Let's say you have many images taken the same way from two different samples: One Control group and One test group.
What will/should you do to analyse them?
The first thing I would do will be to open a random pair of image (one from the control and one from the test) and have a look at them...
Control Test
Then I would normalize the brightness and contrast for each channel and across images to make the display settings the same for both images.
Then I would look at each channel individually and use a LUT Fire to have better appreciate the intensities.
Finally I will use the syncrhonize Windows features to navigate the two images at the same time
ImageJ>Windows>Synchronize Windows
ou dans une macro
run("Synchronize Windows");
Control Ch1 Test Ch1
Control Ch2 Test Ch2
To my eyes the Ch1 and Ch2 are slighly brighter in the Test conditions.
Here we can't compare Ch1 to Ch2 because the range of display are not the same. It seems than Ch2 is much weaker than Ch1 but it is actually not accurate. The best way to sort this is to add a calibration bar to each channel ImageJ>Tools>Calibration Bar. The calibration bar is non-destructive as it will be added to the overlay. To "print" it on the image you can flatten it on the image ImageJ>Image>Overlay>Flatten.
Control Ch1 Control Ch2
If you apply the same display to Ch1 and Ch2 then you can see that Ch1 overall more intense while Ch2 has few very strong spots.
Control Ch1 Control Ch2 with same display than Ch1
Looking more closely
In Ch1 we can see that there is some low level intensity and a high level circular foci whereas in Ch2 there is a bean shaped structure. In the example below the Foci seems stronger in the control than the Test condition.
Control Ch1 Test Ch1
Control Ch2 Test Ch2
But we need obviously to do some quantification to confirm or infirm these first observations.
The first way to address it would be in a bulk fashion: By measuring the mean intensity for example for all the images.
If all works fine you should have a CSV file you can open with your favourite spreadsheet applications. This table should give one line per image and all available measurements for the whole image and for each channel of the image. Of course some measurements will be all the same because the images were taken in the same way.
What to do with the file? Explore the data and see if there is any relelvant infroamtion.
My view would be to use a short script in R to plot all the data and to some basic statistics
This should give you a pdf file with one plot per page. You can scroll it and look at the data. p-values from t-test are indicated on the graphs. As you can see below the mean intensity in both Ch1 and Ch2 are higher in the test than the control. What does it mean?
It means than the average pixel intensity is higher in the test conditions than the control condition.
Other values that are significantly different:
- Mean Intensity Ch1 and Ch2 (Control<Test)
- Maximum Intensity of Ch1 (Control>Test) Brightest value in the image
- Integrated Intensity of Ch1 and Ch2(Control<Test) It is equal to the Mean x Area
- Median Ch1 and Ch2 (Control<Test)
- Skew of Ch1 (Control>Test): The third order moment about the mean. Relate to the distribution of intensities. If=0 then intensities are symmetrically distributed around the mean. if<0 then distribution is asymmetric to the Left of the mean (lower intensities), if>0 then it is to the right (higher intensities).
- Raw Integrated Intensities of Ch1 and Ch2 (Control<Test) Sum of all pixel intensities
Now we start to have some results and statistically relevant information about the data. The test condition have a higher mean intensity (integrated intensity, median and raw integrated intensities are all showing the same result) for both Ch1 and Ch2. This is surprising because I had the opposite impression while looking at the image with normalized intensities and LUT fire applied (see above). Another surprising result is the fact that Control images have a higher maximum intensity than Test images but only for Ch1. This is clearly seen in the picture above.
One thing that can explain these results is that the number of cells can be different in the control vs the test images. If there are more cells in one condition then there are more pixels stained (and less background) and the mean intensity would be higher not because the signal itself is higher in each cell but because there are more cell...
To solve this we need to count the number of cells per image. This can be done manually or using segmentation based of intensity.
Looking at the image it seems that Ch1 is a good candidate to segment each cell.