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A Bayesian cluster analysis method for single-molecule localization microscopy data

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Title: A Bayesian cluster analysis method for single-molecule localization microscopy data
Author(s): Griffié, J
Shannon, M
Bromley, CL
Boelen, L
Burn, GL
Williamson, DJ
Heard, NA
Cope, AP
Owen, DM
Rubin-Delanchy, P
Item Type: Journal Article
Abstract: Cell function is regulated by the spatiotemporal organization of the signaling machinery, and a key facet of this is molecular clustering. Here, we present a protocol for the analysis of clustering in data generated by 2D single-molecule localization microscopy (SMLM)-for example, photoactivated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM). Three features of such data can cause standard cluster analysis approaches to be ineffective: (i) the data take the form of a list of points rather than a pixel array; (ii) there is a non-negligible unclustered background density of points that must be accounted for; and (iii) each localization has an associated uncertainty in regard to its position. These issues are overcome using a Bayesian, model-based approach. Many possible cluster configurations are proposed and scored against a generative model, which assumes Gaussian clusters overlaid on a completely spatially random (CSR) background, before every point is scrambled by its localization precision. We present the process of generating simulated and experimental data that are suitable to our algorithm, the analysis itself, and the extraction and interpretation of key cluster descriptors such as the number of clusters, cluster radii and the number of localizations per cluster. Variations in these descriptors can be interpreted as arising from changes in the organization of the cellular nanoarchitecture. The protocol requires no specific programming ability, and the processing time for one data set, typically containing 30 regions of interest, is ∼18 h; user input takes ∼1 h.
Publication Date: 17-Nov-2016
Date of Acceptance: 1-Nov-2016
URI: http://hdl.handle.net/10044/1/43069
DOI: http://dx.doi.org/10.1038/nprot.2016.149
ISSN: 1754-2189
Publisher: Nature Publishing Group
Start Page: 2499
End Page: 2514
Journal / Book Title: Nature Protocols
Volume: 11
Issue: 12
Copyright Statement: © 2016 Nature Publishing Group
Keywords: Bioinformatics
06 Biological Sciences
11 Medical And Health Sciences
03 Chemical Sciences
Publication Status: Published
Conference Place: England
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences



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