An Enhanced Pipeline for Multi-Omic Integration Based on Topological Data Analysis

The advent of high-throughput sequencing technologies has made it essential to employ advanced tools for data integration and interpretation. In this work, we propose to build upon two existing efforts by expanding their scope and applicability to multi-omic data integration through the development of a pipeline that generates a topologically informed ranking of genes to assess gene relevance to disease. Specifically, we exploit topological data analysis, capitalizing on: its graph-based framework, to refine complex interactions characteristic of biological systems; its intrinsic explainability, to enable interpretation of model outputs in terms of biologically meaningful features; its robustness to small input perturbations, to ensure stable results in the presence of noisy data, as omics are. In particular, we employ Harmonic Persistent Homology (HPH), and propose two relevant advances. First, we adopt an integration and clustering tool (i.e. iNETgrate) as an informed means to reduce data size and integrate multiple omic layers by aggregating methylation beta values from loci to the gene level, to be combined with gene expression values. This enables the application of HPH, otherwise limited by the computational burden. Second, as a non-trivial consequence, we apply HPH to multi-omic molecular data, and not to patients as done so far, enlarging HPH scope. Finally, we follow up on recent efforts toward performance standardization, and validate our results against biomarkers from a well-known TCGA breast cancer benchmark dataset.