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bioRxiv · 10.1101/2025.03.17.643687

Deep Integrated Network Analysis: a data-driven tool to discover and characterize disease pathways in the liver

Quin, J. E.·Urrutia Iturritza, M.·Mosquera, K. D.·Hildebrandt, F. F. A.·Barrenas, F.·Ankarklev, J.
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Abstract

Background & AimsAn extensive number of studies have utilized transcriptomic profiling as a valuable tool for uncovering genes related to diseases and physiological processes of the liver. Here we combine this wealth of information to provide a powerful resource, by computationally constructing a comprehensive and unbiased network of gene interactions specific to the liver. MethodsWe have performed a computational approach termed Deep Integrated Network Analysis (DINA) on a curated catalog of 655 liver transcriptomic datasets (including a total of 48,311 transcriptomes). These datasets include human, monkey, mouse, rat and other mammalian species, and studies linked to a broad range of conditions. Together this facilitated construction of a network of strongly conserved gene-gene interactions relevant across the spectrum of liver diseases. ResultsThe Liver DINA Resource described herein contains 89,683 statistically conserved interactions among 19,317 genes in a unified network unique to the mammalian liver. The network unveils a hierarchical structure of strongly co-regulated modules, which are organized into a Tree-and-Leaf Network to provide a comprehensive overview of the resource. ConclusionsThis data-driven resource provides an interactive, publicly available tool for the examination of previously undescribed gene networks, and enables unbiased analysis of transcriptomic datasets of the liver, thus preventing bias in favor of well-studied genes and pathways and providing a complementary approach towards novel discoveries.

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Quin, J. E., Urrutia Iturritza, M., Mosquera, K. D., Hildebrandt, F. F. A., Barrenas, F., Ankarklev, J.. 2025-03-18. Deep Integrated Network Analysis: a data-driven tool to discover and characterize disease pathways in the liver. https://doi.org/10.1101/2025.03.17.643687

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