Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics

Author:

Bridgeford Eric W.ORCID,Wang ShangsiORCID,Wang ZeyiORCID,Xu TingORCID,Craddock CameronORCID,Dey JayantaORCID,Kiar GregoryORCID,Gray-Roncal WilliamORCID,Colantuoni Carlo,Douville ChristopherORCID,Noble StephanieORCID,Priebe Carey E.ORCID,Caffo Brian,Milham MichaelORCID,Zuo Xi-NianORCID,Vogelstein Joshua T.ORCID,

Abstract

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.

Funder

National Science Foundation

National Institutes of Health

Defense Advanced Research Projects Agency

Microsoft Research

National Basic Research Program of China

Natural Science Foundation of China

China Netherlands CAS-NWO Programme

Beijing Municipal Science and Tech Commission

Start-up Funds for Leading Talents at Beijing Normal University, the Major Project of National Social Science Foundation of China

National Basic Science Data Center “Chinese Data-sharing Warehouse for In-vivo Imaging Brain”

Guangxi Bagui Scholarship

Publisher

Public Library of Science (PLoS)

Subject

Computational Theory and Mathematics,Cellular and Molecular Neuroscience,Genetics,Molecular Biology,Ecology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics

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