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  • finding DE genes from VSD normalized data

    Hi all,

    I have time series mRNA-seq data but without replicates.
    I have some doubts about calling DE genes with DESeq.

    I have used
    Code:
    varianceStabilizingTransformation
    function from DESeq to normalize count data. Now can I use this vsd transformed data to calculate fold change and to call DE genes?? may be using classic LIMMA package.. Is it good practice to do so ??

    I have tried
    Code:
    nbionTest
    on raw count too.. but after vsd transforming, data look more like microarray and I was wondering is it of any harm to call DE genes/FC change on vsd transfored data.. since in original DESeq paper they have made clear that count data follows poisson distribution unlike microarray which is more like normally distributed, but after vsd transformation, data looks more like normally distributed.

    Thank you.

  • #2
    You should do replicates. And DESeq uses the negative binomial, not the poisson.

    Comment


    • #3
      Sorry, I meant to say in general count data follows poisson distribution.
      But,is it ok to use vsd normalized data to detect DE genes ?? and I don't have any replicates..

      Comment


      • #4
        Does count data follow a poisson distribution? The authors of DESeq, EdgeR and others would disagree with that.

        And the purpose of VSD normalized data is not for calling differential expression, but for clustering, creating heat maps, etc. In the DESeq vignette they actually describes a protocol for analyzing data without replicates, however that does not mean you should! I honestly don't know how somebody would publish results without replicates, your really can't make sense of the data without them.
        Last edited by chadn737; 04-28-2013, 11:19 PM.

        Comment


        • #5
          ok.. not arguing.. but for your reference

          Background Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data. Results We conducted an extensive comparison of eleven methods for differential expression analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real RNA-seq data. Conclusions Very small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the ‘limma’ method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.


          and a quote from DESeq paper :

          If reads were independently sampled from a population with given, fixed fractions of genes, the read counts would follow a multinomial distribution, which can be approximated by the Poisson distribution.
          quote from DEGseq paper :

          Current observations suggest that typically RNA-seq experiments have low technical background noise (which could be checked using DEGseq) and the Poisson model fits data well.
          And even I think no replicate does not make any sense.. but the data I am using is a published one,and just I am trying out different methods to call DEG's.

          Comment


          • #6
            Originally posted by a_mt View Post
            ok.. not arguing.. but for your reference

            Background Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data. Results We conducted an extensive comparison of eleven methods for differential expression analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real RNA-seq data. Conclusions Very small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the ‘limma’ method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.


            and a quote from DESeq paper :



            quote from DEGseq paper :



            And even I think no replicate does not make any sense.. but the data I am using is a published one,and just I am trying out different methods to call DEG's.
            I apologize if I came across a little strongly.

            From the DESeq paper:

            "However, it has been noted [1,8] that the assumption of Poisson distribution is too restrictive: it predicts smaller variations than what is seen in the data. Therefore, the resulting statistical test does not control type-I error (the probability of false discoveries) as advertised."

            In other words, the Poisson distribution leads to false positives and is not suitable. That is why DESeq is based on a Negative Binomial, not a Poisson distribution:

            "To address this so-called overdispersion problem, it has been proposed to model count data with negative binomial (NB) distributions [9], and this approach is used in the edgeR package for analysis of SAGE and RNA-Seq [8,10]."

            The DESeq vignette provides protocols for analyzing data without technical replicates.

            Go here: http://bioconductor.org/packages/rel.../doc/DESeq.pdf

            and read section 3.3 titled "Working without any replicates." That will tell you how to do this in DESeq. The purpose of the VSD normalized data is to put everything on the same scale for clustering and other sorts of analysis, not for differential expression.
            Last edited by chadn737; 04-29-2013, 06:35 AM.

            Comment

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