数据驱动的数学和可视化方法用于删除复合数据分析 (CoDA) 的罕见特征
Adrian Ortiz-Velez1,2, Scott T Kelley1,2
1Biological and Medical Informatics Program, San Diego State University, San Diego, CA 92182, USA.
NAR genomics and bioinformatics
|January 8, 2024
概括
曲线切割是一种新的无监督方法,可以从稀有的生物数据中删除罕见的特征,例如元基因组学. 它使用数据驱动的断片来提高统计能力,并保留更多的特征而没有偏差.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 稀有的特征表与许多罕见的特征在大型生物数据集中很常见,例如元基因组学.
- 忽视零负载数据可能会导致统计估计偏差,并降低下游分析的力量.
- 在组合数据中,逻辑比分析是有问题的,因为未定义的log{0}.
研究的目的:
- 引入CurvCut,一种不受监督的,基于数据的方法,用于在稀疏的生物数据中去除罕见的特征.
- 为处理低频特征提供强大和合理的方法,解决任意删除值的局限性.
- 为了使研究人员能够自信地识别和应用功能删除切断,最大限度地提高功能保留和分析能力.
主要方法:
- CurvCut采用两种无监督的方法来识别特征频率分布中的自然断裂:曲率分析和费舍尔-詹克斯统计方法.
- 该方法包括人类确认,以验证已识别的切断点.
- 它旨在适用于各种生物数据类型.
主要成果:
- CurvCut有效地识别了针对低频特征删除的特定数据的切断值,最大限度地保持特征.
- 该方法可以快速识别这些断裂,并为用户确认生成清晰的可视化图像.
- 跨不同生物数据类型的应用证实了它的多功能性和有效性.
结论:
- CurvCut提供了一个数据驱动的,无监督的解决方案,用于在稀疏的生物数据集中去除罕见的特征.
- 该方法增强了统计能力,并通过提供合理的特征删除截止值来减少下游分析中的偏差.
- "CurvCut"有助于改进数据预处理,用于构成数据分析和其他生物信息学应用.
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