Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

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

Eric W. Bridgeford, Shangsi Wang, Zhi Yang, Zeyi Wang, Ting Xu, Cameron Craddock, Jayanta Dey, Gregory Kiar, William Gray-Roncal, Carlo Colantuoni, Christopher Douville, Stephanie Noble, Carey E. Priebe, Brian Caffo, Michael Milham, Xi-Nian Zuo, Consortium for Reliability and Reproducibility, View ORCID ProfileJoshua T. Vogelstein
doi: https://doi.org/10.1101/802629
Eric W. Bridgeford
1Johns Hopkins University, Baltimore, Maryland, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Shangsi Wang
1Johns Hopkins University, Baltimore, Maryland, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zhi Yang
2Shanghai Jiaotong University, Shanghai, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zeyi Wang
1Johns Hopkins University, Baltimore, Maryland, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ting Xu
3Child Mind Institute, New York, New York, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Cameron Craddock
3Child Mind Institute, New York, New York, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jayanta Dey
1Johns Hopkins University, Baltimore, Maryland, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gregory Kiar
1Johns Hopkins University, Baltimore, Maryland, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
William Gray-Roncal
1Johns Hopkins University, Baltimore, Maryland, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Carlo Colantuoni
1Johns Hopkins University, Baltimore, Maryland, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Christopher Douville
1Johns Hopkins University, Baltimore, Maryland, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Stephanie Noble
4Yale University, New Haven, Connecticut, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Carey E. Priebe
1Johns Hopkins University, Baltimore, Maryland, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Brian Caffo
1Johns Hopkins University, Baltimore, Maryland, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michael Milham
3Child Mind Institute, New York, New York, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xi-Nian Zuo
2Shanghai Jiaotong University, Shanghai, China
5Beijing Normal University, Beijing, China, Nanning Normal University, Nanning, China University of Chinese Academy of Sciences, Beijing, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Joshua T. Vogelstein
1Johns Hopkins University, Baltimore, Maryland, USA
6Progressive Learning, Baltimore, Maryland, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Joshua T. Vogelstein
  • For correspondence: jovo@jhu.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

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.

Author Summary In recent decades, the size and complexity of data has grown exponentially. Unfortunately, the increased scale of modern datasets brings many new challenges. At present, we are in the midst of a replicability crisis, in which scientific discoveries fail to replicate to new datasets. Difficulties in the measurement procedure and measurement processing pipelines coupled with the influx of complex high-resolution measurements, we believe, are at the core of the replicability crisis. If measurements themselves are not replicable, what hope can we have that we will be able to use the measurements for replicable scientific findings? We introduce the “discriminability” statistic, which quantifies how discriminable measurements are from one another, without limitations on the structure of the underlying measurements. We prove that discriminable strategies tend to be strategies which provide better accuracy on downstream scientific questions. We demonstrate the utility of discriminability over competing approaches in this context on two disparate datasets from both neuroimaging and genomics. Together, we believe these results suggest the value of designing experimental protocols and analysis procedures which optimize the discriminability.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Revised according to new feedback

  • https://neurodata.io/mgc

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted July 24, 2021.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Eliminating accidental deviations to minimize generalization error and maximize replicability: applications in connectomics and genomics
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Eliminating accidental deviations to minimize generalization error and maximize replicability: applications in connectomics and genomics
Eric W. Bridgeford, Shangsi Wang, Zhi Yang, Zeyi Wang, Ting Xu, Cameron Craddock, Jayanta Dey, Gregory Kiar, William Gray-Roncal, Carlo Colantuoni, Christopher Douville, Stephanie Noble, Carey E. Priebe, Brian Caffo, Michael Milham, Xi-Nian Zuo, Consortium for Reliability and Reproducibility, Joshua T. Vogelstein
bioRxiv 802629; doi: https://doi.org/10.1101/802629
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Eliminating accidental deviations to minimize generalization error and maximize replicability: applications in connectomics and genomics
Eric W. Bridgeford, Shangsi Wang, Zhi Yang, Zeyi Wang, Ting Xu, Cameron Craddock, Jayanta Dey, Gregory Kiar, William Gray-Roncal, Carlo Colantuoni, Christopher Douville, Stephanie Noble, Carey E. Priebe, Brian Caffo, Michael Milham, Xi-Nian Zuo, Consortium for Reliability and Reproducibility, Joshua T. Vogelstein
bioRxiv 802629; doi: https://doi.org/10.1101/802629

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4684)
  • Biochemistry (10361)
  • Bioengineering (7677)
  • Bioinformatics (26337)
  • Biophysics (13530)
  • Cancer Biology (10687)
  • Cell Biology (15444)
  • Clinical Trials (138)
  • Developmental Biology (8498)
  • Ecology (12821)
  • Epidemiology (2067)
  • Evolutionary Biology (16863)
  • Genetics (11400)
  • Genomics (15480)
  • Immunology (10617)
  • Microbiology (25221)
  • Molecular Biology (10224)
  • Neuroscience (54476)
  • Paleontology (402)
  • Pathology (1668)
  • Pharmacology and Toxicology (2897)
  • Physiology (4343)
  • Plant Biology (9248)
  • Scientific Communication and Education (1586)
  • Synthetic Biology (2558)
  • Systems Biology (6781)
  • Zoology (1466)