Hi, everyone.

In my experiment, I have 5 objects (A,B,C,D,E) with drug treatment (Pre and Post). Therefore total samples include A-pre, A-post, B-pre, B-post, C-pre, C-post, D-pre, D-post, E-pre, E-post

Fist aim is comparing pre-treatment and post-treament, it should be 5 samples vs 5 samples

Second aim is comparing pre-treatment and post-treament in each indidual object, it should be A-pre vs A-post; B-pre vs B-post, C-pre vs C-post, D-pre vs D-post, E-pre vs E-post

I am confused about how to make design matrix and contrast matrix.

Design <- model.matrix(~0+treat_status+object) ??

Contrast <- make.contrast() ???

Thanks

Bests,

Shawn
 

To get the overall pre vs post, taking account of the pairing, you'll need ~treat_status + object and the second coefficient will give you the log fold-change estimate of the pre vs post (it'll be relative to 'post' if you've kept the default alphabetic factor order). So topTable(..., coef=2), or topTable(..., coef="treat_statusPost") 

If you don't include an intercept (with your inclusion of '0' on the RHS of the model formula), then you'll get an esimate for both pre and post, so you'd need to have a make.contrast that subtracted these two; with an intercept, everythings relative to the baseline condition, so that subtraction is already done for you.

To get individual objects' changes, you'll need  ~ object + object:treat_status - 1 and you'll get the first five coefficients being the per-object baseline, and the next five being the per-object post-to-pre log fold change.  So topTable(..., coef=6)  or topTable(..., coef="objectA:treat_statusPost")  etc

Thanks a lot for your replying. Let me try it.

Bests,

Shawn