B-S.B.01: Interpretable Gene Program analysis for single-cell transcriptomics: A comparative Methodological framework with application to Idiopathic Pulmonary Fibrosis
Interpretable gene-program analysis offers a principled way to extract disease-relevant biology from single-cell RNA sequencing (scRNA-seq) while preserving cell-type specificity and reducing the complexity of gene-level results. Here, we present a methodological framework to quantify and compare cell-type-specific gene-program activation across conditions, and evaluate it in idiopathic pulmonary fibrosis (IPF), a progressive fibrotic lung disease with limited therapeutic options.
Our approach combines unsupervised and supervised matrix factorization-based methods to infer gene-program activity from scRNA-seq data, utilizing the Cytopus knowledge graph to define biologically grounded programs. Differential program activation is then tested with statistical models that account for patient-level dependence and cell-cycle effects.
To assess robustness and interpretability, we benchmark this workflow against conventional disease-analysis workflows, including single-cell and pseudo-bulk differential expression followed by over-representation and gene set enrichment analyses, by finely tuning a comparative evaluation of diverse computational methodologies.
Applied to scRNA-seq data from IPF and healthy lungs, the framework enabled a systematic evaluation of concordance across conventional and matrix factorization-based methods, leading to the identification of highly robust consensus disease-associated programs. Importantly, each method also detected biologically relevant dysregulation independently highlighting the importance of a comparative, multi-methodological approach.
Beyond prioritizing dysregulated processes, we reconstructed regulatory and interaction networks for relevant programs to identify core interactors and shared regulators with therapeutic potential for drug targeting and repurposing.
This study evaluates interpretable gene program dysregulation analysis in IPF, showing that it can serve as a valuable complement to gene-level workflows and a biologically relevant framework for target identification and drug repurposing.
Co-authors: Nicola Casiraghi, Enrico Domenici, Alessandro Romanel
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