abstract

Digital PCR (dPCR) is an accurate method to quantify the target nucleic acids. The study addresses key challenges in the analysis of dPCR data, with a focus on developing and evaluating methods to improve the accuracy of clustering, variance estimation, and practical application. Current dPCR analysis tools often fall short in handling multi-color data, lack comprehensive benchmarking studies, and fail to provide clear guidelines for end users. To bridge these gaps, this work proposes a robust and versatile pipeline that enhances the analysis of various quantities of interest, such as copy number variation (CNV) and DNA integrity. With its comprehensive pipeline, this thesis aims to equip researchers with the tools and knowledge necessary for accurate and reliable dPCR quantification, contributing to advancements in the analysis of dPCR data.