Structured Joint Decomposition (SJD) identifies conserved molecular dynamics across collections of biologically related multi-omics data matrices

It is necessary to develop exploratory tools to learn from the unprecedented volume of high-dimensional multi-omic data currently being produced across the field of biomedicine. We have developed an R package, Structured Joint Decomposition (SJD), which identifies components of variation that are shared across multiple matrices. The approach focuses specifically on variation across the samples/cells within each dataset while incorporating biologist-defined hierarchical structure among input experiments that can span in vivo and in vitro systems, multi-omic data modalities, and species. SJD enables the definition of molecular variation that is conserved across systems, those that are shared within subsets of studies, and elements unique to individual matrices. We have included functions to simplify the construction and visualization of highly complex in silico experiments involving many diverse multi-omic matrices from multiple species. Here we apply SJD to decompose four RNA-seq experiments focused on neurogenesis in the neocortex. The public datasets used in this analysis are at NeMO Analytics and can be explored at the individual gene level or using the conserved transcriptomic dynamics in mammalian neurogenesis that we define here. The SJD R package and tutorial can be found at https://chuansite.github.io/SJD. Contact: hzchenhuan@gmail.com; ccolant1@jhmi.edu [carlocolantuoni.org]