Seqanswers Leaderboard Ad

Collapse

Announcement

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

  • edgeR p-value and logFC

    Dear all,

    I am kind of new at RNAseq analysis. I hope someone can help me? I got like 880 genes with a p-value <0.01, using edgeR. I got the table with those 880 genes, and I separate the genes by up (+) and down (-) regulated by the LogFC option. But then I found that some of the 880 genes had a p value > 0.01 like 2,01E+05
    Is there any explanation for this? What is the best way to get the DE genes, by p-value or logFC?

    Thanks,

  • #2
    You may filter the edge$table object within R using subset functions, I'm sure you'll get the right results. Usually, as a rule of thumb, I use P<=0.05 and logFC>=1 or logFC<=-1, in other words, fold change larger than two, and still statistically significant.

    Comment


    • #3
      Thanks for your reply!! I did it again within R and I got the rigth results. But now I got another question. I have a gene that is kind of important for me:

      contig03563 -11.329612 1.0838975 1.168637e-03 4.800752e-02

      contig03563 41 47

      Based on the logFC = 1.08 is up regulated, but if I saw the raw data is down? and this happens with some others too. is ok to look ath the raw data? Could it be because of the small logFC?
      is this still statistically significant? Can I believe in this results? I am really confused with the logFC meaning.

      I'll really appreciate any help!!

      Comment


      • #4
        Looks like you don't have replicates. The difference between raw counts and edgeR might be from the normalization you did in the edgeR process. So if no replicates, I wouldn't trust the results. For me, I can't say anything if there is no replicates for 41 and 47.

        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, 06-07-2024, 06:58 AM
        0 responses
        179 views
        0 likes
        Last Post seqadmin  
        Started by seqadmin, 06-06-2024, 08:18 AM
        0 responses
        228 views
        0 likes
        Last Post seqadmin  
        Started by seqadmin, 06-06-2024, 08:04 AM
        0 responses
        184 views
        0 likes
        Last Post seqadmin  
        Started by seqadmin, 06-03-2024, 06:55 AM
        0 responses
        18 views
        0 likes
        Last Post seqadmin  
        Working...
        X