Despite the breakthroughs in biomarker discovery facilitated by differential gene analysis, challenges remain, particularly at the single-cell level. Traditional methodologies heavily rely on user-supplied cell annotations, focusing on individually expressed data, often neglecting the critical interactions between biological conditions, such as healthy versus diseased states. In response, here we introduce scBeacon, an innovative framework built upon a deep contrastive siamese network. scBeacon pioneers an unsupervised approach, adeptly identifying matched cell populations across varied conditions, enabling a refined differential gene analysis. By utilizing a VQ-VAE framework, a contrastive siamese network, and a greedy iterative strategy, scBeacon effectively pinpoints differential genes that hold potential as key biomarkers. Comprehensive evaluations on a diverse array of datasets validate scBeacon's superiority over existing single-cell differential gene analysis tools. Its precision and adaptability underscore its significant role in enhancing diagnostic accuracy in biomarker discovery. With the emphasis on the importance of biomarkers in diagnosis, scBeacon is positioned to be a pivotal asset in the evolution of personalized medicine and targeted treatments.

翻译：尽管差异基因分析推动了生物标志物发现取得突破性进展，但在单细胞层面仍面临诸多挑战。传统方法严重依赖用户提供的细胞注释信息，且仅关注单个表达数据，常忽略健康与疾病状态等生物学条件间的关键相互作用。为此，本文提出scBeacon——一种基于深度对比孪生网络的创新框架。scBeacon开创性地采用无监督方法，能够智能识别不同条件下匹配的细胞群体，实现精准的差异基因分析。通过融合VQ-VAE框架、对比孪生网络与贪心迭代策略，scBeacon有效锁定具有生物标志物潜力的差异基因。经多组不同数据集的全面评估，scBeacon在性能上超越现有单细胞差异基因分析工具，其精准度与适应性充分彰显了其在提升生物标志物发现诊断准确性方面的重要价值。鉴于生物标志物在诊断中的关键作用，scBeacon有望成为个性化医疗与靶向治疗发展进程中的核心工具。