419

edits
# Changes

## Coloc 2

,→Effect of noise on Manders coefficients: fix typos and clarify

[[Image:BadOffsetConfusesCostesAutoThreshold.png|300px]]

== Effect of noise on Pearson's and Manders ' coefficients ==

In the case of perfect ~~colocalisation~~colocalization, where the intensities of the 2 channels are always ~~the same~~perfectly correlated: Low red with low green, and high green with high red, the scatterplot would have all the data ~~point ~~points falling on a straight diagonal line, since the green intensity would always ~~bee the same as that of ~~be in proportion to the red! ~~however~~However, that is the ideal case and real biological data is noisy! Noise (be it from the dyes not staining every single molecule, or from statistical photon shot noise from recording the signal from too few photons, or from other electronics noise sources) will cause the pixel intensities to deviate from the perfect /true case, to be lower or higher than they really are on average. This causes scatter in the distribution of the data ~~point ~~points in the scatterplot , perpendicular to the line of regression fit. So you can see the noise by looking at how spread or tight the scatterplot points arefrom the linear regression line. Also, since Manders ' coefficients are measuring correlation, and noise lowers the similarity of two identical signals, noise lowers the Manders ' coefficients to less than they should be for an image with very low noise. The same is true for Pearson's correlation. So for the same object under the microscope, ~~a nosier ~~noisier images will appear to give less ~~colocalisation ~~colocalization than a clean , low noise , image. That means you ~~cant ~~can't compare different images with different signal:noise levels, unless you have some way of estimating the noise and correcting for it.

== Fluorescence emission bleed through looks like perfect colocalization ==