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Single-cell machine learning uncovers genetically anchored, cell-type specific programs of Alzheimer’s disease
Univariate analyses alone cannot resolve multivariate disease-associated structure. Here, the authors develop TriSCOPE, a machine learning approach that identifies gene programs specific to individual brain cell types in Alzheimer’s disease.
Univariate differential expression analysis dominates single-cell transcriptomic studies of Alzheimer’s disease, yet marginal testing alone cannot resolve multivariate disease-associated structure or distinguish clinical from neuropathological signatures. Here we introduce TriSCOPE, a framework integrating multivariate predictive modeling, donor-aware differential expression, and pseudotime inference, applied to six Alzheimer’s disease snRNA-seq datasets comprising ~4.2 million nuclei from 627 donors. Across six major brain cell types and 41 subclusters, we identify cell-type-specific predictively expressed genes that are largely non-overlapping with differentially expressed genes, revealing disease-relevant signals invisible to univariate approaches. Conditional GWAS-eQTL colocalization anchors a subset of these predictively expressed genes to inherited Alzheimer’s disease risk loci, including 27 genetically supported gene-cell type pairs (among them ARL17B , JAZF1 ) with no significant differential expression. […]
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