Entering edit mode
4.4 years ago
jamieoyang
•
0
Hi, I have multiple rna-seq samples with patients who underwent shock vs controls (Variable of interest = VAR). A sample of how the data looks like is this:
- Patient Gender Age VAR
- 1 F 4 Shock
- 2 M 2 Control
- 3 M 1 Shock
- 4 F 6 Shock
- 5 F 3 Shock
- 6 F 2 Control
My current edgeR design is just:
design <- model.matrix(~VAR)
I wanted to remove/control for the effect of Gender and Age on my results. How would I write the design variable to do this? I am a beginner at this, and I have read the EdgeR manual but am still slightly confused!
I would be so grateful for any help!
-Jamie
Before correcting for age and gender, a good idea would be to do a PCA plot and color your sample by age, gender and VAR if you see some variable that may have an impact on the sample grouping.
That is a good idea, I will definitely do that, thank you!
After that though, do you have any advice on how to write the design model?
Do you expect interactions between sex or age and VAR? Meaning do you want to subtract the effect of age or do you think age would change the response to shock?
The question is if also if it is worth bothering as n=2 for male and n=4 for female in human patients is a very small sample size given all the potential confounding effects in humans.
Hello, thank you for your reply! The n is much larger than the 6 people I have up there, that was just provided as a sample for the variables I have for my patients.
I would like to subtract the effect of age!