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Title: | Benchmarking digital PCR partition classification methods with empirical and simulated duplex data | Authors: | Chen, Yao De Spiegelaere, Ward Trypsteen, Wim Gleerup, David Vandesompele, Jo Lievens, Antoon Vynck, Matthijs THAS, Olivier |
Issue Date: | 2024 | Publisher: | OXFORD UNIV PRESS | Source: | BRIEFINGS IN BIOINFORMATICS, 25 (3) (Art N° bbae120) | Abstract: | Digital PCR (dPCR) is a highly accurate technique for the quantification of target nucleic acid(s). It has shown great potential in clinical applications, like tumor liquid biopsy and validation of biomarkers. Accurate classification of partitions based on end-point fluorescence intensities is crucial to avoid biased estimators of the concentration of the target molecules. We have evaluated many clustering methods, from general-purpose methods to specific methods for dPCR and flowcytometry, on both simulated and real-life data. Clustering method performance was evaluated by simulating various scenarios. Based on our extensive comparison of clustering methods, we describe the limits of these methods, and formulate guidelines for choosing an appropriate method. In addition, we have developed a novel method for simulating realistic dPCR data. The method is based on a mixture distribution of a Poisson point process and a skew-$t$ distribution, which enables the generation of irregularities of cluster shapes and randomness of partitions between clusters ('rain') as commonly observed in dPCR data. Users can fine-tune the model parameters and generate labeled datasets, using their own data as a template. Besides, the database of experimental dPCR data augmented with the labeled simulated data can serve as training and testing data for new clustering methods. The simulation method is available as an R Shiny app. | Notes: | Thas, O (corresponding author), Univ Ghent, Dept Appl Math Comp Sci & Stat, B-9000 Ghent, Belgium. olivier.thas@ugent.be |
Keywords: | digital PCR; nucleic acid quantification; clustering; simulation;;nucleic acid amplification; absolute quantification; molecular;diagnostics; high-precision PCR | Document URI: | http://hdl.handle.net/1942/42855 | ISSN: | 1467-5463 | e-ISSN: | 1477-4054 | DOI: | 10.1093/bib/bbae120 | ISI #: | 001193845100005 | Rights: | The Author(s) 2024. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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Benchmarking digital PCR partition classification methods with empirical and simulated duplex data.pdf | Published version | 1.27 MB | Adobe PDF | View/Open |
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