#Pearson's Coefficient Range between -1 and 1. -1 is a negative correlation and +1 is a positive correlation.
Scatter plots and PCs point to colocalization especially where it is complete (Fig. 5A and B); however, they rarely discriminate differences between partial colocalization or exclusion, especially if images contain noise.
Mander's removes mean fluorescent intensity from Pearson's, which gives a new value between 0 and 1.0, where 1.0 is 100% colocalization.
Same flaws as pearson's essentially.
Appears to be a useful supplement for the overlap coefficient.
Furthermore, it is not possible to distinguish between complete and partial colocalization situations with the M1 and M2 coefficient.
Based on the Pearson's Coefficient. It automatically thresholds by trying to minimize image noise. It also adds a statistical method to exclude chance colocalization - it works by creating a random noise image to compare with. Useful for images with a lot of noise.
Although providing a first statistical estimate of colocaliza- tion, Costes’ approach is also highly dependent on the way in which the test is set up. The authors initially proposed 200 randomization rounds to obtain a significant statistical distribution with more randomization leading to more reliable elimination of false positives.
A clever technique that shifts image A on the x-axis and determining how that effects the color overlap.
However, it has the major drawback that it is only valuable for small and isotropic particles, as it may vary depending on their orientation relative to the selected shift axis. This would present a major problem with our dendrite shapes.
Detects statistical significance in a situation of high image heterogenity. It's based on the assumption that the mean intensity of a channel is usually 0. As a consequence, the product of two equalities should tend to 0.
On colocalization events, the product of the equalities should be positive of because boths means will be of the same sign.
(Slightly messed up description, but the idea is there. This won't work for us anyway.)
#Cytoflurogram A visualization technique for viewing overlap. Essentially the closer the trendline is to diagonal, the better your colocalization.
The main disadvantage of the ICCB tools introduced so far is that no spatial exploration of the colocalized signal is possible. All methods previously described rely on individual pixel coincidence analysis, considering that each pixel is part of the image and not part of a unique structure. Although giving a global estimation of colocalization, their numerical indicators suffer from the composite nature of the images, which is a patchwork of both structures and, even though minimized, background.
If were analyzing the whole image, we'd want partial volumetric colocalization, but because we're only looking at a single dendrite complete colocalization is also probably reasonable.
We aren't doing the whole image because the CAMKII dots are near the optical resolution of the microscope and we aren't getting good coverage across the whole image.
Because the proteins are near the resolution limit of the microscope, I think manual thresholding for segmentation is the most reasonable option. If imaging techniques improved, automatic techniques might be helpful.
Lachmanovich et al. (2003) developed another approach called the overlap approach; objects in the green and red channels colocalize if the centroid of an object of the green channel falls into the area covered by an object of the red channel (Fig. 9G). The degree of colocalization is then given by the percentage of green objects colocalizing with red objects in the area of interest. Counting the number of green centroids matching red object areas and red centroids matching green object areas resulted in two percentages of overlap. These percentages were compared with a random distribution obtained as described before and thereby allowed a statistical evaluation of colocalization. The overlap method enhances the probability of matching structures, as matching a centroid to an object area is more probable than matching two centroids.
It consist in counting the number of centres from objects of image A falling inside segmented objects from image B. Image B should be our dendrite and Image A should have our proteins.
Geometrical center vs Centre of Mass: Geometrical center is a simplified method that can be used when we have relatively uniform shapes (like our circles).
We can filter out more noise by setting a min/max particle size. Work on center's particle coincidence with Image A as proteins and B as dendrite.
Proteins will appear as yellow if overlap/colocalizing (green otherwise). The dendrite will be highlighted in red.
PSD - Image A CaMKII - Image B