Abstract We consider the problem of constructing a reduced-rank regression model whose coefficient parameter is represented as singular value decomposition with sparse vectors. The traditional estimation procedure for often fails when true rank high. To overcome this issue, we develop an algorithm and variable selection via regularization manifold optimization, which enables us to obtain accura...

In genomics, transcriptomics, and related biological fields (collectively known as omics), combinations of experimental techniques can yield multiple sets features for the same set replicates. One example is Patch-seq, a method combining single-cell RNA sequencing with electrophysiological recordings from cells. Here we present framework based on sparse reduced-rank regression (RRR) obtaining a...