In genomics, transcriptomics, and related biological fields (collectively known as omics), combinations of experimental techniques can yield multiple sets of features for the same set of biological replicates. One example is Patch-seq, a method combining single-cell RNA sequencing with electrophysiological recordings from the same cells. Here we present a framework based on sparse reduced-rank regression (RRR) for obtaining an interpretable visualisation of the relationship between the transcriptomic and the electrophysiological data. We use elastic net regularisation that yields sparse solutions and allows for an efficient computational implementation. Using several Patch-seq datasets, we show that sparse RRR outperforms both sparse full-rank regression and non-sparse RRR, as well as previous sparse RRR approaches, in terms of predictive performance. We introduce a bibiplot visualisation in order to display the dominant factors determining the relationship between transcriptomic and electrophysiological properties of neurons. We believe that sparse RRR can provide a valuable tool for the exploration and visualisation of paired multivariate datasets.

中文翻译：

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用于成对多元数据的探索性可视化的稀疏降阶回归

在基因组学、转录组学和相关生物学领域（统称为组学)，实验技术的组合可以为同一组生物复制产生多组特征。一个例子是 Patch-seq，这是一种将单细胞 RNA 测序与来自相同细胞的电生理记录相结合的方法。在这里，我们提出了一个基于稀疏降阶回归 (RRR) 的框架，用于获得转录组和电生理数据之间关系的可解释可视化。我们使用弹性网络正则化产生稀疏解并允许有效的计算实现。使用多个 Patch-seq 数据集，我们表明稀疏 RRR 在预测性能方面优于稀疏全秩回归和非稀疏 RRR，以及之前的稀疏 RRR 方法。我们引入了一个bibiplot可视化以显示决定神经元转录组学和电生理学特性之间关系的主要因素。我们相信稀疏 RRR 可以为成对的多元数据集的探索和可视化提供有价值的工具。