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  • Cuffdiff results; no replicates

    Hello everyone,

    We are working with some data in lab in which we are comparing two different treatments but did not have the sample means necessary for replicates. When I run cuffdiff I only get 30 or so transcripts that are significantly DE out of the few thousand or so. The weird part is that all of these transcripts it finds significant are expressed in one sample at a fairly high level and not expressed in the other sample at all. When running DESeq I am seeing no significance in any transcripts for DE analysis. This information seems rather non-essential to be kind and I know our problem is no replicates. Telling me that a transcript that isn't even expressed in one population but highly in the other(cuffdiff) is differentially expressed is stating the very obvious.

    My thoughts on how to approach our data are as follows and I was looking for some more RNA-Seq savvy people to let me know if this is acceptable or not as we are fairly new to RNA-Seq and this is our first sample to sample comparison.

    What I proposed we do with our data is to take cufflinks FPKM values and just simple evaluate ratios, calling anything with say 2:1 ratio DE and having higher ratios (ex. 5:1; 10:1) in separate categories. We have looked at the data in this manner and tried to validate with our little bit of leftover sample on qPCR to determine if these ratios are indeed truly represented in the sample via validation.

    Could this be an acceptable alternative to cuffdiff/deseq/etc for not having replicates and those analyses offering little to no useful information? Is validation via qPCR essential or could these ratios be reported as is?

    I feel our data is still very useful exploratory data as RNA-Seq has never been done to compare these 2 sample types but the DE analysis has been lacking due to our lack or replicates.

    Thank you in advance for any input,

    -C

  • #2
    If DESeq did not found any significant results, maybe this is the result. The problem with the pure DE ratio is it is produce a large number of false positives.

    Comment


    • #3
      DESeq and all other DE software like edreR... are not capable of calculating DE-Genes w/o replicates. In fact you can't (of course you can force them but...) use this kind of software for that. Your approach of calculating fold-changes between your samples is the only way to get something out of the data. Since you have no replicates, imho you have to validate everything you want to report via qPCR / qRT-PCR just to handle the huge amount of flase positives as TiborNagy said - just my two cents.

      Comment


      • #4
        Hi ccard28,

        I also work with similar case.. No replicates but i follow

        * Tophat-Cufflinks ( FPKM values from cufflinks not cuffdiff ) or HTSeq read count and then i do analysis based on gene of interest and high log fold differences and for those we validate with qPCR.

        Its all up to U how you setup. All the current tools are better with replicates ( That produce a bit more confident results. ). No replicates means one has to validate..

        Even with the replicates better to validate up to some extent.
        Krishna

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        • #5
          Thank you everyone for all of your input. This has helped me make a decision on our data moving forward using a variant of ratio analysis with some qPCR validation.

          Does anyone have a reference handy for an RNA-Seq paper that did not use replicates and skipped computational methods such as cuffdiff/deseq but did some validation via qPCR?

          Comment


          • #6
            Hi ccard28,

            I tried Gfold. This seems good when no replicates.. it rank based on logfold data.

            For more details
            Look at the paper. http://www.ncbi.nlm.nih.gov/pubmed/22923299

            Source code : https://bitbucket.org/feeldead/gfold/downloads


            Have a nice time
            Krishna

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