Entering edit mode

17 months ago

joonhong kwon
▴
70

Hi all,

I am analyzing RNA-seq data. And I performed pairwise DEG analysis using Limma.

I want compare Disease group and Control. And I have 6 Disease sample and 6 matched control sample.

So could you check that the my R code is correct??

And Can I use the same model matrix when I use DESeq2?

Thanks in advance,

Joonhong

```
design <- model.matrix(~0 + Disease + Paired)
fit <- lmFit(expr,design)
cont <- makeContrasts(DiseaseMDD-DiseaseCon,levels=design)
fit.cont <- contrasts.fit(fit,cont)
fit.cont <- eBayes(fit.cont)
res <- topTable(fit.cont,number=Inf)
> design
DiseaseCon DiseaseMDD PairedS2 PairedS3 PairedS4 PairedS5 PairedS6
1 1 0 0 0 0 0 0
2 1 0 1 0 0 0 0
3 1 0 0 1 0 0 0
4 1 0 0 0 1 0 0
5 1 0 0 0 0 1 0
6 1 0 0 0 0 0 1
7 0 1 0 0 0 0 0
8 0 1 1 0 0 0 0
9 0 1 0 1 0 0 0
10 0 1 0 0 1 0 0
11 0 1 0 0 0 1 0
12 0 1 0 0 0 0 1
```

Looks fine to me, it is a normal paired design as you can fit it with DESeq2 as well.

Thank you!

In DESeq2 vignette, it is described as follows.

"Yes, you should use a multi-factor design which includes the sample information as a term in the design formula. This will account for differences between the samples while estimating the effect due to the condition. The condition of interest should go at the end of the design formula, e.g. ~ subject + condition."

So should I modify my model matrix like "~ Paried + Disease"?