Hello, I would like to use mitoXplorer for my DE genes link and the required input format is like below.

```
Dataset ID+ Gene Identifier^ Control Mutant Log2Fold* p-value
dataset1 MT-CO2 5.14185 0.525591 -3.29027 0.04165
dataset1 EPHB2 21.6398 77.9794 1.84941 0.03615
dataset1 CD52 113979 24.01 -2.24706 0.14155
dataset1 MFSD2A 2671 16.7198 2.64611 0.0318
```

I did DE using DESeq2 and get a classical output like this:

```
gene baseMean log2FoldChange lfcSE stat pvalue padj
gene1 145,6 3,72051115683167 0,228357536526171 16,5624981658657 1,30079132918825E-61 9,5818528058662E-60
gene2 132,89 2,64563360440389 0,48507809031081 6,0969129994873 1,08136332656555E-09 7,23746649195554E-09
gene3 1200 3,47737947525206 0,371740409280336 9,80334240603984 1,08920561006602E-22 1,90277551472758E-21
gene4 12,3 4,36963212258223 0,156024000446566 28,181259678469 9,92432650971091E-175 4,24761174615627E-172
```

Now, what I am trying to do is to get the normalized counts of dds and calculate the mean normalized expression values of controls and mutants in order to get 3rd and 4th columns required to mitoXplorer:

```
normalized_counts_dfr <- as.data.frame(counts(dds, normalized=TRUE))
names(normalized_counts_dfr) <- c("mutant","mutant","mutant","control","control","control")
normalized_counts_dfr$mutant_mean <- apply(normalized_counts_dfr[c(1:3],1, mean)
normalized_counts_dfr$control_mean <- apply(normalized_counts_dfr[c(4:6)],1, mean)
newdata <- normalized_counts_dfr[-c(1:6)]
newdata$log <- log2(newdata$mutant_mean/newdata$control_mean)
```

However, the log2FC value I get using this calculation is slightly different from the log2FC value I get with DESeq2. I guess this is because DESeq2 uses a different algorithm? My question is then how can I get the mean normalized expression values of mutants and controls that were used in DESeq2 algorithm before calculating the log2FC? Since for other analysis I am using the log2FC value I got with DESeq2, I cannot use different log2FC for the mitoXplorer analysis.