ArticleAnalytical chemistry2017
Robust Multiplexed Clustering and Denoising of Digital PCR Assays by Data Gridding.
Article in Analytical chemistry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Digital PCR threshold robustness analysis and optimization using dipcensR.Briefings in bioinformatics · 2024Article
- Benchmarking digital PCR partition classification methods with empirical and simulated duplex data.Briefings in bioinformatics · 2024Article
- Subfamily-specific quantification of endogenous mouse L1 retrotransposons by droplet digital PCR.Analytical biochemistry · 2020Article
- Therapeutic Monitoring of Circulating DNA Mutations in Metastatic Cancer with Personalized Digital PCR.The Journal of molecular diagnostics : JMD · 2020Article
- dPCR: A Technology Review.Sensors (Basel, Switzerland) · 2018Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Digital PCR (dPCR) relies on the analysis of individual partitions to accurately quantify nucleic acid species. The most widely used analysis method requires manual clustering through individual visual inspection. Some automated analysis methods have emerged but do not robustly account for multiplexed targets, low target concentration, and assay noise. In this study, we describe an open source analysis software called Calico that uses "data gridding" to increase the sensitivity of clustering toward small clusters. Our workflow also generates quality score metrics in order to gauge and filter individual assay partitions by how well they were classified. We applied our analysis algorithm to multiplexed droplet-based digital PCR data sets in both EvaGreen and probes-based schemes, and targeted the oncogenic BRAF V600E and KRAS G12D mutations. We demonstrate an automated clustering sensitivity of down to 0.1% mutant fraction and filtering of artifactual assay partitions from low quality DNA samples. Overall, we demonstrate a vastly improved approach to analyzing ddPCR data that can be applied to clinical use, where automation and reproducibility are critical.
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Registered trials
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