Hello all. Merry Christmas and happy holidays!
I am currently conducting a study and was hoping to get some advice on my model for differential expression. Currently, I have the following data:
Sample || Sex || Age || TissueType || Individual || Diseased
ID1 || Male || 70 || Hemisphere || Individual 1 || Affected
ID2 || Male || 70 || Cortex || Individual 1 || Affected
ID3 || Female || 80 || Hemisphere || Individual 2 || Unaffected
ID4 || Female || 80 || Cortex || Individual 2 || Unaffected
ID5 || Male || 100 || Hemisphere || Individual 3 || Affected
.....
Currently, I have the following model for my sleuth LRT test because I am interested in whether across these two different regions of the brain, are there differentially expressed genes in cases VS controls. I was reading about nested models and was wondering if the following model makes sense:
Full Model: ~ Diseased + Sex + Age + Sex:Age + Sex:Diseased + Age:Diseased + Diseased*TissueType + Diseased*Individual
Reduced Model: Sex + Age + Sex:Age + Sex:Diseased + Age:Diseased + Diseased*TissueType + Diseased*Individual
I tested this and found 6000 genes with a p-value of 0, and realized that there is something wrong haha. I'm also not sure whether to include additively + TissueType and + Individual. I would really appreciate any advice. Thank you so much!
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gradstudentNew •
1 answer
That certainly sounds like an over-fit model.
Perhaps use PCA and clustering to determine what has the greatest overall effect, and then see what you can identify if you only factor out 1 or 2 other variables?
Or, even with 1 or 2 total variables, sometimes having a fold-change filter in addition to a p-value / FDR filter can help narrow down a few hundred up- and down-regulated genes (each).
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[construct a model.matrix ]
written by kyj222637 •
