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Hi Faranak,

Did it work for you the WGCNA with the normalized values? Did you find another possible solution?
When I use the normalized values it gives me a weird scale free topology model fit.

Thank you,
Spanos

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  • Peter Langfelder at Mar 7, 2014 at 7:45 am
    Hi Spanos,


    you can certainly use WGCNA for RNA-seq data. Two recommendations: 1.
    Filter out genes whose count is less than say 5 in more than say 80%
    of the samples. This gets rid of a lot of noise and gets rid of
    expression profiles for which correlation makes little sense. 2. Use a
    variance-stabilizing transformation, such as the one implemented in
    varianceStabilizingTransformation or rlogTransformation in the DESeq2.


    I have analyzed a few RNA-seq data sets and have had great results.


    Hope this helps,


    Peter

    On Thu, Mar 6, 2014 at 4:32 PM, spano spano wrote:
    Hi Faranak,

    Did it work for you the WGCNA with the normalized values? Did you find another possible solution?
    When I use the normalized values it gives me a weird scale free topology model fit.

    Thank you,
    Spanos


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