A method for improved clustering and classification of microscopy images using quantitative co-localization coefficients

Hdl Handle:
http://hdl.handle.net/10147/237601
Title:
A method for improved clustering and classification of microscopy images using quantitative co-localization coefficients
Authors:
Singan, Vasanth R; Handzic, Kenan; Curran, Kathleen M; Simpson, Jeremy C
Citation:
BMC Research Notes. 2012 Jun 08;5(1):281
Issue Date:
8-Jun-2012
URI:
http://dx.doi.org/10.1186/1756-0500-5-281; http://hdl.handle.net/10147/237601
Abstract:
AbstractBackgroundThe localization of proteins to specific subcellular structures in eukaryotic cells provides important information with respect to their function. Fluorescence microscopy approaches to determine localization distribution have proved to be an essential tool in the characterization of unknown proteins, and are now particularly pertinent as a result of the wide availability of fluorescently-tagged constructs and antibodies. However, there are currently very few image analysis options able to effectively discriminate proteins with apparently similar distributions in cells, despite this information being important for protein characterization.FindingsWe have developed a novel method for combining two existing image analysis approaches, which results in highly efficient and accurate discrimination of proteins with seemingly similar distributions. We have combined image texture-based analysis with quantitative co-localization coefficients, a method that has traditionally only been used to study the spatial overlap between two populations of molecules. Here we describe and present a novel application for quantitative co-localization, as applied to the study of Rab family small GTP binding proteins localizing to the endomembrane system of cultured cells.ConclusionsWe show how quantitative co-localization can be used alongside texture feature analysis, resulting in improved clustering of microscopy images. The use of co-localization as an additional clustering parameter is non-biased and highly applicable to high-throughput image data sets.
Item Type:
Journal Article

Full metadata record

DC FieldValue Language
dc.contributor.authorSingan, Vasanth R-
dc.contributor.authorHandzic, Kenan-
dc.contributor.authorCurran, Kathleen M-
dc.contributor.authorSimpson, Jeremy C-
dc.date.accessioned2012-08-07T14:33:31Z-
dc.date.available2012-08-07T14:33:31Z-
dc.date.issued2012-06-08-
dc.identifier.citationBMC Research Notes. 2012 Jun 08;5(1):281-
dc.identifier.urihttp://dx.doi.org/10.1186/1756-0500-5-281-
dc.identifier.urihttp://hdl.handle.net/10147/237601-
dc.description.abstractAbstractBackgroundThe localization of proteins to specific subcellular structures in eukaryotic cells provides important information with respect to their function. Fluorescence microscopy approaches to determine localization distribution have proved to be an essential tool in the characterization of unknown proteins, and are now particularly pertinent as a result of the wide availability of fluorescently-tagged constructs and antibodies. However, there are currently very few image analysis options able to effectively discriminate proteins with apparently similar distributions in cells, despite this information being important for protein characterization.FindingsWe have developed a novel method for combining two existing image analysis approaches, which results in highly efficient and accurate discrimination of proteins with seemingly similar distributions. We have combined image texture-based analysis with quantitative co-localization coefficients, a method that has traditionally only been used to study the spatial overlap between two populations of molecules. Here we describe and present a novel application for quantitative co-localization, as applied to the study of Rab family small GTP binding proteins localizing to the endomembrane system of cultured cells.ConclusionsWe show how quantitative co-localization can be used alongside texture feature analysis, resulting in improved clustering of microscopy images. The use of co-localization as an additional clustering parameter is non-biased and highly applicable to high-throughput image data sets.-
dc.titleA method for improved clustering and classification of microscopy images using quantitative co-localization coefficients-
dc.typeJournal Article-
dc.language.rfc3066en-
dc.rights.holderVasanth R Singan et al.; licensee BioMed Central Ltd.-
dc.description.statusPeer Reviewed-
dc.date.updated2012-07-26T07:16:02Z-
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