Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • Mocca
    Junior Member
    • Mar 2012
    • 9

    RNAseq time series data w/ controls for each time point

    Hi everyone,

    I am currently working on a RNAseq data set consisting of 4 time points where each time point has 6 challenged and 6 control individuals (not paired). I have used both DEseq and edgeR for pairwise comparisons such as: time 1 - challenged vs control which worked nicely.

    I am not sure that the reported DE genes at one time point is comparable to the DE genes at another time point. Thus I am looking for a package that can handle time series data. I have found several but none of them handles controls on each time point but prefers a base line expression level or a time 0 (which I don't have).

    I can manage with my pairwise analyses, but if any of you have good suggestions to what I might try I would appreciate it greatly!

    Thanks in advance
  • dpryan
    Devon Ryan
    • Jul 2011
    • 3478

    #2
    You could use DESeq or edgeR still and just not do the pair-wise comparisons. Using DESeq syntax:
    Code:
    fit1 <- fitNbinomGLMs(cds, count~treatment*time)
    fit0 <- fitNbinomGLMs(cds, count~time + treatment:time)
    pvals <- nbinomGLMTest(fit1, fit0)
    ...assuming you're interested in genes that are differentially expressed by treatment when accounting for time point and a possible time-point:treatment interaction.

    Comment

    • Mocca
      Junior Member
      • Mar 2012
      • 9

      #3
      Thanks for the reply.

      I didn't consider doing it this way. Got a bit caught up in my control samples i guess. Will give it a try!

      Cheers

      Comment

      Latest Articles

      Collapse

      • GATTACAT
        Reply to Nine Things a Sample Prep Scientist Thinks About Before Sequencing
        by GATTACAT
        Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.
        07-01-2026, 11:43 AM
      • SEQadmin2
        Nine Things a Sample Prep Scientist Thinks About Before Sequencing
        by SEQadmin2


        I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.

        Here are nine questions we think about, in roughly the order they matter, before...
        06-18-2026, 07:11 AM

      ad_right_rmr

      Collapse

      News

      Collapse

      Topics Statistics Last Post
      Started by SEQadmin2, 07-02-2026, 11:08 AM
      0 responses
      13 views
      0 reactions
      Last Post SEQadmin2  
      Started by SEQadmin2, 06-30-2026, 05:37 AM
      0 responses
      15 views
      0 reactions
      Last Post SEQadmin2  
      Started by SEQadmin2, 06-26-2026, 11:10 AM
      0 responses
      20 views
      0 reactions
      Last Post SEQadmin2  
      Started by SEQadmin2, 06-17-2026, 06:09 AM
      0 responses
      54 views
      0 reactions
      Last Post SEQadmin2  
      Working...