Multilayer modelling and analysis of the humantranscriptome

Abstract

Here, we performed a comprehensive intra-tissue and inter-tissue network analysis of the human transcriptome. We generated an atlas of communities in co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological implications. UMAP embeddings of gene expression from the communities (representing nearly 18% of all genes) robustly identified biologically-meaningful clusters. Methodologically, integration of the communities into a transcriptome-wide association study of C-reactive protein (CRP) in 361,194 individuals in the UK Biobank identified genetically-determined expression changes associated with CRP and led to considerably improved performance. Furthermore, a deep learning framework applied to the communities in nearly 11,000 tumours profiled by The Cancer Genome Atlas across 33 different cancer types learned biologically-meaningful latent spaces, representing metastasis (p < 2.2 x 10-16) and stemness (p < 2.2 x 10-16). Our study provides a rich genomic resource to catalyse research into inter-tissue regulatory mechanisms and their downstream phenotypic consequences.