Journal article
Group Testing Matrix Design for PCR Screening with Real-Valued Measurements
JOURNAL OF COMPUTATIONAL BIOLOGY, v 29(12), p1397
01 Dec 2022
PMID: 36450118
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Single-step nonadaptive group testing approaches for reducing the number of tests required to detect a small subset of positive samples from a larger set require solving two algorithmic problems. First, how to design the samples-to-tests measurement matrix, and second, how to decode the results of the tests to uncover positive samples. In this study, we focus on the first challenge. We introduce real-valued group testing, which matches the characteristics of existing PCR testing pipelines more closely than combinatorial group testing or compressed sensing settings. We show a set of conditions that allow measurement matrices to guarantee unambiguous decoding of positives in this new setting. For small matrix sizes, we also propose an algorithm for constructing matrices that meet the proposed condition. On simulated data sets, we show that the matrices resulting from the algorithm can successfully recover positive samples at higher positivity rates than matrices designed for combinatorial group testing setting. We use wet laboratory experiments involving SARS-CoV-2 nasopharyngeal swab samples to further validate the approach.
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Details
- Title
- Group Testing Matrix Design for PCR Screening with Real-Valued Measurements
- Publication Details
- JOURNAL OF COMPUTATIONAL BIOLOGY, v 29(12), p1397
- Publisher
- MARY ANN LIEBERT, INC; NEW ROCHELLE
- Grant note
- T.A., G.A.B., and M.S. are funded by National Science Foundation (NSF) grant CBET-2034995.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:000892570000001
- Scopus ID
- 2-s2.0-85144589304
- Other Identifier
- 991021860721104721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Statistics & Probability