Development of a data-driven multi-omics module repertoire for data analysis and interpretation

Background

Advances in technology have facilitated the generation of deep systems-level immune profiling data. Efforts to characterize these datasets rely on the availability of well-annotated sources of prior knowledge, such as gene modules for enrichment analysis. Modules consist of groups of co-expressed analytes and provide a stable framework for identifying and annotating signatures to understand broader patterns of immune responses. While certain components of immune responses can be explained through single assay profiling, the complex interplay between various molecular factors is dependent upon multiple assays, motivating the need for multi-omics modules.

Results

As part of the Human Immunology Project Consortium (HIPC), we have developed Multi-Omics Modules version 1 (MOMod1), a set of data-driven multiomics modules designed to serve as a stable and reusable framework for downstream enrichment analysis of multi-omics data. MOMod1 encapsulates over 1000 matched transcriptomic, proteomic, and metabolomic molecular profiles from hospitalized SARS-CoV-2 participants in 33 diverse immune programs. Furthermore, we present a thorough investigation into the underlying biological processes represented in each module. Finally, we validate our findings on multiple external multi-omics datasets.

Discussion

MOMod1 modules can be utilized to characterize immune profiling data consisting of transcriptomic, metabolomic, and proteomic profiles. Future versions of multi-omics modules will combine molecular profiles from a broader set of disease states. The modules and accompanying R scripts for enrichment analyses will be hosted as a public resource on ImmuneSpace at https://immunespace.org/.