``` r
deseq2.dds <- DESeq(deseq2.dds)
```

```
## estimating size factors
```

```
## estimating dispersions
```

```
## gene-wise dispersion estimates
```

```
## mean-dispersion relationship
```

```
## final dispersion estimates
```

```
## fitting model and testing
```

```
## -- replacing outliers and refitting for 5677 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)
```

```
## estimating dispersions
```

```
## fitting model and testing
```

``` r
deseq2.res <- results(deseq2.dds)
deseq2.res <- deseq2.res[order(rownames(deseq2.res)), ]
# Save results as a data frame
deseq2.res.df <- as.data.frame(deseq2.res)
# save significant results
res.sig <- deseq2.res.df %>% filter(padj < 0.05)
```