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BARcode DEmixing through Non-negative Spatial Regression (BarDensr)

Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many voxels...

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Detalles Bibliográficos
Autores principales: Chen, Shuonan, Loper, Jackson, Chen, Xiaoyin, Vaughan, Alex, Zador, Anthony M., Paninski, Liam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7971881/
https://www.ncbi.nlm.nih.gov/pubmed/33684106
http://dx.doi.org/10.1371/journal.pcbi.1008256
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author Chen, Shuonan
Loper, Jackson
Chen, Xiaoyin
Vaughan, Alex
Zador, Anthony M.
Paninski, Liam
author_facet Chen, Shuonan
Loper, Jackson
Chen, Xiaoyin
Vaughan, Alex
Zador, Anthony M.
Paninski, Liam
author_sort Chen, Shuonan
collection PubMed
description Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many voxels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities. We apply BarDensr to simulated and real data and find that it achieves state of the art signal recovery, particularly in densely-labeled regions or data with low spatial resolution. Finally, BarDensr is fast and parallelizable. We provide open-source code as well as an implementation for the ‘NeuroCAAS’ cloud platform.
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spelling pubmed-79718812021-03-31 BARcode DEmixing through Non-negative Spatial Regression (BarDensr) Chen, Shuonan Loper, Jackson Chen, Xiaoyin Vaughan, Alex Zador, Anthony M. Paninski, Liam PLoS Comput Biol Research Article Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many voxels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities. We apply BarDensr to simulated and real data and find that it achieves state of the art signal recovery, particularly in densely-labeled regions or data with low spatial resolution. Finally, BarDensr is fast and parallelizable. We provide open-source code as well as an implementation for the ‘NeuroCAAS’ cloud platform. Public Library of Science 2021-03-08 /pmc/articles/PMC7971881/ /pubmed/33684106 http://dx.doi.org/10.1371/journal.pcbi.1008256 Text en © 2021 Chen et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Chen, Shuonan
Loper, Jackson
Chen, Xiaoyin
Vaughan, Alex
Zador, Anthony M.
Paninski, Liam
BARcode DEmixing through Non-negative Spatial Regression (BarDensr)
title BARcode DEmixing through Non-negative Spatial Regression (BarDensr)
title_full BARcode DEmixing through Non-negative Spatial Regression (BarDensr)
title_fullStr BARcode DEmixing through Non-negative Spatial Regression (BarDensr)
title_full_unstemmed BARcode DEmixing through Non-negative Spatial Regression (BarDensr)
title_short BARcode DEmixing through Non-negative Spatial Regression (BarDensr)
title_sort barcode demixing through non-negative spatial regression (bardensr)
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7971881/
https://www.ncbi.nlm.nih.gov/pubmed/33684106
http://dx.doi.org/10.1371/journal.pcbi.1008256
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