High-throughput data representing large mixtures of chemical or biological signals are ordinarily produced in the molecular sciences. Given a number of samples, partial least squares (PLS) regression is a well-established statistical method to investigate associations between them and any continuous response variables of interest. However, technical artifacts generally make the raw signals not directly comparable between samples. Thus, data normalization is required before any meaningful scientific information can be drawn. This often allows to characterize the processed signals as compositional data where the relevant information is contained in the pairwise log-ratios between the components of the mixture. The (log-ratio) pivot coordinate approach facilitates the aggregation into single variables of the pairwise log-ratios of a component to all the remaining components. This simplifies interpretability and the investigation of their relative importance but, particularly in a high-dimensional context, the aggregated log-ratios can easily mix up information from different underlaying processes. In this context, we propose a weighting strategy for the construction of pivot coordinates for PLS regression which draws on the correlation between response variable and pairwise log-ratios. Using real and simulated data sets, we demonstrate that this proposal enhances the discovery of biological markers in high-throughput compositional data.

中文翻译：

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用于高通量成分数据中基于偏最小二乘法的标记发现的加权枢轴坐标

代表大量化学或生物信号混合的高通量数据通常在分子科学中产生。给定大量样本，偏最小二乘 (PLS) 回归是一种行之有效的统计方法，用于研究它们与任何感兴趣的连续响应变量之间的关联。然而，技术工件通常会使原始信号在样本之间无法直接比较。因此，在得出任何有意义的科学信息之前，需要进行数据标准化。这通常允许将处理后的信号表征为成分数据，其中相关信息包含在混合物组分之间的成对对数比中。(log-ratio) 枢轴坐标方法有助于将一个组件与所有剩余组件的成对对数比聚合成单个变量。这简化了可解释性和对其相对重要性的调查，但是，特别是在高维环境中，聚合的对数比可以很容易地混合来自不同底层过程的信息。在这种情况下，我们提出了一种加权策略，用于构建 PLS 回归的枢轴坐标，该策略利用响应变量和成对对数比之间的相关性。使用真实和模拟数据集，我们证明该提议增强了高通量成分数据中生物标记的发现。特别是在高维环境中，聚合的对数比可以很容易地混合来自不同底层过程的信息。在这种情况下，我们提出了一种加权策略，用于构建 PLS 回归的枢轴坐标，该策略利用响应变量和成对对数比之间的相关性。使用真实和模拟数据集，我们证明该提议增强了高通量成分数据中生物标记的发现。特别是在高维环境中，聚合的对数比可以很容易地混合来自不同底层过程的信息。在这种情况下，我们提出了一种加权策略，用于构建 PLS 回归的枢轴坐标，该策略利用响应变量和成对对数比之间的相关性。使用真实和模拟数据集，我们证明该提议增强了高通量成分数据中生物标记的发现。