面.
研究的目的:
- 通过计算模拟ATAC-seq数据,以调查各种相关联和关联统计数据的行为.
- 在可复制性和数据差异的受控条件下评估这些统计数据的准确性.
- 确定可靠的方法来量化染色质可访问性测试中的关系.
主要方法:
- 开发了一个用于ATAC-seq数据生成的计算模拟框架.
- 评估了皮尔森的R,斯皮尔曼的rho,肯德尔的tau,上下相关性,R平方,肯德尔的W和正常化的相互信息的表现.
- 研究了删除共同零区域对统计估计的影响.
- 利用随机森林模型来比较ATAC-seq复制关系的预测准确性.
主要成果:
- 大多数测试的统计数据 (斯皮尔曼的rho,肯德尔的tau,肯德尔的W) 显示对模拟的ATAC-seq复制品之间的差异越来越不敏感.
- 删除co-zeros大大改善了相关性和关联估计.
- R-平方和正常化的相互信息表现出卓越的性能,在同零去除后,与已知的共享位置密切匹配.
- 当用随机森林模型分析时,规范化的相互信息最好地预测了ATAC-seq复制关系.
结论:
- 标准的相关性和关联度可能不准确地反映表观遗传学数据中的关系,特别是ATAC-seq.
- 删除co-zeros是提高ATAC-seq数据相关性和关联分析可靠性的关键预处理步骤.
- 规范化的相互信息和R平方提供了更强大的策略来量化染色体可访问性测试中的关系.
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