Multi-‘Omic Integration via Similarity Network Fusion to Detect Molecular Subtypes of Aging

Abstract

AbstractBackgroundMolecular subtyping of brain tissue provides insights into the heterogeneity of common neurodegenerative conditions, such as Alzheimer’s disease (AD). However, existing subtyping studies have mostly focused on single data modalities and only those individuals with severe cognitive impairment. To address these gaps, we applied Similarity Network Fusion (SNF), a method capable of integrating multiple high-dimensional multi-’omic data modalities simultaneously, to an elderly sample spanning the full spectrum of cognitive aging trajectories.MethodsWe analyzed human frontal cortex brain samples characterized by five ‘omic modalities: bulk RNA sequencing (18,629 genes), DNA methylation (53,932 cpg sites), histone H3K9 acetylation (26,384 peaks), proteomics (7,737 proteins), and metabolomics (654 metabolites). SNF followed by spectral clustering was used for subtype detection, and subtype numbers were determined by eigen-gap and rotation cost statistics. Normalized Mutual Information (NMI) determined the relative contribution of each modality to the fused network. Subtypes were characterized by associations with 13 age-related neuropathologies and cognitive decline.ResultsFusion of all five data modalities (n=111) yielded two subtypes (nS1=53, nS2=58) which were nominally associated with diffuse amyloid plaques; however, this effect was not significant after correction for multiple testing. Histone acetylation (NMI=0.38), DNA methylation (NMI=0.18) and RNA abundance (NMI=0.15) contributed most strongly to this network. Secondary analysis integrating only these three modalities in a larger subsample (n=513) indicated support for both 3- and 5-subtype solutions, which had significant overlap, but showed varying degrees of internal stability and external validity. One subtype showed marked cognitive decline, which remained significant even after correcting for tests across both 3- and 5-subtype solutions (pBonf=5.9×10−3). Comparison to single-modality subtypes demonstrated that the three-modal subtypes were able to uniquely capture cognitive variability. Comprehensive sensitivity analyses explored influences of sample size and cluster number parameters.ConclusionWe identified highly integrative molecular subtypes of aging derived from multiple high dimensional, multi-’omic data modalities simultaneously. Fusing RNA abundance, DNA methylation, and H3K9 acetylation measures generated subtypes that were associated with cognitive decline. This work highlights the potential value and challenges of multi-’omic integration in unsupervised subtyping of postmortem brain.