All Times ET
Keywords: Tensor regression, Single-cell sequencing, Network analysis
Advancements in genomic sequencing continually improve personalized medicine in complex diseases. Recent breakthroughs generate multiple types of signatures (or multi-omics) from each cell, producing numerous datasets per single-cell experiment. We explore techniques for detecting networks across multi-omic single-cell datasets related to clinical outcomes. We leverage penalization concepts, often used in multi-omic network analytics due to the high-dimensionality where multiple-testing is likely underpowered. We organize the data into multi-dimensional tensors where the dimensions correspond to the different omic types and cell types. Using the outcome and the single-cell tensors, we perform regularized tensor regression to return a variable set for each omic type that forms the clinically-associated network. Robustness is assessed over simulations based on available single-cell simulation methods. The flexibility of our approach enables future extensions on distributional assumptions and covariate adjustments. This algorithm may identify clinically-relevant genetic patterns on a cellular-level that span multiple layers of sequencing data and ultimately inform highly precise therapeutic targets in complex diseases.