Dear all Hi

I worked on RNA-seq and microarray data before but now I am currently working on TMT labeled proteome data for the first time. My data achieved based on 5-time points (in days 0,1,3,6,12) and at any time point, I have data from two TMT 10-plex (two batches). So in any time point, for any TMT we have 2 replicates. As you know the range of TMT data is large and based on the last similar studies, I calculated log2 of data firstly. Then I normalized data by "scale" function and then removed the batch effect between two TMT runs by **Combat** (or *removeBatchEffect* from limma). After these steps, my data became ready to analyze.
My questions are:

1- Am I right to use the limma package and "ebayes" function to find DEPs (differencial expressed proteins) between any specific time point to other time points?

2- If the answer of question 1 is yes, how can I write the "makeContrasts" function?

3- For Day3, when I put **colnames=D3** for columns related to **Day3** and put **colnames=other** for columns related to other time points and:

```
cont.matrix <- makeContrasts("D3-other",levels=design)
```

==> No DEPs can find.

when I put **colnames=D3** for columns related to Day3, **colnames=before** for columns related to **Day0** and **Day1** and **colnames=after** for columns related to **Day6** and **Day12** :

```
cont.matrix <- makeContrasts("D3-befor","D3-after",levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2, 0.01)
tT=topTable(fit2, adjust="BH", number=Inf)
if (! is.null(ann)) tT <- cbind(tT, ann[as.numeric(rownames(tT)),,drop=F])
colnames(tT)[1:2] <- c("before","after")
tT$logFC <- rowMeans(cbind(tT$before, tT$after), na.rm=TRUE)
DEPs <- tT[tT$adj.P.Val<0.05,]
DEPs$ABS_lofFC <- abs(DEPs$logFC)
DEPs<- DEPs[DEPs$ABS_lofFC >1,]
```

==> I could find 646 DEPs.

4- Please note that at least 400 DEPs is obtained when the indipendent t-test is performed between the given time point data and the other time points data.

Gordon Smyth and other colleagues whats your idea? how can I design the contrast to find true DEPs?

Best regards

Samaneh

This article proposes a web interface for TMT normalization and gives references to many methods.

proteiNorm − A User-Friendly Tool for Normalization and Analysis of TMT and Label-Free Protein Quantification

The authors cite limma and other packages. I am not convinced by the DAtest package as it relies on permutation of samples which are not numerous in your design.