The structure is the message: preserving experimental context through tensor decomposition

TL;DR

This paper advocates for using tensor decomposition techniques to preserve experimental context in high-dimensional biological data, enhancing analysis accuracy and interpretability.

Contribution

It introduces tensor-structured analysis methods that maintain experimental structure, offering a novel approach to biological data representation and analysis.

Findings

Tensor methods effectively preserve experimental context.

Tensor decomposition improves data interpretability.

Proposed methods are poised to become standard in biomedical analysis.

Abstract

Recent biological studies have been revolutionized in scale and granularity by multiplex and high-throughput assays. Profiling cell responses across several experimental parameters, such as perturbations, time, and genetic contexts, leads to richer and more generalizable findings. However, these multidimensional datasets necessitate a reevaluation of the conventional methods for their representation and analysis. Traditionally, experimental parameters are merged to flatten the data into a two-dimensional matrix, sacrificing crucial experiment context reflected by the structure. As Marshall McLuhan famously stated, "The medium is the message." In this work, we propose that the experiment structure is the medium in which subsequent analysis is performed, and the optimal choice of data representation must reflect the experiment structure. We introduce tensor-structured analyses and decompositions to preserve this information. We contend that tensor methods are poised to become integral to the biomedical data sciences toolkit.