Cargando…

Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images

BACKGROUND: Accurate quantitative co-localization is a key parameter in the context of understanding the spatial co-ordination of molecules and therefore their function in cells. Existing co-localization algorithms consider either the presence of co-occurring pixels or correlations of intensity in r...

Descripción completa

Detalles Bibliográficos
Autores principales: Singan, Vasanth R, Jones, Thouis R, Curran, Kathleen M, Simpson, Jeremy C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3207948/
https://www.ncbi.nlm.nih.gov/pubmed/22017789
http://dx.doi.org/10.1186/1471-2105-12-407
_version_ 1782215583790530560
author Singan, Vasanth R
Jones, Thouis R
Curran, Kathleen M
Simpson, Jeremy C
author_facet Singan, Vasanth R
Jones, Thouis R
Curran, Kathleen M
Simpson, Jeremy C
author_sort Singan, Vasanth R
collection PubMed
description BACKGROUND: Accurate quantitative co-localization is a key parameter in the context of understanding the spatial co-ordination of molecules and therefore their function in cells. Existing co-localization algorithms consider either the presence of co-occurring pixels or correlations of intensity in regions of interest. Depending on the image source, and the algorithm selected, the co-localization coefficients determined can be highly variable, and often inaccurate. Furthermore, this choice of whether co-occurrence or correlation is the best approach for quantifying co-localization remains controversial. RESULTS: We have developed a novel algorithm to quantify co-localization that improves on and addresses the major shortcomings of existing co-localization measures. This algorithm uses a non-parametric ranking of pixel intensities in each channel, and the difference in ranks of co-localizing pixel positions in the two channels is used to weight the coefficient. This weighting is applied to co-occurring pixels thereby efficiently combining both co-occurrence and correlation. Tests with synthetic data sets show that the algorithm is sensitive to both co-occurrence and correlation at varying levels of intensity. Analysis of biological data sets demonstrate that this new algorithm offers high sensitivity, and that it is capable of detecting subtle changes in co-localization, exemplified by studies on a well characterized cargo protein that moves through the secretory pathway of cells. CONCLUSIONS: This algorithm provides a novel way to efficiently combine co-occurrence and correlation components in biological images, thereby generating an accurate measure of co-localization. This approach of rank weighting of intensities also eliminates the need for manual thresholding of the image, which is often a cause of error in co-localization quantification. We envisage that this tool will facilitate the quantitative analysis of a wide range of biological data sets, including high resolution confocal images, live cell time-lapse recordings, and high-throughput screening data sets.
format Online
Article
Text
id pubmed-3207948
institution National Center for Biotechnology Information
language English
publishDate 2011
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-32079482011-11-04 Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images Singan, Vasanth R Jones, Thouis R Curran, Kathleen M Simpson, Jeremy C BMC Bioinformatics Methodology Article BACKGROUND: Accurate quantitative co-localization is a key parameter in the context of understanding the spatial co-ordination of molecules and therefore their function in cells. Existing co-localization algorithms consider either the presence of co-occurring pixels or correlations of intensity in regions of interest. Depending on the image source, and the algorithm selected, the co-localization coefficients determined can be highly variable, and often inaccurate. Furthermore, this choice of whether co-occurrence or correlation is the best approach for quantifying co-localization remains controversial. RESULTS: We have developed a novel algorithm to quantify co-localization that improves on and addresses the major shortcomings of existing co-localization measures. This algorithm uses a non-parametric ranking of pixel intensities in each channel, and the difference in ranks of co-localizing pixel positions in the two channels is used to weight the coefficient. This weighting is applied to co-occurring pixels thereby efficiently combining both co-occurrence and correlation. Tests with synthetic data sets show that the algorithm is sensitive to both co-occurrence and correlation at varying levels of intensity. Analysis of biological data sets demonstrate that this new algorithm offers high sensitivity, and that it is capable of detecting subtle changes in co-localization, exemplified by studies on a well characterized cargo protein that moves through the secretory pathway of cells. CONCLUSIONS: This algorithm provides a novel way to efficiently combine co-occurrence and correlation components in biological images, thereby generating an accurate measure of co-localization. This approach of rank weighting of intensities also eliminates the need for manual thresholding of the image, which is often a cause of error in co-localization quantification. We envisage that this tool will facilitate the quantitative analysis of a wide range of biological data sets, including high resolution confocal images, live cell time-lapse recordings, and high-throughput screening data sets. BioMed Central 2011-10-21 /pmc/articles/PMC3207948/ /pubmed/22017789 http://dx.doi.org/10.1186/1471-2105-12-407 Text en Copyright ©2011 Singan et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Singan, Vasanth R
Jones, Thouis R
Curran, Kathleen M
Simpson, Jeremy C
Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images
title Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images
title_full Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images
title_fullStr Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images
title_full_unstemmed Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images
title_short Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images
title_sort dual channel rank-based intensity weighting for quantitative co-localization of microscopy images
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3207948/
https://www.ncbi.nlm.nih.gov/pubmed/22017789
http://dx.doi.org/10.1186/1471-2105-12-407
work_keys_str_mv AT singanvasanthr dualchannelrankbasedintensityweightingforquantitativecolocalizationofmicroscopyimages
AT jonesthouisr dualchannelrankbasedintensityweightingforquantitativecolocalizationofmicroscopyimages
AT currankathleenm dualchannelrankbasedintensityweightingforquantitativecolocalizationofmicroscopyimages
AT simpsonjeremyc dualchannelrankbasedintensityweightingforquantitativecolocalizationofmicroscopyimages