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

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

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Title: Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization
Author: Glaser, Joshua I.; Zamft, Bradley M.; Church, George M.; Kording, Konrad P.

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Citation: Glaser, Joshua I., Bradley M. Zamft, George M. Church, and Konrad P. Kording. 2015. “Puzzle Imaging: Using Large-Scale Dimensionality Reduction Algorithms for Localization.” PLoS ONE 10 (7): e0131593. doi:10.1371/journal.pone.0131593. http://dx.doi.org/10.1371/journal.pone.0131593.
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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.
Published Version: doi:10.1371/journal.pone.0131593
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4507868/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:17820963
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