ArticleBioinformatics (Oxford, England)2018
ddPCRclust: an R package and Shiny app for automated analysis of multiplexed ddPCR data.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Chromatography in Downstream Processing of Recombinant Adeno-Associated Viruses: A Review of Current and Future Practises.Biotechnology and bioengineering · 2025Review
- Polytect: an automatic clustering and labeling method for multicolor digital PCR data.NAR genomics and bioinformatics · 2025Article
- Considerations for the Successful Detection and Quantification of Genetically Modified Events in Grain and Food Samples Using Multiplex Digital PCR.Foods (Basel, Switzerland) · 2024Article
- Benchmarking digital PCR partition classification methods with empirical and simulated duplex data.Briefings in bioinformatics · 2024Article
- Expanded Multiplexing on Sensor-Constrained Microfluidic Partitioning Systems.Analytical chemistry · 2023Article
- Digital PCR cluster predictor: a universal R-package and shiny app for the automated analysis of multiplex digital PCR data.Bioinformatics (Oxford, England) · 2023Article
- Skin Cancer Research Goes Digital: Looking for Biomarkers within the Droplets.Journal of personalized medicine · 2022Review
- Computational challenges in detection of cancer using cell-free DNA methylation.Computational and structural biotechnology journal · 2022Review
- Generic Multiplex Digital PCR for Accurate Quantification of T Cells in Copy Number Stable and Unstable DNA Samples.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Machine learning algorithm improved automated droplet classification of ddPCR for detection of BRAF V600E in paraffin-embedded samples.Scientific reports · 2021Article
- Validation Strategy for Ultrasensitive Mutation Detection.Molecular diagnosis & therapy · 2018Article
- Mitigation of the effect of variability in digital PCR assays through use of duplexed reference assays for normalization.BioTechniques · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Droplet digital PCR (ddPCR) is an emerging technology for quantifying DNA. By partitioning the target DNA into ∼20 000 droplets, each serving as its own PCR reaction compartment, a very high sensitivity of DNA quantification can be achieved. However, manual analysis of the data is time consuming and algorithms for automated analysis of non-orthogonal, multiplexed ddPCR data are unavailable, presenting a major bottleneck for the advancement of ddPCR transitioning from low-throughput to high-throughput. Results: ddPCRclust is an R package for automated analysis of data from Bio-Rad's droplet digital PCR systems (QX100 and QX200). It can automatically analyze and visualize multiplexed ddPCR experiments with up to four targets per reaction. Results are on par with manual analysis, but only take minutes to compute instead of hours. The accompanying Shiny app ddPCRvis provides easy access to the functionalities of ddPCRclust through a web-browser based GUI. Availability and implementation: R package: https://github.com/bgbrink/ddPCRclust; Interface: https://github.com/bgbrink/ddPCRvis/; Web: https://bibiserv.cebitec.uni-bielefeld.de/ddPCRvis/. Supplementary information: Supplementary data are available at Bioinformatics online.
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Registered trials
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