Entering edit mode

I have 3 donors, and each donor has 2 samples (KO and WT). The matrix is:

```
Donor_ID Condition
1 WT
1 KO
2 WT
2 KO
3 WT
3 KO
```

I am interested in looking at differentially expressed genes (DEG) between KO and WT while correcting for donor variability.

I have doubts about my model, especially regarding the intercept. Should it be:

```
dds <- DESeqDataSetFromMatrix(data, colData = meta, design = ~ Donor_ID + Condition)
res <- results(dds, name = "Condition_KO_vs_WT")
```

or

```
dds <- DESeqDataSetFromMatrix(data, colData = meta, design = ~ 0 + Donor_ID + Condition)
res <- results(dds, name = "ConditionKO")
```

because I get different results after using lfcShrink.

1) I looked at ExploreModelMatrix, but it didn't help me much. For instance, in the first model with an intercept, I don't see any mention of the coefficient

`Condition_KO_vs_WT`

(which I can also get from`resultsNames(dds)`

). Instead, I see the coefficient`ConditionKO`

, which is a bit confusing. Why is that?2) If the first model represents the KO vs WT comparison, how can we interpret the second model without an intercept?

I have to restrict my time on the support site for software related questions.

For questions about statistical designs and analysis choices, I'd recommend consulting with a local statistician, or anyone familiar with linear models in R. DESeq2 uses the same linear modeling framework as basic linear models implemented in R, e.g.

`lm`

and`model.matrix`

.