Dear Sir or Madam,
I am interested in understanding the meaning of "multiple coef" in topTable(), limma package.
For instance, I understand "topTable(fit,coef=1)", but what does "topTable(fit,coef=1:5) mean"?
Thanks in advance.
Regards,
Jianhai
Dear Sir or Madam,
I am interested in understanding the meaning of "multiple coef" in topTable(), limma package.
For instance, I understand "topTable(fit,coef=1)", but what does "topTable(fit,coef=1:5) mean"?
Thanks in advance.
Regards,
Jianhai
Dear Zhang,
when you define multiple coefficients (or no coefficients set), you actually perform an ANOVA-like comparison (similarly if you instead use topTableF), where your null hypothesis would be that there are no DE between any of the conditions/defined coefficients. Thus, in the first case when you define a specific coefficient, you test and set your null hypothesis regarding only this specific contrast. For a more detailed example, you can check the following post:
Model matrix for anova in limma
Best,
Efstathios
Dear Efstathios,
Thanks for your explanation very much. I have a similar question in edgeR.
For example, I have the following design matrix, and I want to compare each patient to the other 2 patients: 8vs33, 8vs51, 33vs51.
Can you tell me how to set the coeff in "lrt <- glmLRT(fit, coef=)", "topTags(lrt)" in a simple way? So that I don't need to set the pairwise coef/contract one by one. Thanks in advance.
In my research I have 70 samples and need to compare each to the rest 69 samples, here I just use 3 samples as a toy example for simplicity purpose.
I know I can set the pairwise contract in such a way: contrast=c(0,0, ,,,,,,,,,-1,1,0,0,0,0,,,,,,0,000), but considering so many samples, it is not convenient to set the pairwise contracts one by one. So is there a one-step way to compare all pairs simultaneously?
Design matrix:
Patient8 Patient33 Patient51
8N 1 0 0
8T 1 0 0
33N 0 1 0
33T 0 1 0
51N 0 0 1
51T 0 0 1
Regards,
Jianhai
Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
For the benefit of future readers, this follow-up question was re-posted and answered here: edgeR multiple coeff in "lrt <- glmLRT(fit, coef=)"