Entering edit mode
i have a dataset in which i want to compare the disease vs control (disease/control)
When I am doing the analysis I have Coefficient: -1*control 1*disease. Is this correct to compare disease vs control ?
and the total DE results are
summary(deg1)
-1*control 1*disease
Down 5342
NotSig 58145
Up 5462
Could you please explain what is going on with the logFC?
Coefficient: -1*control 1*parkinson
logFC logCPM LR PValue FDR
ENST00000309758.6 5.596663 5.290097 104.58809 1.503581e-24 1.036704e-19
ENST00000375650.5 2.499489 9.443004 73.07818 1.246143e-17 4.296015e-13
ENST00000375651.7 2.814114 9.399579 71.41025 2.901603e-17 6.668753e-13
ENST00000525876.1 3.499724 2.089409 70.70220 4.154286e-17 7.160846e-13
ENST00000674129.1 -1.615221 1.126 12.388 0.00043209 0.00730928
if it is positive means overexpression in disease or not? and if it negative means underexpression ?
So in case where i have
Coefficient: -1*disease 1*controlthe positive logFC means underexpression and the negative over in disease? so it is the opposite than before ?You can interpret this as if it were simple algebra (which it is).
-1 * disease 1 * controlis identical to control - disease, which shows the directionality. This is not how I would normally fit a contrast, because a negative logFC indicates upregulation in disease, which is not IMO how people would normally think about such things. I usually put the 'least affected' group in the denominator (these are logged coefficients, so log(disease) - log(control) == log(disease/control)), so control is always subtracted from treatment or disease or whatever. In which case the contrast should be1 * disease -1 * control.Yes i completely agree but somehow i used in all my data
-1* disease 1* controlwhich was coming from manual of edger andlrt12 <- glmLRT(fit, contrast=c(-1,1)). Thats why I am confused how to select the contrast in a proper way.My design is :
So the
-1 * disease 1 * controlis the opposite .the
-1*control 1*diseaseor1*disease -1*controlis the same ?Well, you shouldn't be using
glmLRTthese days. You want the quasi-likelihood pipeline.I'll answer your question with a question. If
contrast = c(-1,1)doesn't give you what you want (you want the opposite), how do you think one would change that to get what you do want?I tried the contrast=c(1,-1)) which is the opposite. Maybe is should try the quasi-likelihood ! Thank you !