Using One condition and 4 times, I have compared each Time point and got a little surprising results - all comparisons have same pvalues and padj. Can anyone tell me if what I am doing is correct and my doubt it valuable

Indeed, I also figure out that baseMean in between two time points should not be the same. But I have it. And I really don't know why?

script in DESeq2:

```
> # design
> ExpDesign <- data.frame(row.names=colnames(ko), condition = design)
> ExpDesign
condition.label condition.Time
KO.T1.1 KO T1
KO.T1.2 KO T1
KO.T1.3 KO T1
KO.T2.1 KO T2
...
.......
> ##DESeqDataSet
> bckCDS <- DESeqDataSetFromMatrix(countData = ko, colData=ExpDesign, design= ~condition.Time)
> ddsLRT= DESeq(bckCDS, test ="LRT", fitType='local', reduced=~1)
> resultsNames(ddsLRT)
> resultsNames(ddsLRT)
[1] "Intercept" "condition.Time_T2_vs_T1" "condition.Time_T3_vs_T1"
[4] "condition.Time_T4_vs_T1"
> ## by default first (T1) and last (T4) comparison
> resLRT=results(ddsLRT, cooksCutoff=F, independentFiltering = F)
> resLRT$symbol <- mcols(ddsLRT)$symbol
> head(resLRT[order(resLRT$pvalue),],2)
log2 fold change (MLE): condition.Time T4 vs T1
LRT p-value: '~ condition.Time' vs '~ 1'
DataFrame with 4 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
mmu-miR-145b 35.51911 -0.4172776 0.3513721 56.82489 2.800864e-12 3.431058e-09
mmu-miR-181a-2-3p 144.74966 -0.8433754 0.5111826 42.11996 3.783699e-09 1.792311e-06
> sum(resLRT$padj < 0.05)
[1] 203
> ## for each time point
> lfc_resKO_T2_T1 <- results(ddsLRT,cooksCutoff=F, independentFiltering = F, contrast=c("condition.Time","T2","T1"))
> head(lfc_resKO_T2_T1[order(lfc_resKO_T2_T1$pvalue),],2)
log2 fold change (MLE): condition.Time T2 vs T1
LRT p-value: '~ condition.Time' vs '~ 1'
DataFrame with 4 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
mmu-miR-145b 35.51911 -2.6590521 0.4283991 56.82489 2.800864e-12 3.431058e-09
mmu-miR-181a-2-3p 144.74966 -3.2386468 0.5431705 42.11996 3.783699e-09 1.792311e-06
> sum(lfc_resKO_T2_T1$padj < 0.05)
[1] 203
> lfc_resKO_T3_T2 <- results(ddsLRT,cooksCutoff=F, independentFiltering = F, contrast=c("condition.Time","T3","T2"))
> head(lfc_resKO_T3_T2[order(lfc_resKO_T3_T2$pvalue),],2)
log2 fold change (MLE): condition.Time T3 vs T2
LRT p-value: '~ condition.Time' vs '~ 1'
DataFrame with 4 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
mmu-miR-145b 35.51911 2.75662374 0.3913252 56.82489 2.800864e-12 3.431058e-09
mmu-miR-181a-2-3p 144.74966 3.08362802 0.4757690 42.11996 3.783699e-09 1.792311e-06
> sum(lfc_resKO_T3_T2$padj < 0.05)
[1] 203
```

Thank you and any advice?