Hi,
I need some clarification on my results as i have been getting myself very confused!
I have an an microarray experiment where i have controls and cases (dosed with a drug). I have used the following in the design matrix for limma:
design <- model.matrix( ~CLASS) )
This creates a design matrix:
| intercept | CLASS$Drug |
|---|---|
| 1 | 0 |
| 1 | 0 |
| 1 | 1 |
| 1 | 1 |
| 1 | 1 |
The zero's are my control group and the 1s are those samples dosed with the drug. Therefore the control group is my reference group.
From my output from limma for each gene i get a logFC. If i have a positive logFC for a gene does this mean that my gene is upregulated in my Drug group compared with my control group for that gene?
I have been reading lots of posts but i have now gotten very confused so any help will be appreciated!
Thanks for any help,
Danielle
In general, it's useful to "inspect the raw data". For example, take one of your most significant DEGs and make a box plot of expression values (raw counts, TPM, voom-normalized) in both conditions; repeat with some non-significant genes for comparison. Among other things, this will show you if the expression is higher with the drug or without. This is normal part of my own debugging/QA process to make sure I've got the design matrix correct and stuff - if you've done it wrong, your top DEGs won't look very different from non-significant DEGs.