  Alzheimer's disease (AD) is a pervasive neurodegenerative disorder that leads
to memory and behavior impairment severe enough to interfere with daily life
activities. Understanding this disease pathogenesis can drive the development
of new targets and strategies to prevent and treat AD. Recent advances in
high-throughput single-cell RNA sequencing technology (scRNA-seq) have enabled
the generation of massive amounts of transcriptomic data at the single-cell
level provided remarkable insights into understanding the molecular
pathogenesis of Alzheimer's disease. In this study, we introduce ScAtt, an
innovative Attention-based architecture, devised specifically for the
concurrent identification of cell-type specific AD-related genes and their
associated gene regulatory network. ScAtt incorporates a flexible model capable
of capturing nonlinear effects, leading to the detection of AD-associated genes
that might be overlooked by traditional differentially expressed gene (DEG)
analyses. Moreover, ScAtt effectively infers a gene regulatory network
depicting the combined influences of genes on the targeted disease, as opposed
to examining correlations among genes in conventional gene co-expression
networks. In an application to 95,186 single-nucleus transcriptomes from 17
hippocampus samples, ScAtt shows substantially better performance in modeling
quantitative changes in expression levels between AD and healthy controls.
Consequently, ScAtt performs better than existing methods in the identification
of AD-related genes, with more unique discoveries and less overlap between cell
types. Functional enrichments of the corresponding gene modules detected from
gene regulatory network show significant enrichment of biologically meaningful
AD-related pathways across different cell types.
