Demographic confounders distort inference of gene regulatory and gene co-expression networks in cancer

Abstract

Gene regulatory networks (GRNs) and gene co-expression networks (GCNs) allow genome-wide exploration of molecular regulation patterns in health and disease. The standard approach for obtaining GRNs and GCNs is to infer them from gene expression data, using computational network inference methods. However, since network inference methods are usually applied on aggregate data, distortion of the networks by demographic confounders might remain undetected, especially because gene expression patterns are known to vary between different demographic groups. In this paper, we present a computational framework to systematically evaluate the influence of demographic confounders on network inference from gene expression data. Our framework compares similarities between networks inferred for different demographic groups with similarity distributions obtained for random splits of the expression data. Moreover, it allows to quantify to which extent demographic groups are represented by networks inferred from the aggregate data in a confounder-agnostic way. We apply our framework to test four widely used GRN and GCN inference methods as to their robustness w. r. t. confounding by age, ethnicity and sex in cancer. Our findings based on more than $ {44000}$ inferred networks indicate that age and sex confounders play an important role in network inference for certain cancer types, emphasizing the importance of incorporating an assessment of the effect of demographic confounders into network inference workflows. Our framework is available as a Python package on GitHub: https://github.com/bionetslab/grn-confounders.

DysRegNet: Patient-specific and confounder-aware dysregulated network inference

“…Age, for example, only affected the BRCA dataset. This finding is well-aligned with a recent study on the effect of demographic confounders on cohort-level network inference tools where age was identified as a strong confounder on network inference in BRCA based on data from two independent cohorts (TCGA and METABRIC)[37]. Given that BRCA has notably more control samples (113) than other cancer types (see Table S1), we assume that certain effects require a sufficiently large sample to be detected.…”