Adler Jeremy, Parmryd Ingela
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
Department of Medical Cell Biology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
PLoS One. 2014 Nov 6;9(11):e111983. doi: 10.1371/journal.pone.0111983. eCollection 2014.
A critical step in the analysis of images is identifying the area of interest e.g. nuclei. When the nuclei are brighter than the remainder of the image an intensity can be chosen to identify the nuclei. Intensity thresholding is complicated by variations in the intensity of individual nuclei and their intensity relative to their surroundings. To compensate thresholds can be based on local rather than global intensities. By testing local thresholding methods we found that the local mean performed poorly while the Phansalkar method and a new method based on identifying the local background were superior. A new colocalization coefficient, the H(coef), highlights a number of controversial issues. (i) Are molecular interactions measurable (ii) whether to include voxels without fluorophores in calculations, and (iii) the meaning of negative correlations. Negative correlations can arise biologically (a) because the two fluorophores are in different places or (b) when high intensities of one fluorophore coincide with low intensities of a second. The cases are distinct and we argue that it is only relevant to measure correlation using pixels that contain both fluorophores and, when the fluorophores are in different places, to just report the lack of co-occurrence and omit these uninformative negative correlation. The H(coef) could report molecular interactions in a homogenous medium. But biology is not homogenous and distributions also reflect physico-chemical properties, targeted delivery and retention. The H(coef) actually measures a mix of correlation and co-occurrence, which makes its interpretation problematic and in the absence of a convincing demonstration we advise caution, favouring separate measurements of correlation and of co-occurrence.
图像分析中的一个关键步骤是识别感兴趣的区域,例如细胞核。当细胞核比图像的其余部分更亮时,可以选择一个强度来识别细胞核。由于单个细胞核的强度及其相对于周围环境的强度存在变化,强度阈值化变得复杂。为了进行补偿,阈值可以基于局部强度而非全局强度。通过测试局部阈值化方法,我们发现局部均值的表现较差,而Phansalkar方法和一种基于识别局部背景的新方法则更优。一种新的共定位系数H(coef)突出了一些有争议的问题。(i)分子相互作用是否可测量;(ii)在计算中是否包括没有荧光团的体素;以及(iii)负相关的含义。负相关可能在生物学上出现:(a)因为两种荧光团位于不同位置;或者(b)当一种荧光团的高强度与另一种荧光团的低强度重合时。这些情况是不同的,我们认为仅使用包含两种荧光团的像素来测量相关性才是相关的,并且当荧光团位于不同位置时,只需报告没有共现情况并省略这些无信息的负相关。H(coef)可以报告均匀介质中的分子相互作用。但生物学并非均匀的,分布还反映了物理化学性质、靶向递送和保留情况。H(coef)实际上测量的是相关性和共现性的混合,这使得其解释存在问题,并且在没有令人信服的证明的情况下,我们建议谨慎行事,倾向于分别测量相关性和共现性。