Hi,
I have 5 samples (1 control +2 patients + 2 parents) for which I plotted an average methylation profile using violin plot. I would like to know which statistical test I need to apply to compare control vs patients 1/ patient2? An example plot is like similar to this google image:
Thanks
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Ankit
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To me a violin plot is mostly a fancy representation of a box-plot. And a box plot is (for gaussian distributions) a graphical representation of a t-test.
You can't apply a t-test blindly. You need to make sure that the assumptions of the test are met so that it is valid.
So my data has ~750,000 methylation array probes.
I first plotted the boxplot and then violin.
I have five individuals with varying methylation profile for each probes. So which test do you think is applicable to this kind of data, if I want to compare control with any of the 2 patient I have?
Does my data full-fill the assumptions as mentioned here? or how will I test the assumptions are made?
Does my data full-fill the assumptions as mentioned here?
You don't show the plots you produced. What Jean-Karime wrote above still holds true.
That can't be decided without knowing the data.
We have methylation EPIC array data 850K for five individals as I previously mentioned.
After normalization and filtering ~750K probes left. I would like to compare global or overall methylation profle for control vs patient. I would like to see if there is a significant difference between the two considering all the probes.
Please suggest me how else I can better explain the data?
Are there any plots or values which I need to show here?
Are there any plots or values which I need to show here?
in case you want someone to give qualified answers, that might help. How about the violin plots and box plots you mentioned? With box plots histograms are helpful, too (though included in the violin plots).
![Here is the image attached]
HI Ankit, i'm not a specialist on methylation data (not even a bit).
Do you really think a simple test is what you want? I mean, you obviously have a difference between patient and control and not so much if any between parents and control. Would it be more interesting to figure where there's differences in methylation? What have others done with similar data? Here's a nature article with a linear regression analysis, something that feels far more appropriate.
But again, not my area of expertise...
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The test to apply isn't determined by the type of plot but by the nature of the data and assumptions that can be made about its distribution.
Yes you are right. So as #Carambakaracho suggested, I will apply t-test?
That can't be decided without knowing the data.