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nGauge: Integrated and Extensible Neuron Morphology Analysis in Python

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Abstract

The study of neuron morphology requires robust and comprehensive methods to quantify the differences between neurons of different subtypes and animal species. Several software packages have been developed for the analysis of neuron tracing results stored in the standard SWC format. The packages, however, provide relatively simple quantifications and their non-extendable architecture prohibit their use for advanced data analysis and visualization. We developed nGauge, a Python toolkit to support the parsing and analysis of neuron morphology data. As an application programming interface (API), nGauge can be referenced by other popular open-source software to create custom informatics analysis pipelines and advanced visualizations. nGauge defines an extendable data structure that handles volumetric constructions (e.g. soma), in addition to the SWC linear reconstructions, while remaining lightweight. This greatly extends nGauge’s data compatibility.

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Notes

  1. https://docs.python.org/3/library/unittest.html

  2. https://docs.python.org/3/library/timeit.html

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Acknowledgements

JSW received support from the University of Michigan Women in Science and Engineering Residence Program (WISE-RP) Judith Cram Memorial Fund Research Award. WJL received support from the Magnificent Michigan Fellowship. LAW and DC received support from NSF-1707316 (Neuronex-MINT), NIH-RF1MH123402, and NIH-RF1MH124611. The authors thank Fred Shen for his comments on an early version of the library. LAW thanks Chris Midkiff for his comments on figure design and example code clarity.

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Author notes

  1. Logan A. Walker and Jennifer S. Williams contributed equally on this work

Authors and Affiliations

  1. Biophysics Program, University of Michigan LS&A, Ann Arbor, MI, USA

    Logan A. Walker, Nigel S. Michki & Dawen Cai

  2. Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA

    Logan A. Walker

  3. Electrical Engineering and Computer Science, University of Michigan Engineering, Ann Arbor, MI, USA

    Jennifer S. Williams & Wei Jie Lee

  4. Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI, USA

    Ye Li & Dawen Cai

  5. Department of Biology, Ball State University, Muncie, IN, USA

    Douglas H. Roossien

  6. Neuroscience Graduate Program, University of Michigan LS&A, Ann Arbor, MI, USA

    Dawen Cai

Authors
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Contributions

LAW and DC conceptualized the nGauge library, which was then implemented by LAW and JSW. YL and DR provided imaging and neuron reconstruction datasets which were used in library testing. LAW, JSW, YL, WJL, and NM contributed to beta testing of early versions of nGauge and provided comments on the library design. LAW, JSW, and DC wrote the manuscript, which was edited and approved by all authors.

Corresponding author

Correspondence to Dawen Cai.

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Supplementary Information

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12021_2022_9573_MOESM1_ESM.eps

Supplementary file 1 (EPS 1169 KB) Supplementary Figure 1 Extended L-Measure Comparison. A collection of 13 functions highlighted in Supplementary Table 1 are compared between nGauge (xaxis) and L-Measure (y-axis) by calculating each metric in both pieces of software. We find that all functions perform as expected.

12021_2022_9573_MOESM2_ESM.xlsx

Supplementary file 2 (XLSX 16 KB) Supplementary Table 1 Implemented nGauge Functions. All functions implemented in nGauge. Along the left-hand side, functions are divided into different modules and function types. For each function, we have identified the closest equivalent function in LnGauge Walker LA, Williams JS, et al. Measure, MorphoPy, and TREEs toolbox, however, not all functions are exact matches due to differencesin API design between all four programs. As a result, in some cases, summations or other summary functions are required to make results match between each program.

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Walker, L.A., Williams, J.S., Li, Y. et al. nGauge: Integrated and Extensible Neuron Morphology Analysis in Python. Neuroinform 20, 755–764 (2022). https://doi.org/10.1007/s12021-022-09573-8

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