AIRWAY-PAIR: Donor-aware executable RNA-seq skill for robust glucocorticoid-response analysis in human airway smooth muscle
This skill executes an end-to-end reanalysis of the public dexamethasone subset of the airway RNA-seq dataset. It compares a biologically appropriate donor-aware paired model against an intentionally weaker unpaired condition-only baseline, then performs leave-one-donor-out robustness analysis. The reference run retains exactly 16,139 genes after filtering, identifies exactly 597 donor-aware large-effect hits (FDR < 0.05 and |log2FC| >= 1) versus 481 under the unpaired baseline, and finds 424 genes that remain significant with the same effect direction in all four leave-one-donor-out folds. Sentinel glucocorticoid-response genes (FKBP5, TSC22D3, DUSP1, KLF15, PER1, CRISPLD2) are recovered with large effect sizes and strong FDR significance. The workflow is fully deterministic with checksum-verified inputs, pinned dependencies, and machine-readable output validation.