论文标题
组成协方差收缩和正则部分相关性
Compositional Covariance Shrinkage and Regularised Partial Correlations
论文作者
论文摘要
我们提出了一个估计程序，以在广泛的组成数据集中进行协方差。对于组成，由于常见参考，广泛使用的延展性变量是相互依存的。在施加单位和限制之前，logratio不相关的组合物是线性独立的。我们展示了它们如何用于构建定制收缩率目标，以进行求解协方差矩阵，并在模拟和单细胞基因表达数据集上测试一个简单的过程，以进行部分相关估计。对于基础计数，评估了不同的零备用。封闭引起的部分相关是通过分析得出的。数据和代码可从GitHub获得。
We propose an estimation procedure for covariation in wide compositional data sets. For compositions, widely-used logratio variables are interdependent due to a common reference. Logratio uncorrelated compositions are linearly independent before the unit-sum constraint is imposed. We show how they are used to construct bespoke shrinkage targets for logratio covariance matrices and test a simple procedure for partial correlation estimates on both a simulated and a single-cell gene expression data set. For the underlying counts, different zero imputations are evaluated. The partial correlation induced by the closure is derived analytically. Data and code are available from GitHub.
