AWmeta Empowers Adaptively Weighted Transcriptomic Meta-Analysis

-value or effect-size integration approaches exhibit limited power to resolve subtle yet vital gene signatures. This study presents AWmeta, an adaptively weighted framework that unifies both meta-analytical paradigms for the first time. Benchmarked on 35 Parkinson's and Crohn's disease datasets spanning diverse tissues and adaptively down-weighting underpowered studies, AWmeta yields higher-fidelity DEGs with markedly reduced false positives and achieves more truthful gene differential expression quantification across individual studies at both gene and study levels over the random-effects model (REM). Resilience experiments demonstrate AWmeta's remarkable stability and robustness against external and internal perturbations. Crucially, AWmeta prioritizes more tissue-contextual genes of Parkinson's and Crohn's disease with genuine pathological importance than those from REM and constituent studies. Functional enrichment analysis further verifies that these screened gene signatures capture higher contextual coherence in all analyzed disease tissues. AWmeta harmonizes heterogeneous transcriptomic datasets into reliable DEG identification and mechanistic insights, serving as an indispensable tool for precision transcriptomic integration.