Tensor clustering with algebraic constraints gives interpretable groups of crosstalk mechanisms in breast cancer.

ABSTRACT: We introduce a tensor-based clustering method to extract sparse, low-dimensional structure from high-dimensional, multi-indexed datasets. This framework is designed to enable detection of clusters of data in the presence of structural requirements which we encode as algebraic constraints in a linear program. Our clustering method is general and can be tailored to a variety of applications in science and industry. We illustrate our method on a collection of experiments measuring the response of genetically diverse breast cancer cell lines to an array of ligands. Each experiment consists of a cell line-ligand combination, and contains time-course measurements of the early signalling kinases MAPK and AKT at two different ligand dose levels. By imposing appropriate structural constraints and r

SUBMITTER: Seigal A 

PROVIDER: S-EPMC6408352 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature