The numbers in themselves don't necessarily imply too few biological replicates. The numbers you detect depend on numerous properties of the experiment or study, and the conditions or treatments being compared (or more complex relationship for more complex experimental designs): as you mention the sample size (number of biological replicates), but also the true effect sizes or interaction sizes for each gene, the variability among biological replicates, the sequencing depth, just to mention a few.

It could be an under-powered experiment, or an under-sequenced experiment, or a well-powered experiment with sufficient sequencing depth, low biological variability and the "treatment" only induces a few changes in expression. 

Hello Michael,

if you remember, I wanted to do DE for all treated vs all controls samples.and you suggested me a design of ~experiment + condition, I need DE for co-expression network,so for coding and non coding I did that,but I see that there are a variety value in log2 fold change.I can not understand which has important role in select genes,fold change or adjust p-value?

this the result for coding;

out of 19933 with nonzero total read count

adjusted p-value < 0.1

LFC > 0 (up) : 335, 1.7%

LFC < 0 (down) : 300, 1.5%

outliers [1] : 289, 1.4%

low counts [2] : 6012, 30%

(mean count < 61)

I should say that samples are similar for coding and non coding but the result are very different!