BARcode DEmixing through Non-negative Spatial Regression (BarDensr).

Chen, Shuonan, Loper, Jackson, Chen, Xiaoyin, Vaughan, Alex, Zador, Anthony M, Paninski, Liam (March 2021) BARcode DEmixing through Non-negative Spatial Regression (BarDensr). PLoS Computational Biology, 17 (3). e1008256. ISSN 1553-734X

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URL: https://www.ncbi.nlm.nih.gov/pubmed/33684106
DOI: 10.1371/journal.pcbi.1008256

Abstract

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.

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > RNA expression
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene expression
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > transcriptomes
CSHL Authors:
Communities: CSHL labs > Zador lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: March 2021
Date Deposited: 03 May 2021 19:32
Last Modified: 03 May 2021 19:32
PMCID: PMC7971881
URI: https://repository.cshl.edu/id/eprint/39974

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