Non-negative consensus tensor factorization and applications in genomics

Abstract: The tumor microenvironment (TME) is a complex milieu around the tumor, whereby cancer cells interact with stromal, immune,
vascular, and extracellular components. The TME is being increasingly recognized as a key determinant of tumor growth, disease
progression, and response to therapies. A comprehensive study in this area requires the integration of multi-modal, multi-sample
data from different sources. Tensor methods are known to be able to successfully incorporate data from multiple sources and
perform a joint analysis of heterogeneous high-dimensional data sets. We build a generalizable and robust tensor-based
framework capable of integrating dissociated single-cell and spatially resolved RNA-sequencing data for a comprehensive
analysis of the TME. The methodologies developed as part of this effort will advance our understanding of the TME in multiple
directions. These include cellular heterogeneity within the TME, crosstalks between cells, and tumor-intrinsic pathways
stimulating tumor growth and immune evasion.