TY - JOUR
T1 - Eliminating accidental deviations to minimize generalization error and maximize replicability
T2 - Applications in connectomics and genomics
AU - Bridgeford, Eric W.
AU - Wang, Shangsi
AU - Wang, Zeyi
AU - Xu, Ting
AU - Craddock, Cameron
AU - Dey, Jayanta
AU - Kiar, Gregory
AU - Gray-Roncal, William
AU - Colantuoni, Carlo
AU - Douville, Christopher
AU - Noble, Stephanie
AU - Priebe, Carey E.
AU - Caffo, Brian
AU - Milham, Michael
AU - Zuo, Xi Nian
AU - Vogelstein, Joshua T.
N1 - Publisher Copyright:
Copyright: © 2021 Bridgeford et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2021/9
Y1 - 2021/9
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pcbi.1009279
DO - 10.1371/journal.pcbi.1009279
M3 - Article
C2 - 34529652
AN - SCOPUS:85115234007
SN - 1553-734X
VL - 17
JO - PLoS computational biology
JF - PLoS computational biology
IS - 9
M1 - e1009279
ER -