Seqanswers Leaderboard Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • shurjo
    Senior Member
    • Jan 2009
    • 132

    Another DESeq question

    Hi Simon,

    First of all, many thanks for DESeq. I have found it wonderfully easy to work with, even with my beginner's knowledge of R. My understanding of statistics is limited to a couple of graduate-level courses, so I would really appreciate your input on the following questions, which arise from using DESeq on my data. My project comprises 14 individuals (7 case-control pairs) and we have ~40 million Illumina transcriptome reads from each of these people. I used BedTools to overlap these reads with the UCSC knownGene annotations, and ran DESeq on the count data.

    Question 1:
    The ECDF curves lie above the diagonal in both cases, so I used a correction factor of 0.7, which brings them closer. I have attached both the uncorrected and corrected curves below, as well as the plots of the fit of the variance functions. Can you tell me if, in your opinion, the correction factor seems adequate?

    Question 2:
    On page 10 of the manual, you mention that any hit with a very large value in any one of the last two columns in the differential expression output file should be checked carefully to exclude false hits due to “variance outliers”. Do you have a ballpark figure in mind when you say "very large value"? In my data, the values range from 0.003 - 214, so I am confused what to use as a cutoff.

    Any advice you have to offer would be very welcome.

    Thanks,

    Shurjo
    Attached Files
    Last edited by shurjo; 05-13-2010, 01:22 PM.
  • Simon Anders
    Senior Member
    • Feb 2010
    • 995

    #2
    Hi Shurio

    Originally posted by shurjo View Post
    Question 1:
    The ECDF curves lie above the diagonal in both cases, so I used a correction factor of 0.7, which brings them closer. I have attached both the uncorrected and corrected curves below, as well as the plots of the fit of the variance functions. Can you tell me if, in your opinion, the correction factor seems adequate?
    I'm afraid I wrote quite some nonsense last week, when I amended the package vignette last week to describe the variance adjustment factors, see my post here.

    Now correcting my mistake of last week, i reverse my advice: If the curves are above the green line, use the fit as it is. Bringing them down onto the green line may DESeq become anti-conservative due to the curves now being partly too low.

    Originally posted by shurjo View Post
    Question 2:
    On page 10 of the manual, you mention that any hit with a very large value in any one of the last two columns in the differential expression output file should be checked carefully to exclude false hits due to “variance outliers”. Do you have a ballpark figure in mind when you say "very large value"? In my data, the values range from 0.003 - 214, so I am confused what to use as a cutoff.
    I am still trying to find a good general answer to this question. I have an idea, and I may add a feature quite soon for this. Until then, you may want to plot a histogram of variance values, e.g., with

    Code:
    hist( res$resVarA, breaks=100 )
    hist( res$resVarB, breaks=100 )
    You will probably notice that 99% or so of the values are below 10 or 20 or so and only very few as far up at 214. This histogram will help you judge which values are clearly outliers, i.e., out of the range of most of the values.

    For two or three replicates, a cut-off around 15 or 20 seems to be good, but as you have many more replicates, your histogram may look different.

    Cheers
    Simon

    Comment

    • shurjo
      Senior Member
      • Jan 2009
      • 132

      #3
      Hi Simon,

      Your advice makes perfect sense, thanks.

      Regards,

      Shurjo

      Comment

      Latest Articles

      Collapse

      • seqadmin
        Pathogen Surveillance with Advanced Genomic Tools
        by seqadmin




        The COVID-19 pandemic highlighted the need for proactive pathogen surveillance systems. As ongoing threats like avian influenza and newly emerging infections continue to pose risks, researchers are working to improve how quickly and accurately pathogens can be identified and tracked. In a recent SEQanswers webinar, two experts discussed how next-generation sequencing (NGS) and machine learning are shaping efforts to monitor viral variation and trace the origins of infectious...
        03-24-2025, 11:48 AM
      • seqadmin
        New Genomics Tools and Methods Shared at AGBT 2025
        by seqadmin


        This year’s Advances in Genome Biology and Technology (AGBT) General Meeting commemorated the 25th anniversary of the event at its original venue on Marco Island, Florida. While this year’s event didn’t include high-profile musical performances, the industry announcements and cutting-edge research still drew the attention of leading scientists.

        The Headliner
        The biggest announcement was Roche stepping back into the sequencing platform market. In the years since...
        03-03-2025, 01:39 PM

      ad_right_rmr

      Collapse

      News

      Collapse

      Topics Statistics Last Post
      Started by seqadmin, 03-20-2025, 05:03 AM
      0 responses
      49 views
      0 reactions
      Last Post seqadmin  
      Started by seqadmin, 03-19-2025, 07:27 AM
      0 responses
      57 views
      0 reactions
      Last Post seqadmin  
      Started by seqadmin, 03-18-2025, 12:50 PM
      0 responses
      50 views
      0 reactions
      Last Post seqadmin  
      Started by seqadmin, 03-03-2025, 01:15 PM
      0 responses
      201 views
      0 reactions
      Last Post seqadmin  
      Working...