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Despite the breakthroughs in biomarker discovery facilitated by differentialgene 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 criticalinteractions between biological conditions, such as healthy versus diseasedstates. In response, here we introduce scBeacon, an innovative framework builtupon a deep contrastive siamese network. scBeacon pioneers an unsupervisedapproach, adeptly identifying matched cell populations across variedconditions, enabling a refined differential gene analysis. By utilizing aVQ-VAE framework, a contrastive siamese network, and a greedy iterativestrategy, scBeacon effectively pinpoints differential genes that hold potentialas key biomarkers. Comprehensive evaluations on a diverse array of datasetsvalidate scBeacon's superiority over existing single-cell differential geneanalysis tools. Its precision and adaptability underscore its significant rolein enhancing diagnostic accuracy in biomarker discovery. With the emphasis onthe importance of biomarkers in diagnosis, scBeacon is positioned to be apivotal asset in the evolution of personalized medicine and targetedtreatments.

scBeacon: single-cell biomarker extraction via identifying paired cell clusters across biological conditions with contrastive siamese networks