Author
Seigal, A
Beguerisse-Díaz, M
Schoeberl, B
Niepel, M
Harrington, H
Journal title
Journal of the Royal Society Interface
Last updated
2024-03-21T17:05:41.787+00:00
Abstract
We introduce a tensor-based clustering method to extract sparse,
low-dimensional structure from high-dimensional, multi-indexed datasets.
Specifically, 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. 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 respecting the multi-indexed structure
of the data, our clustering analysis can be optimized for biological
interpretation and therapeutic understanding. We then perform a systematic,
large-scale exploration of mechanistic models of MAPK-AKT crosstalk for each
cluster. This analysis allows us to quantify the heterogeneity of breast cancer
cell subtypes, and leads to hypotheses about the mechanisms by which cell lines
respond to ligands. Our clustering method is general and can be tailored to a
variety of applications in science and industry.
Symplectic ID
800949
Download URL
http://arxiv.org/abs/1612.08116v2
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Publication type
Journal Article
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