1School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland.
BMC Bioinformatics. 2011 Oct 21;12:407. doi: 10.1186/1471-2105-12-407.
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.
准确的定量共定位是理解分子空间协调及其在细胞中功能的关键参数。现有的共定位算法要么考虑共现像素的存在，要么考虑感兴趣区域中强度的相关性。根据图像来源和所选算法，确定的共定位系数可能变化很大，且通常不准确。此外，关于共现还是相关性是量化共定位的最佳方法的选择仍存在争议。
我们开发了一种新的算法来定量共定位，该算法改进并解决了现有共定位度量方法的主要缺点。该算法使用每个通道中像素强度的非参数排序，并且两个通道中共定位像素位置的排序差异用于加权系数。这种加权适用于共现像素，从而有效地结合了共现和相关性。使用合成数据集进行的测试表明，该算法对不同强度水平的共现和相关性均敏感。对生物数据集的分析表明，该新算法具有高灵敏度，并且能够检测共定位的细微变化，这一点通过对一种经过良好表征的货物蛋白的研究得到了例证，该货物蛋白通过细胞的分泌途径运输。
该算法为生物图像中有效结合共现和相关性成分提供了一种新方法，从而生成共定位的准确度量。这种强度排序加权的方法还消除了对图像进行手动阈值处理的需要，这通常是共定位量化中的一个误差源。我们设想该工具将促进广泛的生物数据集的定量分析，包括高分辨率共聚焦图像、活细胞延时记录和高通量筛选数据集。
