plmmr:一个R包,适用于惩罚性线性混合模型,用于具有复杂的相关性结构的全基因组关联数据
Tabitha K Peter1, Anna C Reisetter1, Yujing Lu1
1Department of Biostatistics, University of Iowa, 145 N Riverside Dr, Iowa 52242, United States.
Briefings in bioinformatics
|January 31, 2026
概括
我们开发了plmmr,这是一款针对处罚线性混合模型的R包,以解决高维数据中的混问题. 它估计相关性,以提高预测,并处理大数据集,使用内存映射进行全基因组关联研究.
科学领域:
- 遗传学 遗传学 是一个
- 统计建模 统计建模
- 生物信息学是一种生物信息学.
背景情况:
- 高维数据分析由于观测混而存在挑战.
- 准确的相关性估计对于强大的回归建模至关重要.
研究的目的:
- 介绍plmmr,一个开源的R包,用于处罚的线性混合模型.
- 通过估计观测间的相关性,提高高维数据集中的预测准确性.
主要方法:
- 使用R.实施处罚的线性混合模型.
- 使用内存映射来有效分析超过RAM的基因组规模数据.
- 为处理大型数据集开发一个文件支持方法.
主要成果:
- plmmr有效地估计了高维数据中的相关性.
- 该包通过使用最好的线性无偏预测器来增强预测.
- 在真实全基因组关联研究数据上证明了计算效率.
结论:
- plmmr提供了一个可扩展的解决方案,用于分析大型,高维的遗传数据集.
- 该软件包在存在复杂的相关性结构时,可促进改进的预测建模.
- 开源的可用性促进了基因研究的更广泛采用.
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