Charting critical transient gene interactions in disease progression across bulk, single-cell, and spatial transcriptomics

Critical transitions (CTs) in gene regulatory networks presage abrupt disease shifts, yet existing tools rank signals unsupervisedly at gene/module level, use unweighted enrichments, and underuse multimodal data. We present CRISGI, which models interaction-level CT dynamics across bulk, single-cell, and spatial transcriptomics, providing phenotype- and observation-level CT-score rank enrichment and CT presence/onset prediction. CRISGI outperforms existing methods on in silico benchmarks, prioritizes 128 symptom-onset-predictive interactions in H3N2 influenza with eight external validation datasets, uncovers stage-specific survival-linked interactions across TCGA cohorts, highlights CDK-FOXO interactions in colorectal cancer cells, and links LUM-centric interactions to invasive breast-tumor regions, yielding testable mechanistic hypotheses.