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Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization

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  • Joshua I Glaser
  • Bradley M Zamft
  • George M Church
  • Konrad P Kording

Abstract

Current high-resolution imaging techniques require an intact sample that preserves spatial relationships. We here present a novel approach, “puzzle imaging,” that allows imaging a spatially scrambled sample. This technique takes many spatially disordered samples, and then pieces them back together using local properties embedded within the sample. We show that puzzle imaging can efficiently produce high-resolution images using dimensionality reduction algorithms. We demonstrate the theoretical capabilities of puzzle imaging in three biological scenarios, showing that (1) relatively precise 3-dimensional brain imaging is possible; (2) the physical structure of a neural network can often be recovered based only on the neural connectivity matrix; and (3) a chemical map could be reproduced using bacteria with chemosensitive DNA and conjugative transfer. The ability to reconstruct scrambled images promises to enable imaging based on DNA sequencing of homogenized tissue samples.

Suggested Citation

  • Joshua I Glaser & Bradley M Zamft & George M Church & Konrad P Kording, 2015. "Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-23, July.
    • Handle: RePEc:plo:pone00:0131593
    DOI: 10.1371/journal.pone.0131593
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    References listed on IDEAS

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    1. J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, March.
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    1. Thaddeus R Cybulski & Edward S Boyden & George M Church & Keith E J Tyo & Konrad P Kording, 2017. "Nucleotide-time alignment for molecular recorders," PLOS Computational Biology, Public Library of Science, vol. 13(5), pages 1-22, May.

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