Replicates correlation for differential expression

[seq_finder]

I have miRNA seq experiment with two conditions; (i) Asthma patients (treatments) and (ii) normal (controls). There are 9 human patients and 6 normal human that I am using as replicates for differential expression analysis by DESeq. I found following miRNAs count.

Treatments [/B]
Replicates : miRNA expressed
T_R_1 : 581
T_R_2 : 617
T_R_3 : 618
T_R_4 : 591
T_R_5 : 573
T_R_6 : 585
T_R_7 : 602
T_R_8 : 568
T_R_9 : 633

Controls[/B]
Replicates : miRNA expressed
C_R_1 : 587
C_R_2 : 560
C_R_3 : 423
C_R_4 : 572
C_R_5 : 550
C_R_6 : 535

After performing differential expression by DESeq only 42 miRNAs are significant at pval <=0.05 and fold change ±1.5. To understand the correlation between replicates I created correlation heatmap (attached) and found strange correlation. I would like to know that with this correlation the 42 miRNAs that are differentially expressed is a good result or a bad result? Waiting for a positive response, thanks in advance.

[dpryan]

1. I hope that by pval <= 0.05 you actually mean adjusted p-value < 0.1 (<0.05, not <=0.05, is common for unadjusted p-values).
2. This isn't the 90s, you don't need artificial fold-change cutoffs. Yes, you probably want to prioritize things by fold-change, but the biologically significant minimal fold-change is gene (and context) dependent.
3. DESeq or DESeq2? If DESeq, try DESeq2, it's more powerful.
4. How were the correlations determined? As in the DESeq vignette or did you code something up yourself?