I try to do DE analysis of a miR RNAseq dataset

I used miRPlant to identify and annotate miRs.

I was aiming to analyze DE of my miRs with DEseq2.

My data consist of 2 replicates. 2 times treated 2 times untreated. Many of the readcounts are relatively low.

dds <- DESeqDataSetFromMatrix(countData = countData, colData = colData, design = ~ condition)
dds
dds <- DESeq(dds)
result <- results(dds)
> summary(result)
out of 11160 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)     : 0, 0%
LFC < 0 (down)   : 0, 0%
outliers [1]     : 0, 0%
low counts [2]   : 0, 0%
(mean count < 1)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

This is the summary of the result. Where is the problem? Why are there no up or down regulated miRs?

Is my problem that the mean count is <1?

Any suggestions what is wrong?

Two replicates and low read counts means that it wasn't possible to tell if observed differences were rising above the level of technical and biological variation. Two replicates here is in general under-powered except for very large differences. Perhaps with more samples or higher sequencing depth, you might find some differences.