I am trying to learn how to analyse normalised DIA-MS data and I am struggling with it ://
The original dataset I got is (6 conditions (2 samples each)) with 3 technical replicates (total: 36 sample columns x 1128 proteins in rows). I removed the proteins with more than 80% of NA, and impute with kNN the rest of missing values.
Then I collapse the replicates doing the average of the 3 replicates (is this correct?)
So now, I have a dataframe with 12 columns x 1072 rows.
The question is... I am not sure how to perform the differential analysis from here (assuming I did well the previous steps lol)
Could someone help me with the tools or code to compare all the 6 conditions between them?
I read something about Limma package, but not sure if it can be used in this DIA-ms data and neither sure of how to use limma for it (I already check the vignette with no succeed)
UPDATE: I tried to use Limma as follows:
fit1 <- lmFit(norm.data_collapsed, design) fit2 <- contrasts.fit(fit1, contrasts = contrast) fit3 <- eBayes(fit2) limma.res.01 <- topTable(fit3, coef = comb.contrast, n= Inf)
This returns me a non-sense values for logFC or significance:
Any help would be very welcome,