📝 SummarXiv

Abstract

In large screening campaigns, group testing can greatly reduce the number of tests needed when compared to testing each sample individually. However, choosing and applying an appropriate group testing method remains challenging due to the wide variety in design and performance across methods, and the lack of accessible tools. Here, we present PoolPy, a unified framework for designing and selecting optimal group testing strategies across ten different methods according to user-defined constraints, such as time, cost or sample dilution. By computing over 10,000 group testing designs made available through a web interface, we identified key trade-offs, such as minimizing test number or group size, that define applicability to specific use cases. Overall, we show that no single method is universally optimal, and provide clear indications for method choice on a case-by-case basis.