Qi, GuanghaoCui, Tengfei2025-08-012025-08-012025-08-012025Cui_washington_0250O_28346.pdfhttps://hdl.handle.net/1773/53423Thesis (Master's)--University of Washington, 2025Allele-specific expression (ASE) is a powerful signal to study cis-regulatory effects. We previously developed DAESC, a statistical method for single-cell differential ASE analysis across multiple individuals. Despite improved power, the lack of computational efficiency limits its utility on large-scale datasets. Here, we present DAESC-GPU, an accelerated version of DAESC powered by Graphics Processing Units (GPUs). DAESC-GPU is dozens of times faster than DAESC and scalable to datasets of over a million cells. Application of the software on single-cell ASE data from the OneK1K cohort identified novel genes with regulatory patterns specific to naïve and central memory CD4+ T cells.application/pdfen-USnonescASESingle-cell Allele-spcific Gene Expression AnalysisStatistical genetics and genomicsBiostatisticsBiostatisticsThesis