Localizing synergies of hidden factors across complex systems: resting brain networks and HeLa gene expression profile as case studies

TL;DR

This paper introduces a novel framework combining factor analysis with high-order dependency metrics to analyze complex systems, demonstrated through brain network and gene expression case studies.

Contribution

It proposes a new method to quantify synergy of latent factors and observed variables using O-information, bridging factor analysis and high-order interaction analysis.

Findings

DMN and FP networks show highest synergy in resting brain data

STK-12 gene identified as most synergistic in HeLa cells

Approach reveals key factors in complex biological systems

Abstract

Factor analysis is a well-known statistical method to describe the variability of observed variables in terms of a smaller number of unobserved latent variables called factors. Even though latent factors are conceptually independent of each other, their influence on the observed variables is often joint and synergistic. We propose to quantify the synergy of the joint influence of factors on the observed variables using the O-information, a recently introduced metrics to assess high order dependencies in complex systems, in a new framework where latent factors and observed variables are jointly analyzed in terms of their joint informational character. Two case studies are reported: analyzing resting fMRI data, we find that DMN and FP networks show the highest synergy, consistently with their crucial role in higher cognitive functions; concerning HeLa cells, we find that the most synergistic gene is STK-12 (AURKB), suggesting that this gene is involved in controlling the HeLa cell cycle. We believe that this approach, representing a bridge between factor analysis and the field of high-order interactions, will find wide application across several domains.