Merge pull request #228 from lucasSaavedra123/patch-1

Typo Fixed in Colocalization Analysis page

Author: ctrueden

_pages/imaging/colocalization-analysis.md

@@ -28,7 +28,7 @@ Most people might think that the image contains 4 distinct colours: 2 sets of th
 
 So... now, how do you feel about determining colocalization by looking for yellow blobs? Doesn't make much sense does it? We notice that the shades and hues of colours look different according to what other colours they are next to! So, you need to measure something from the pixel values, not simply subjectively "look at" a red/green colour merge image.
 
-An even better reason to always quantitatively evaluate colcoalization is that this actually tells you what you are looking for - the correlation (or not) between 2 channels of pixels over space.
+An even better reason to always quantitatively evaluate colocalization is that this actually tells you what you are looking for - the correlation (or not) between 2 channels of pixels over space.
 
 ## Methods of colocalization analysis
 
@@ -46,7 +46,7 @@ Here are just two of many colocalization coefficients to express the intensity c
 1.  **Pearson's correlation coefficient.** It is not sensitive to differences in mean signal intensities or range, or a zero offset between the two components. The result is +1 for perfect correlation, 0 for no correlation, and -1 for perfect anti-correlation. Noise makes the value closer to 0 than it should be.
 2.  **Manders split coefficients.** Proportional to the amount of fluorescence of the colocalizing pixels or voxels in each colour channel. You can get more details in [Manders et al.](/media/manders.pdf) Values range from 0 to 1, expressing the fraction of intensity in a channel that is located in pixels where there is above zero (or threshold) intensity in the other colour channel.
 
-These coefficients measure the amount or degree of colocalization, or rather correlation and co-occurrence respectively (but should not be expressed as % values, because that is not how they are defined). But if there is nothing to compare them to, what do they mean? A statistical significance test was derived by Costses to evaluate the probability that the measured value of Pearson's correlation, r between the two colour channels is significantly greater than values of r that would be calculated if there was only random overlap of the same information. This test is performed by randomly scrambling the blocks of pixels (instead of individual pixels, because each pixel's intensity is correlated with its neighboring pixels) in one image, and then measuring the correlation of this image with the other (unscrambled) image. You can get more details in [Costes et al.](/media/costes-etalcoloc.pdf) The result of this tests tell us if the Pearsons r and split Manders' coefficients we measure are better than pure chance or not.
+These coefficients measure the amount or degree of colocalization, or rather correlation and co-occurrence respectively (but should not be expressed as % values, because that is not how they are defined). But if there is nothing to compare them to, what do they mean? A statistical significance test was derived by Costses to evaluate the probability that the measured value of Pearson's correlation r between the two colour channels is significantly greater than values of r that would be calculated if there was only random overlap of the same information. This test is performed by randomly scrambling the blocks of pixels (instead of individual pixels, because each pixel's intensity is correlated with its neighboring pixels) in one image, and then measuring the correlation of this image with the other (unscrambled) image. You can get more details in [Costes et al.](/media/costes-etalcoloc.pdf) The result of this tests tell us if the Pearsons r and split Manders' coefficients we measure are better than pure chance or not.
 
 The example below (Thanks Tony Collins for this nice figure), generally demonstrates the type of results generated by one to one pixel matching analyses.