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长Dat:一个R包用于对高维数据的共变量敏感纵向分析
Chia-Yu Chen1,2,3,4, Ulrike Löber1,2,3,4, Sofia K Forslund1,2,3,4,5
1Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin 13125, Germany.
Bioinformatics advances
|June 26, 2023
概括
介绍LongDat,一个R包用于分析许多共变量的纵向数据. 它有效地区分干预效应,并识别机械中间体,优于现有的工具.
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
- 统计遗传学 统计遗传学
背景情况:
- 纵向数据分析对于理解随时间推移的生物过程至关重要.
- 在复杂的数据集中识别直接与间接的影响和机械中间体仍然具有挑战性.
- 现有的工具经常在纵向研究中与高维共变量数据作斗争.
研究的目的:
- 介绍LongDat,一个用于分析纵向多变量数据的新型R包.
- 为了使大量的共变量能够同时进行核算.
- 为了促进干预措施的直接和间接影响的区分,并确定机械中间体.
主要方法:
- 长数据采用针对纵向数据量身定制的先进统计方法.
- 该包旨在处理多种类型的数据,包括微生物组,二进制,分类和连续数据.
- 使用模拟和现实数据,对现有工具 (MaAsLin2,ANCOM,lgpr,ZIBR) 的性能进行了评估.
主要成果:
- 与现有方法相比,LongDat显示出更高的准确性,运行时间效率和更低的内存使用量.
- 该方案特别出色,特别是在具有大量共变量的场景中.
- 在高维纵向数据集中强大的生物标志物发现得到了LongDat.的帮助.
结论:
- LongDat是一个计算效率高,节省内存的R包,用于纵向数据分析.
- 它为剖析复杂的干预效应和识别关键生物中间体提供了一个强大的工具.
- 该包适用于广泛的应用,包括微生物组研究和其他高维生物数据.
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