Hello All,

I have a quick question that I need some advise about. I am running differential expression on a set of proteins and I am using Limma package in R.

I have three replicates of positive control, three rep. of treatments and 3 rep. of controls. I would like to compare the expression level differences of the treatment vs the control data.

Though the expression level of the significant proteins are different (smaller) when I use this design matrix:

```
design <- cbind(Intercept=1,Group=c(-1,-1,-1,1,1,1))
Compare to this matrix: design <- cbind(Intercept=1,Group=c(0,0,0,1,1,1))
```

The number and p-value of the significant proteins remain similar in magnitude

Any hint in this regard would be appreciated.

SN

Thank you for your explanation. I am working on the cell lines and I am investigating the effect of the treatment on the expression level, so it was important to me to know which design is more accurate.

I realized that the LogFC is different, using different design matrices, and if I consider logFC > 1 as my interesting proteins/genes, then it is iportant to me not to wrongly exclude some genes/proteins.

When is it recommended to use the

`design <- cbind(Intercept=1,Group=c(-1,-1,-1,1,1,1))`

?Thank you for your time on explaining this.

SN

It is not recommended. No such code appears anywhere in the limma documentation (except for two-color microarrays with dye-swaps, which is a very specialized application).

Again, that is not recommended. Using a fold-change cutoff interferes with limma doing its job and is not recommended anywhere in the limma documentation. Stick to FDR cutoffs or use treat().

Got it, thank you for your response.

SN