Interpretable integration of unpaired multi-omics for Alzheimer’s diagnosis via cross-modal transformer reconstruction
Fig 3
Interpretable AE-Trans Analysis via Counterfactual Integrated Gradients with Key Feature Highlight.
The model utilizes Integrated Gradients (IG) to attribute predictive outputs to specific multi-omics features. (A-B) Global attribution profiles. Density plots showing the highly skewed distribution of IG scores for RNA (A) and DNA methylation (B) features, where the majority of predictive weight is concentrated in a small subset of features. (C) Quantitative ranking of top features. Bar charts illustrating the top 20 biomarkers prioritized by their average attribution scores and ranking stability across five-fold cross-validation. (D) Statistical significance of top biomarkers. Comparison of expression and methylation levels between AD and Control groups for the top-ranked features, with significance determined by Wilcoxon rank-sum tests and Benjamini-Hochberg FDR correction. (E) Heatmap of candidate biomarkers. Visualization of differential expression and methylation patterns for the identified top features, showing clear group separation and consistent biological signals across AD and non-AD samples.