论文标题
探索性对潜在结构模型用于转录组分析的投影
Exploratory Projection to Latent Structure Models for use in Transcriptomic Analysis
论文作者
论文摘要
在本文中，我们询问是否有可能通过将协变量对准和投影在比较子空间中来提高多元分析中的可解释性。我们演示了我们的方法以及PLS分解模型的解释力以及可靠的解释性如何导致定量见解。 我们讨论了PLS权重的统计属性，与特定轴相关的$ P $值及其对齐属性。 该方法在生命科学中的适用性也可以通过将其应用于公开可用数据集的三种用例中。 此外，我们介绍了由对齐的$ p $值与富集分析得出的结果进行比较的层次途径富集结果，作为我们方法的外部验证。 我们发现，该方法可以在所有研究的用例中发现基因组学的已知结果，即来自多发性硬化症和糖尿病患者的微阵列数据，以及来自乳腺癌患者的RNA测序数据。
In this paper, we ask if it is possible to increase the interpretability in multivariate analysis by aligning and projecting covariates onto comparative subspaces. We demonstrate our method as well as the interpretative power of PLS decomposed models and how robust interpretability can lead to quantitative insights. We discuss the statistical properties of the PLS weights, $p$-values associated with specific axes, as well as their alignment properties. The applicability of this approach within life science is also demonstrated by applying it to three use cases of publically available datasets. Further we present hierarchical pathway enrichment results stemming from aligned $p$-values, which are compared with results derived from enrichment analysis, as an external validation of our method. We find that the method can uncover known results from genomics for all of the studied use cases, i.e. microarray data from multiple sclerosis and diabetes patients as well as RNA sequencing data from breast cancer patients.
