Hi Dominic, I have question about image preprocessing. To perform Pearson’s test is ir required to do some kind of background subtraction of the image before ROI selection to remove any cellular auto fluorescence and noise? I am using Coloc2 plugin for pearson's test, do I have to convert my images to 8 bit or using 12 or 16 bit image is okay? For Manders, M1 and M2 I rely on JaCop. I use 12 bit image, do a background subtraction from the entire image, draw an ROI and make copy of ROI and then run Jacop on different cells/ROI from the same image. Is this a correct way? Two questions here. 1. How to access the best background subtraction method in imagej? a) Process- math-subtract(the mean value from area outside cell) would remove only off cell background, right? what about on cell background? b) If I use Rolling ball background subtraction then how to determine the correct radius for 12 bit images? And is sliding paraboloid better? 2. JaCop does auto thresholding which can be manually changed, shall I rely on Jacop's auto threshold or manually change the percentage based on one of imageJ algorithm(like otsu) and then use the same algorithm for all images? I would really appreciate your inputs. Thanks!
Hi Dominic, I have a question about the statistics of Pearson (the image you showed with the time effect of colocalization), I thought it was not possible to proceed with regular data analysis, once Pearson is a coefficient. Can we proceed normally with analysis? does it need any different treatment? Another question: does the color intensity interfere with the correlation? (For example, the red is more bright than the green. Thanks!! Amazing video!
Each analysed cell, or image, depending on which you analysed can be treated a single data-point in an overall analysis. For example you could do statistics comparing the Pearson's coefficient generated from say 300 cells in one condition and compare the mean to that generated from an experimental treatment condition. Each cell in this case gives you a coefficient value and your statistics are looking at the average of the population you have imaged in both conditions. This is exclusive to the inherent statistical meaning of Pearson's coefficient, which can be utilised in theory, but seldom is when applied to biological imaging.
The CHB is the intersection of CH1 and CH2. This can be achieved using the 'AND' operator in Fiji/ImageJ. Process-> Image Calculator and then select 'AND' from the drop down list, as well as your two images in the drop-down menus.
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Hi, I think you had a mistake for M1, becasue CHB/CH1 = 0.5918,... not 0.519. So M1 has a higher Mander coefficient than M2.
I have the same feeling
Hi Both, yes Chloe you are correct. Thank you for working it out with you calculator. It is just a typo. It should say: 0.592
Hi Dominic, I have question about image preprocessing. To perform Pearson’s test is ir required to do some kind of background subtraction of the image before ROI selection to remove any cellular auto fluorescence and noise? I am using Coloc2 plugin for pearson's test, do I have to convert my images to 8 bit or using 12 or 16 bit image is okay?
For Manders, M1 and M2 I rely on JaCop. I use 12 bit image, do a background subtraction from the entire image, draw an ROI and make copy of ROI and then run Jacop on different cells/ROI from the same image. Is this a correct way? Two questions here.
1. How to access the best background subtraction method in imagej? a) Process- math-subtract(the mean value from area outside cell) would remove only off cell background, right? what about on cell background? b) If I use Rolling ball background subtraction then how to determine the correct radius for 12 bit images? And is sliding paraboloid better?
2. JaCop does auto thresholding which can be manually changed, shall I rely on Jacop's auto threshold or manually change the percentage based on one of imageJ algorithm(like otsu) and then use the same algorithm for all images?
I would really appreciate your inputs. Thanks!
this is really useful. I would appreciate more videos like this. really really awesome! thanks
Hi Dominic, I have a question about the statistics of Pearson (the image you showed with the time effect of colocalization), I thought it was not possible to proceed with regular data analysis, once Pearson is a coefficient. Can we proceed normally with analysis? does it need any different treatment?
Another question: does the color intensity interfere with the correlation? (For example, the red is more bright than the green. Thanks!!
Amazing video!
Each analysed cell, or image, depending on which you analysed can be treated a single data-point in an overall analysis. For example you could do statistics comparing the Pearson's coefficient generated from say 300 cells in one condition and compare the mean to that generated from an experimental treatment condition. Each cell in this case gives you a coefficient value and your statistics are looking at the average of the population you have imaged in both conditions. This is exclusive to the inherent statistical meaning of Pearson's coefficient, which can be utilised in theory, but seldom is when applied to biological imaging.
Hi Dominic, i wanted to ask you how you merged both tresholds? because i can't find the way to do you'r CHB
The CHB is the intersection of CH1 and CH2. This can be achieved using the 'AND' operator in Fiji/ImageJ. Process-> Image Calculator and then select 'AND' from the drop down list, as well as your two images in the drop-down menus.