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Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics
Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to replicability is that multiple measurements of the same item (e.g., experimental sample or clinical parti...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8500408/ https://www.ncbi.nlm.nih.gov/pubmed/34529652 http://dx.doi.org/10.1371/journal.pcbi.1009279 |
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author | Bridgeford, Eric W. Wang, Shangsi Wang, Zeyi Xu, Ting Craddock, Cameron Dey, Jayanta Kiar, Gregory Gray-Roncal, William Colantuoni, Carlo Douville, Christopher Noble, Stephanie Priebe, Carey E. Caffo, Brian Milham, Michael Zuo, Xi-Nian Vogelstein, Joshua T. |
author_facet | Bridgeford, Eric W. Wang, Shangsi Wang, Zeyi Xu, Ting Craddock, Cameron Dey, Jayanta Kiar, Gregory Gray-Roncal, William Colantuoni, Carlo Douville, Christopher Noble, Stephanie Priebe, Carey E. Caffo, Brian Milham, Michael Zuo, Xi-Nian Vogelstein, Joshua T. |
author_sort | Bridgeford, Eric W. |
collection | PubMed |
description | Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to replicability is that multiple measurements of the same item (e.g., experimental sample or clinical participant) under fixed experimental constraints are relatively similar to one another. Thus, statistics that quantify the relative contributions of accidental deviations—such as measurement error—as compared to systematic deviations—such as individual differences—are critical. We demonstrate that existing replicability statistics, such as intra-class correlation coefficient and fingerprinting, fail to adequately differentiate between accidental and systematic deviations in very simple settings. We therefore propose a novel statistic, discriminability, which quantifies the degree to which an individual’s samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean. Using this statistic, we introduce the possibility of optimizing experimental design via increasing discriminability and prove that optimizing discriminability improves performance bounds in subsequent inference tasks. In extensive simulated and real datasets (focusing on brain imaging and demonstrating on genomics), only optimizing data discriminability improves performance on all subsequent inference tasks for each dataset. We therefore suggest that designing experiments and analyses to optimize discriminability may be a crucial step in solving the replicability crisis, and more generally, mitigating accidental measurement error. |
format | Online Article Text |
id | pubmed-8500408 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-85004082021-10-09 Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics Bridgeford, Eric W. Wang, Shangsi Wang, Zeyi Xu, Ting Craddock, Cameron Dey, Jayanta Kiar, Gregory Gray-Roncal, William Colantuoni, Carlo Douville, Christopher Noble, Stephanie Priebe, Carey E. Caffo, Brian Milham, Michael Zuo, Xi-Nian Vogelstein, Joshua T. PLoS Comput Biol Research Article Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to replicability is that multiple measurements of the same item (e.g., experimental sample or clinical participant) under fixed experimental constraints are relatively similar to one another. Thus, statistics that quantify the relative contributions of accidental deviations—such as measurement error—as compared to systematic deviations—such as individual differences—are critical. We demonstrate that existing replicability statistics, such as intra-class correlation coefficient and fingerprinting, fail to adequately differentiate between accidental and systematic deviations in very simple settings. We therefore propose a novel statistic, discriminability, which quantifies the degree to which an individual’s samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean. Using this statistic, we introduce the possibility of optimizing experimental design via increasing discriminability and prove that optimizing discriminability improves performance bounds in subsequent inference tasks. In extensive simulated and real datasets (focusing on brain imaging and demonstrating on genomics), only optimizing data discriminability improves performance on all subsequent inference tasks for each dataset. We therefore suggest that designing experiments and analyses to optimize discriminability may be a crucial step in solving the replicability crisis, and more generally, mitigating accidental measurement error. Public Library of Science 2021-09-16 /pmc/articles/PMC8500408/ /pubmed/34529652 http://dx.doi.org/10.1371/journal.pcbi.1009279 Text en © 2021 Bridgeford et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Bridgeford, Eric W. Wang, Shangsi Wang, Zeyi Xu, Ting Craddock, Cameron Dey, Jayanta Kiar, Gregory Gray-Roncal, William Colantuoni, Carlo Douville, Christopher Noble, Stephanie Priebe, Carey E. Caffo, Brian Milham, Michael Zuo, Xi-Nian Vogelstein, Joshua T. Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics |
title | Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics |
title_full | Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics |
title_fullStr | Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics |
title_full_unstemmed | Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics |
title_short | Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics |
title_sort | eliminating accidental deviations to minimize generalization error and maximize replicability: applications in connectomics and genomics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8500408/ https://www.ncbi.nlm.nih.gov/pubmed/34529652 http://dx.doi.org/10.1371/journal.pcbi.1009279 |
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