ArticleIEEE open journal of signal processing2021
A Compressed Sensing Approach to Pooled RT-PCR Testing for COVID-19 Detection.
Article in IEEE open journal of signal processing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- High capacity clinical SARS-CoV-2 molecular testing using combinatorial pooling.Communications medicine · 2024Article
- Semi-quantitative group testing for efficient and accurate qPCR screening of pathogens with a wide range of loads.BMC bioinformatics · 2024Article
- Effective matrix designs for COVID-19 group testing.BMC bioinformatics · 2023Review
- Statistical modeling and evaluation of the impact of multiplicity classification thresholds on the COVID-19 pool testing accuracy.PloS one · 2023Article
- Dorfman pooling enhances SARS-CoV-2 large-scale community testing efficiency.PLOS global public health · 2023Article
- Use of compressed sensing to expedite high-throughput diagnostic testing for COVID-19 and beyond.PLoS computational biology · 2022Article
- Deep learning-based approach for detecting COVID-19 in chest X-rays.Biomedical signal processing and control · 2022Article
- Evaluation of efficiency and sensitivity of 1D and 2D sample pooling strategies for SARS-CoV-2 RT-qPCR screening purposes.Scientific reports · 2022Article
- Group testing via hypergraph factorization applied to COVID-19.Nature communications · 2022Article
- The challenges of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing in low-middle income countries and possible cost-effective measures in resource-limited settings.Globalization and health · 2022Review
- Extreme Compressed Sensing of Poisson Rates from Multiple Measurements.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2022Article
- Use of compressed sensing to expedite high-throughput diagnostic testing for COVID-19 and beyond.medRxiv : the preprint server for health sciences · 2021Article
- Optimization in the Context of COVID-19 Prediction and Control: A Literature Review.IEEE access : practical innovations, open solutions · 2021Article
- Rapid, large-scale, and effective detection of COVID-19 via non-adaptive testing.Journal of theoretical biology · 2020Article
- Considerations for Group Testing: A Practical Approach for the Clinical Laboratory.The Clinical biochemist. Reviews · 2020Review
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose 'Tapestry', a single-round pooled testing method with application to COVID-19 testing using quantitative Reverse Transcription Polymerase Chain Reaction (RT-PCR) that can result in shorter testing time and conservation of reagents and testing kits, at clinically acceptable false positive or false negative rates. Tapestry combines ideas from compressed sensing and combinatorial group testing to create a new kind of algorithm that is very effective in deconvoluting pooled tests. Unlike Boolean group testing algorithms, the input is a quantitative readout from each test and the output is a list of viral loads for each sample relative to the pool with the highest viral load. For guaranteed recovery of [Formula: see text] infected samples out of [Formula: see text] being tested, Tapestry needs only [Formula: see text] tests with high probability, using random binary pooling matrices. However, we propose deterministic binary pooling matrices based on combinatorial design ideas of Kirkman Triple Systems, which balance between good reconstruction properties and matrix sparsity for ease of pooling while requiring fewer tests in practice. This enables large savings using Tapestry at low prevalence rates while maintaining viability at prevalence rates as high as 9.5%. Empirically we find that single-round Tapestry pooling improves over two-round Dorfman pooling by almost a factor of 2 in the number of tests required. We evaluate Tapestry in simulations with synthetic data obtained using a novel noise model for RT-PCR, and validate it in wet lab experiments with oligomers in quantitative RT-PCR assays. Lastly, we describe use-case scenarios for deployment.
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