Seqanswers Leaderboard Ad

Collapse

Announcement

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

  • Calculate RPKM of replicates

    I can't find anything through searching so this must be a pretty noob question but any comments are appreciated.
    I want to display abundance in expression experiments in a common format regardless of the tool used for the analysis (i.e. EdgeR, DESeq).
    We have chosen RPKM to represent abundance using the formula:
    (number of reads/kb of exon)/mill mapped reads

    When there are multiple replicates how are the runs combined?:
    1) the total count from all of the replicates (and the sum of the mapped reads from all of the replicates).
    2) the average count of the replicate counts (The sum of the average counts used for the number of mapped reads)
    3) the base mean of the replicate counts (the sum of the base mean used as the of mapped reads)

    Thanks,
    Bill

  • #2
    I would think you would want the average and the standard deviation.

    You do replicates so that you know how much variation is natural, so you can detect exceptional levels of variation.

    Comment


    • #3
      Thanks for the reply. This doesn't seem as common as I thought it was. Talking to other people they calculate a separate RPKM for each replicate and don't combine them. I want an RPKM version of DESeqs base mean.
      Wouldn't the standard deviation be redundant to the p-value, and fdr?

      I'm starting to think that calculating RPKM from averaged counts, averaged RPKMs, or from the base mean are all pretty much the same.

      Comment


      • #4
        What kind of replicates are they? What specifically is the purpose?

        Comment


        • #5
          There are four conditions. Two technical replicates per condition. The purpose is differential expression.

          Comment

          Latest Articles

          Collapse

          • seqadmin
            Best Practices for Single-Cell Sequencing Analysis
            by seqadmin



            While isolating and preparing single cells for sequencing was historically the bottleneck, recent technological advancements have shifted the challenge to data analysis. This highlights the rapidly evolving nature of single-cell sequencing. The inherent complexity of single-cell analysis has intensified with the surge in data volume and the incorporation of diverse and more complex datasets. This article explores the challenges in analysis, examines common pitfalls, offers...
            06-06-2024, 07:15 AM
          • seqadmin
            Latest Developments in Precision Medicine
            by seqadmin



            Technological advances have led to drastic improvements in the field of precision medicine, enabling more personalized approaches to treatment. This article explores four leading groups that are overcoming many of the challenges of genomic profiling and precision medicine through their innovative platforms and technologies.

            Somatic Genomics
            “We have such a tremendous amount of genetic diversity that exists within each of us, and not just between us as individuals,”...
            05-24-2024, 01:16 PM

          ad_right_rmr

          Collapse

          News

          Collapse

          Topics Statistics Last Post
          Started by seqadmin, Yesterday, 07:24 AM
          0 responses
          9 views
          0 likes
          Last Post seqadmin  
          Started by seqadmin, 06-13-2024, 08:58 AM
          0 responses
          11 views
          0 likes
          Last Post seqadmin  
          Started by seqadmin, 06-12-2024, 02:20 PM
          0 responses
          16 views
          0 likes
          Last Post seqadmin  
          Started by seqadmin, 06-07-2024, 06:58 AM
          0 responses
          184 views
          0 likes
          Last Post seqadmin  
          Working...
          X