TY - JOUR T1 - Sparse reduced-rank regression for exploratory visualization of multimodal data sets JF - bioRxiv DO - 10.1101/302208 SP - 302208 AU - Dmitry Kobak AU - Yves Bernaerts AU - Marissa A. Weis AU - Federico Scala AU - Andreas Tolias AU - Philipp Berens Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/08/10/302208.abstract N2 - In genomics, transcriptomics, and related biological fields (collectively known as ‘omics’), it is common to work with n ≪ p data sets with the dimensionality much larger than the sample size. In recent years, combinations of experimental techniques began to 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 for obtaining an interpretable visualization of the relationship between the transcriptomic and the electrophysiological data. We use an elastic net regularization penalty that yields sparse solutions and allows for an efficient computational implementation. Using several publicly available Patch-seq data sets, we show that sparse reduced-rank regression outperforms both sparse full-rank regression and non-sparse reduced-rank regression in terms of predictive performance, and can outperform existing methods for sparse partial least squares and sparse canonical correlation analysis in terms of out-of-sample correlations. We introduce a ‘bibiplot’ visualization in order to display the dominant factors determining the relationship between transcriptomic and electrophysiological properties of neurons. We believe that sparse reduced-rank regression can provide a valuable tool for the exploration and visualization of multimodal data sets, including Patch-seq. ER -