Seqanswers Leaderboard Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • gabe_rosser
    Junior Member
    • Mar 2017
    • 8

    Individual-level RNA-Seq differential expression analysis with paired sample design

    Hi all, I'd appreciate any thoughts that will help us with the experimental design for our project.

    Our experimental design involves collecting paired samples (case-control) for multiple patients. We use RNA-Seq data to measure differential expression. Getting biological replicates is difficult and at present we just have the 'typical' unreplicated paired design:

    Patient 1 case, Patient 1 control
    Patient 2 case, Patient 2 control
    etc.

    In our case, we really want to focus on individual-level differences. However, without biological replicates we can't compute any robust statistics describing the statistical significance of the findings in a direct n=1 case-control comparison.

    We can carry out the analysis using edgeR (I'm sure other packages such as deseq2 also support a similar process - it's just that edgeR has a good section in the user guide about this scenario). We first pool our samples over all patients to compute the coefficient of variation (either genewise or common) with just case and control groups. This gives us an estimate based on a number of 'effective biological replicates', then we can use this estimate to compute p values in the individual comparisons.

    Those p values reflect the probability of observing the single paired sample difference in expression, based on a background model constructed from a pool of all pairs.

    This isn't ideal, I know. But would you, as a reviewer, accept a paper describing that approach to DGE analysis?

    Are you aware of any studies using this approach, or analysing the effectiveness?

    By my reckoning, this will be a *conservative* approach, since it is likely to overestimate the genewise variance when a reasonable number of patients is included, on the assumption that variation between patients is greater than variation between biological replicates. Therefore we are more likely to have an issue with false negatives, rather than false positives. Does this help to justify it?

    Thanks for any thoughts.

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...