Multi-omics data is frequently measured to characterize biological mechanisms underlying phenotypes. We propose a multi-omics analysis method called MONTI (Multi-Omics Non-negative Tensor decomposition for Integrative analysis), which is designed to integrate and analyze large sets of multiple omics data.

The development of single-cell RNA sequencing (scRNA-seq) has enabled gene expression to be quantified at single-cell resolution. Here, we focus on detecting gene expression patterns that well capture the underlying biological differences between time-series scRNA-seq datasets under multiple conditions.