Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder

Sanjiv K. Dwivedi, Andreas Tjärnberg, Jesper Tegnér and Mika Gustafsson ()
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Sanjiv K. Dwivedi: Linköping University
Andreas Tjärnberg: Linköping University
Jesper Tegnér: King Abdullah University of Science and Technology (KAUST)
Mika Gustafsson: Linköping University

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract Disease modules in molecular interaction maps have been useful for characterizing diseases. Yet biological networks, that commonly define such modules are incomplete and biased toward some well-studied disease genes. Here we ask whether disease-relevant modules of genes can be discovered without prior knowledge of a biological network, instead training a deep autoencoder from large transcriptional data. We hypothesize that modules could be discovered within the autoencoder representations. We find a statistically significant enrichment of genome-wide association studies (GWAS) relevant genes in the last layer, and to a successively lesser degree in the middle and first layers respectively. In contrast, we find an opposite gradient where a modular protein–protein interaction signal is strongest in the first layer, but then vanishing smoothly deeper in the network. We conclude that a data-driven discovery approach is sufficient to discover groups of disease-related genes.

Date: 2020
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DOI: 10.1038/s41467-020-14666-6

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