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  • Replicates correlation for differential expression

    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.
    Attached Files
    Last edited by seq_finder; 11-22-2013, 07:44 AM.

  • #2
    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?

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