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randPedPCA:从大型血统中快速近似的主要组成部分
Hanbin Lee1, Rosalind Françoise Craddock2, Gregor Gorjanc2
1Department of Statistics, University of Michigan, Ann Arbor, MI, 48109, USA.
Genetics, selection, evolution : GSE
|August 28, 2025
概括
现在使用randPedPCA R包可以更快地可视化大型谱系. 它使用快速血统主要组件分析 (PCA) 来有效分析数百万条记录,克服计算挑战.
科学领域:
- 遗传学
- 生物信息学
- 计算生物学
背景情况:
- 现代育种计划涉及数以百万计的血统记录, 构成可视化挑战.
- 血统数据可以表示为关系矩阵,通过主要组件分析 (PCA) 实现可视化.
- 纯粹的PCA方法面临着大量的计算和内存限制.
研究的目的:
- 开发一种可视化大型血统结构的有效方法.
- 克服传统PCA对广泛的血统数据集的计算限制.
主要方法:
- 开发了开放访问的R包randPedPCA用于快速血统PCA.
- 使用稀疏矩阵和隐含矩阵向量乘法与反向关系因子.
- 通过RSpectra库实现随机的单数值分解和Eigen分解.
主要成果:
- 与模拟数据上的原始PCA相比, randPedPCA套件实现了超过10,000倍的速度提升.
- 随机PCA方法使得以前由于计算限制而无法进行分析.
- 在英国犬俱乐部进行了对近150万只拉布拉多猎犬血统的分析,显示时间显著减少.
结论:
- 谱系矩阵的主要组成部分可以使用随机化方法有效计算.
- PCA分数的散射图为大血统提供直观的可视化.
- 这种方法比传统的谱系图表对大数据集的染要快得多.
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