I have an experimental design that I did in 2 blocks (1 week apart). I'm struggling to understand how DESeq2 deals with covariates.

I checked a PCA and there is no clustering of blocks.

I've run a model

```
dds <- DESeqDataSetFromMatrix(countData = genes.matrix,
colData = sample_info,
design = ~ Group + block)
```

where block is coded as -0.5 and +0.5, with the idea that this should mean center the effects? But is this true? I've also coded as "B1" and "B2" but this has no impact on what DEGs are expressed.

How do I tell if block is having a significant influence on the overall results?

Would one interpret a significant result in group as occurring over and above variation in block? as you would in a regular linear model?

Any help appreciated!