Seqanswers Leaderboard Ad

Collapse

Announcement

Collapse
No announcement yet.
X
 
  • Filter
  • Time
  • Show
Clear All
new posts

  • EdgeR and Deseq Normalization problems

    Hi all,

    I am trying to run DE analysis on miRNAs samples (from two conditions).

    In a recently published article it was mentioned that "TMM" normalization method is the least suitable to miRNAs and they suggested to use quantile normalization.

    I tried to change the method parameter to "upperquantile"
    y <- calcNormFactors(y,method="upperquartile")

    What I was getting when running the next step is:
    y <- estimateCommonDisp(y) # Estimates common dispersion
    Error in if (mx < tol) { : missing value where TRUE/FALSE needed

    When looking at the y variable I have noticed that all of the norm.factors are NaN.
    Worth mentioning that I didn't have this problem when running the "TMM" and "RLE" normalization methods.

    My questions:
    1. What might cause the problem when running "upperquantile" normalization in EdgeR?
    2. How can I use the "quantile" normalization and not the "upperquantile"? Change the Percentile some how? (p parameter)
    3.From the manual I noticed that the "RLE" normalization is similar to the implemented normalization in DEseq , but when running EdgeR with "RLE" vs DEseq I have been getting very different results ? Why is that?

    I will appreciate your answers,

    Thanks,
    Moriah

  • #2
    Hi Moriah,

    Some quick thoughts:

    1. I'm afraid that I can't tell from the information you've provided why method="upperquantile" gives NaNs. If you send me a count table, I can have a look, there should be a simple explanation.

    2. No, changing the percentile will not allow quantile normalization. It is possible to do quantile normalization in edgeR through offsets (keeping the count data as is), but this is not what the authors of the miRNA paper are suggesting (I've only skimmed that paper and it wasn't clear how they used the normalized data in the differential testing and it appears that their testing strategy does not account for biological variation - binomial or Fisher's test).

    3. Yes, basically with method="RLE", you will get the same normalization factors in edgeR (lib.size * norm.factor) as sizeFactors() in DESeq. But, edgeR and DESeq differ slightly in how the dispersions are estimated and how the exact test is performed. You mentioned "very different results", what do you mean by this?

    If this doesn't answer your question, can I suggest you post to the Bioconductor mailing list? See the section "how to get help" in the edgeR User's Guide.

    Best,
    Mark

    Comment


    • #3
      Hi Mark,

      Thank you for your answer!

      1. How can I send you my table? Thanks for the suggestion!
      2. What method would you suggest when dealing with DE of micorRNA?
      3. In DESeq I got one differentially expressed microRNA, and in EDgeR I got around 100.

      Thanks,
      Moriah

      Comment


      • #4
        @moriah : Any luck with your new analysis with upper-quartile... as you were following methods in this article :
        "Evaluation of normalization methods in mammalian microRNA-Seq data."

        Comment


        • #5
          Hi
          I am getting all the p- values and FDR values as non-significant while performing DE analysis of miRNA's in DEseq. Whereas almost all are significant with chi-square test.What is the reason for such discrepancy? How should I use DEseq to get meaningful result of DE analysis for miRNA?

          Comment

          Latest Articles

          Collapse

          • seqadmin
            Quality Control Essentials for Next-Generation Sequencing Workflows
            by seqadmin




            Like all molecular biology applications, next-generation sequencing (NGS) workflows require diligent quality control (QC) measures to ensure accurate and reproducible results. Proper QC begins at nucleic acid extraction and continues all the way through to data analysis. This article outlines the key QC steps in an NGS workflow, along with the commonly used tools and techniques.

            Nucleic Acid Quality Control
            Preparing for NGS starts with isolating the...
            02-10-2025, 01:58 PM
          • seqadmin
            An Introduction to the Technologies Transforming Precision Medicine
            by seqadmin


            In recent years, precision medicine has become a major focus for researchers and healthcare professionals. This approach offers personalized treatment and wellness plans by utilizing insights from each person's unique biology and lifestyle to deliver more effective care. Its advancement relies on innovative technologies that enable a deeper understanding of individual variability. In a joint documentary with our colleagues at Biocompare, we examined the foundational principles of precision...
            01-27-2025, 07:46 AM

          ad_right_rmr

          Collapse

          News

          Collapse

          Topics Statistics Last Post
          Started by seqadmin, 02-07-2025, 09:30 AM
          0 responses
          68 views
          0 likes
          Last Post seqadmin  
          Started by seqadmin, 02-05-2025, 10:34 AM
          0 responses
          108 views
          0 likes
          Last Post seqadmin  
          Started by seqadmin, 02-03-2025, 09:07 AM
          0 responses
          83 views
          0 likes
          Last Post seqadmin  
          Started by seqadmin, 01-31-2025, 08:31 AM
          0 responses
          47 views
          0 likes
          Last Post seqadmin  
          Working...
          X