1. Computational and Systems Biology
  2. Neuroscience
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The natverse, a versatile toolbox for combining and analysing neuroanatomical data

  1. Alexander S Bates
  2. James D Manton
  3. Sridhar R Jagannathan
  4. Marta Costa
  5. Philipp Schlegel
  6. Torsten Rohlfing
  7. Gregory SXE Jefferis  Is a corresponding author
  1. MRC Laboratory of Molecular Biology, United Kingdom
  2. University of Cambridge, United Kingdom
  3. SRI International, Neuroscience Program, United States
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Cite this article as: eLife 2020;9:e53350 doi: 10.7554/eLife.53350

Abstract

To analyse neuron data at scale, neuroscientists expend substantial effort reading documentation, installing dependencies and moving between analysis and visualisation environments. To facilitate this, we have developed a suite of interoperable open-source R packages called the natverse. The natverse allows users to read local and remote data, perform popular analyses including visualisation and clustering and graph-theoretic analysis of neuronal branching. Unlike most tools, the natverse enables comparison across many neurons of morphology and connectivity after imaging or co-registration within a common template space. The natverse also enables transformations between different template spaces and imaging modalities. We demonstrate tools that integrate the vast majority of Drosophila neuroanatomical light microscopy and electron microscopy connectomic datasets. The natverse is an easy-to-use environment for neuroscientists to solve complex, large-scale analysis challenges as well as an open platform to create new code and packages to share with the community.

Data availability

All code is described at natverse.org which links to individual git repositories at github.com/natverse.

Article and author information

Author details

  1. Alexander S Bates

    Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1195-0445
  2. James D Manton

    Neurobiology Divison, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9260-3156
  3. Sridhar R Jagannathan

    Department of Zoology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Marta Costa

    Department of Zoology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5948-3092
  5. Philipp Schlegel

    Division of Neurobiology, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5633-1314
  6. Torsten Rohlfing

    SRI International, Neuroscience Program, Menlo Park, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Gregory SXE Jefferis

    Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
    For correspondence
    jefferis@mrc-lmb.cam.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0587-9355

Funding

Medical Research Council (MC-U105188491)

  • Alexander S Bates
  • James D Manton
  • Gregory SXE Jefferis

H2020 European Research Council (649111)

  • Alexander S Bates
  • James D Manton
  • Marta Costa
  • Gregory SXE Jefferis

Wellcome (203261/Z/16/Z)

  • Sridhar R Jagannathan
  • Marta Costa
  • Philipp Schlegel
  • Gregory SXE Jefferis

Boehringer Ingelheim Fonds

  • Alexander S Bates

Herchel Smith Fund

  • Alexander S Bates

Fitzwilliam College

  • James D Manton

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. K VijayRaghavan, National Centre for Biological Sciences, Tata Institute of Fundamental Research, India

Publication history

  1. Received: November 5, 2019
  2. Accepted: April 11, 2020
  3. Accepted Manuscript published: April 14, 2020 (version 1)
  4. Version of Record published: May 21, 2020 (version 2)

Copyright

© 2020, Bates et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Ling-Qi Zhang et al.
    Research Article

    We developed an image-computable observer model of the initial visual encoding that operates on natural image input, based on the framework of Bayesian image reconstruction from the excitations of the retinal cone mosaic. Our model extends previous work on ideal observer analysis and evaluation of performance beyond psychophysical discrimination, takes into account the statistical regularities of the visual environment, and provides a unifying framework for answering a wide range of questions regarding the visual front end. Using the error in the reconstructions as a metric, we analyzed variations of the number of different photoreceptor types on human retina as an optimal design problem. In addition, the reconstructions allow both visualization and quantification of information loss due to physiological optics and cone mosaic sampling, and how these vary with eccentricity. Furthermore, in simulations of color deficiencies and interferometric experiments, we found that the reconstructed images provide a reasonable proxy for modeling subjects' percepts. Lastly, we used the reconstruction-based observer for the analysis of psychophysical threshold, and found notable interactions between spatial frequency and chromatic direction in the resulting spatial contrast sensitivity function. Our method is widely applicable to experiments and applications in which the initial visual encoding plays an important role.

    1. Computational and Systems Biology
    2. Medicine
    Xuan Xu et al.
    Research Article Updated

    Background:

    Potential therapy and confounding factors including typical co‐administered medications, patient’s disease states, disease prevalence, patient demographics, medical histories, and reasons for prescribing a drug often are incomplete, conflicting, missing, or uncharacterized in spontaneous adverse drug event (ADE) reporting systems. These missing or incomplete features can affect and limit the application of quantitative methods in pharmacovigilance for meta-analyses of data during randomized clinical trials.

    Methods:

    Data from patients with hypertension were retrieved and integrated from the FDA Adverse Event Reporting System; 134 antihypertensive drugs out of 1131 drugs were filtered and then evaluated using the empirical Bayes geometric mean (EBGM) of the posterior distribution to build ADE-drug profiles with an emphasis on the pulmonary ADEs. Afterward, the graphical least absolute shrinkage and selection operator (GLASSO) captured drug associations based on pulmonary ADEs by correcting hidden factors and confounder misclassification. Selected drugs were then compared using the Friedman test in drug classes and clusters obtained from GLASSO.

    Results:

    Following multiple filtering stages to exclude insignificant and noise-driven reports, we found that drugs from antihypertensives agents, urologicals, and antithrombotic agents (macitentan, bosentan, epoprostenol, selexipag, sildenafil, tadalafil, and beraprost) form a similar class with a significantly higher incidence of pulmonary ADEs. Macitentan and bosentan were associated with 64% and 56% of pulmonary ADEs, respectively. Because these two medications are prescribed in diseases affecting pulmonary function and may be likely to emerge among the highest reported pulmonary ADEs, in fact, they serve to validate the methods utilized here. Conversely, doxazosin and rilmenidine were found to have the least pulmonary ADEs in selected drugs from hypertension patients. Nifedipine and candesartan were also found by signal detection methods to form a drug cluster, shown by several studies an effective combination of these drugs on lowering blood pressure and appeared an improved side effect profile in comparison with single-agent monotherapy.

    Conclusions:

    We consider pulmonary ADE profiles in multiple long-standing groups of therapeutics including antihypertensive agents, antithrombotic agents, beta-blocking agents, calcium channel blockers, or agents acting on the renin-angiotensin system, in patients with hypertension associated with high risk for coronavirus disease 2019 (COVID-19). We found that several individual drugs have significant differences between their drug classes and compared to other drug classes. For instance, macitentan and bosentan from endothelin receptor antagonists show major concern while doxazosin and rilmenidine exhibited the least pulmonary ADEs compared to the outcomes of other drugs. Using techniques in this study, we assessed and confirmed the hypothesis that drugs from the same drug class could have very different pulmonary ADE profiles affecting outcomes in acute respiratory illness.

    Funding:

    GJW and MJD accepted funding from BioNexus KC for funding on this project, but BioNexus KC had no direct role in this article.