SomaModules: A Pathway Enrichment Approach Tailored to SomaScan Data

Motivated by the
lack of adequate tools to perform pathway enrichment
analysis, this work presents an approach specifically tailored to
SomaScan data. Starting from annotated gene sets, we developed a greedy,
top-down procedure to iteratively identify strongly intracorrelated
SOMAmer modules, termed “SomaModules”, based on 11K
SomaScan data. We generated two repositories based on the latest MSigDB
and MitoCarta releases, containing more than 40,000 SOMAmer-based
gene sets combined. These repositories can be utilized by any unstructured
pathway enrichment analysis tool. We validated our results with two
case examples: (i) Alzheimer’s disease specific pathways in
a 7K SomaScan case–control study, and (ii) mitochondrial pathways
using 11K SomaScan data linked to physical performance outcomes. Using
gene set enrichment analysis (GSEA), we found that, in both examples,
SomaModules had significantly higher enrichment than the original
gene set counterparts. These findings were robust and not significantly
affected by the choice of enrichment metric or the Kolmogorov enrichment
statistic used in the GSEA procedure. We provide users with access
to all code, documentation and data needed to reproduce our current
repositories, which also will enable them to leverage our framework
to analyze SomaModules derived from other sources, including custom,
user-generated gene sets.