Highlights d Mouse retinal development was profiled using single-cell RNA-seq d Early and late-stage retinal progenitors are distinct d Primary and neurogenic retinal progenitors are distinct d NFI factors control cell-cycle exit and generation of late-born retinal cell types

Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.

Highlights d Identification of networks governing human retinal cell-type specification d Characterization of mechanisms controlling horizontal cell and foveal development d Analysis of conserved and divergent gene expression between human and mouse d ATOH7 loss during late neurogenesis inhibits specification of cone photoreceptors

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