ExIR

This model is available in both R and Python versions of the influential package. The tutorial video showcases the function performance in the R environment.

ExIR (experimental data-based integrative ranking) is a data-driven framework designed to identify and prioritize the most biologically relevant genes, proteins, and other molecular features from high-dimensional omics data. By analyzing feature behavior within inferred association networks, ExIR classifies and ranks candidates as drivers, biomarkers, or mediators, helping researchers focus on the features most likely to play important biological roles. Unlike many prioritization approaches, ExIR operates directly on experimental data without requiring external annotations or prior biological knowledge.

ExIR is applicable to virtually all bulk and single-cell omics data types, including large-scale single-cell atlases. To support efficient analysis of very large datasets, it incorporates pseudo-bulking functionality that can process datasets containing millions of cells while substantially reducing computational requirements. ExIR also integrates seamlessly with standard single-cell analysis workflows through native support for Seurat objects.

The framework has been evaluated across multiple transcriptomic and proteomic datasets, consistently demonstrating strong feature-prioritization performance relative to commonly used approaches. In one application, ExIR identified candidate regulators associated with disease progression in RNA-seq data from a zebrafish model of mucopolysaccharidosis IIIA. Together, these capabilities make ExIR a versatile and scalable solution for extracting biologically meaningful insights from modern omics datasets and guiding downstream experimental investigation.