**20**wrote:

Hi all,

I want to compare the result between limma + voom with DESeq2. But I don't know how to make the design matrix for my date when I am using Limma.

I have two groups: WT, ABX. Each group has 6 samples: X1~X6(WT), X7~X12(ABX). It looks like:

```
head(exprSet, n=1)
X.1 X.2 X.3 X.4 X.5 X.6 X.7 X.8
ENSMUSG00000051951 88 113 83 101 57 45 45 50
X.9 X.10 X.11 X.12
ENSMUSG00000051951 31 51 59 43
```

The row name is the geneID, the column is the count for each sample.

My code is below:

```
## read count matrix from file
exprSet=read.table("ex_matrix_g1g2_h.txt", sep="\t", header=T, row.names = 1, stringsAsFactors = F)
group_list <- factor(c(rep("WT",6), rep("ABX",6)))
design <- model.matrix(~0+group_list) ## Is this correct?
colnames(design) <- levels(group_list)
rownames(design) <- colnames(exprSet)
v <- voom(exprSet,design,normalize="quantile", plot = T)
fit <- lmFit(v,design)
fit2 <- eBayes(fit)
tempOutput = topTable(fit2, n=Inf, adjust.method = 'BH', coef=2)
## if I want to compare ABX - WT, how to set coef?
```

Unfortunately, the above code generated very different results compared with DESeq2. In limma's result, almost all genes are significantly different.

I think that maybe I used the wrong design matrix. But I am not familiar with limma. Could some help me to figure out what the matter of my code?

Thank you so much!

**3.4k**• written 16 months ago by vw •

**20**