We are analyzing https://link.springer.com/article/10.1186/1471-2105-12-407.

Title:
Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images | BMC Bioinformatics
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.
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Keywords {🔍}

colocalization, pixels, correlation, images, intensity, image, channel, rwc, figure, pixel, channels, cells, algorithm, coefficients, intensities, data, coefficient, cooccurrence, manders, article, analysis, noise, pubmed, antibodies, levels, values, threshold, set, weighting, thresholding, number, cooccurring, synthetic, localization, objects, google, scholar, varying, quantitative, approach, sets, costes, tsg, algorithms, cell, response, presence, protein, time, original,

Topics {✒️}

𝐵 𝑖 𝐶 � open access article dual-colour confocal images article download pdf large-scale cdna sequencing bmc bioinformatics 12 mouse anti-p230 antibodies rank-based weighting addresses sheep anti-tgn46 antibodies hsp60-tgn46 biological data primary anti-hsp60 antibodies irish research council privacy choices/manage cookies fluorescently-labeled secondary antibodies authors’ original file extensive transcriptional products plasmids encoding cfp-ts045g howson rw secretory cargo cfp-ts045g biological data sets synthetic data sets high-throughput approaches biomed central temperature-sensitive viral glycoprotein cis-golgi marker gm130 1 2 𝑛 author information authors steady-state localization anti-tgn46 antibodies competing biological processes full size image primary anti-hsp60 imaging-based technologies fact evenly decorate endosomal/lysosomal system image processing toolbox olympus fv1000 system o'shea ek graduate phd scholarship departement du transfert mitochondrial chaperone hsp60 full access release showing cfp-ts045g genome-sequencing projects membrane-bounded compartments discriminates pixel positions conditions privacy policy anti-p230 antibodies post-golgi sorting

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         description: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. 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. 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.
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