Seqanswers Leaderboard Ad

Collapse

Announcement

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

  • Getting differentially expressed genes based on RPKM values

    Hello,
    I have essentially 2 datasets, each showing the RPKM values for each gene. I want to compare the gene expressions from one set to another, and see which ones are up-regulated or down-regulated significantly comparing to the other.
    I have tried some primitive ways, such as dividing the two by each other and see if that ratio is greater than a fold change threshold..but this yields to me like 10000 genes, which is unlikely.

    Are there any suggestions on how to find differentially expressed genes based on RPKM values?

    btw I don't have access to the mapped reads data, so programs like DEGseq won't work for me. I only have access to the RPKM values

    Thanks!

    *sorry in advance, but I've also double-posted this in the bioinformatics section, because I wasn't sure how this forum was organized (sry i'm new).

  • #2
    Use Z-test

    I think you can you Z-test to find the different expressed genes(DEG). you can also control the P value.

    Comment


    • #3
      Originally posted by casshyr View Post
      Hello,

      Are there any suggestions on how to find differentially expressed genes based on RPKM values?

      btw I don't have access to the mapped reads data, so programs like DEGseq won't work for me. I only have access to the RPKM values
      Your best bet would be to reverse calculate. If you have the transcript lengths, then you could factor that out and assume a standard read depth for all libraries, thus giving you an estimated raw tag count that you could then use as input for DEGSeq or DESeq.

      Just a thought

      Comment


      • #4
        If the fpkm values are a result of cuffdiff then you can use the cummeRbund package in R to do your differential expression analysis

        Comment

        Latest Articles

        Collapse

        • seqadmin
          Exploring the Dynamics of the Tumor Microenvironment
          by seqadmin




          The complexity of cancer is clearly demonstrated in the diverse ecosystem of the tumor microenvironment (TME). The TME is made up of numerous cell types and its development begins with the changes that happen during oncogenesis. “Genomic mutations, copy number changes, epigenetic alterations, and alternative gene expression occur to varying degrees within the affected tumor cells,” explained Andrea O’Hara, Ph.D., Strategic Technical Specialist at Azenta. “As...
          07-08-2024, 03:19 PM
        • seqadmin
          Exploring Human Diversity Through Large-Scale Omics
          by seqadmin


          In 2003, researchers from the Human Genome Project (HGP) announced the most comprehensive genome to date1. Although the genome wasn’t fully completed until nearly 20 years later2, numerous large-scale projects, such as the International HapMap Project and 1000 Genomes Project, continued the HGP's work, capturing extensive variation and genomic diversity within humans. Recently, newer initiatives have significantly increased in scale and expanded beyond genomics, offering a more detailed...
          06-25-2024, 06:43 AM

        ad_right_rmr

        Collapse

        News

        Collapse

        Topics Statistics Last Post
        Started by seqadmin, 07-16-2024, 05:49 AM
        0 responses
        22 views
        0 likes
        Last Post seqadmin  
        Started by seqadmin, 07-15-2024, 06:53 AM
        0 responses
        30 views
        0 likes
        Last Post seqadmin  
        Started by seqadmin, 07-10-2024, 07:30 AM
        0 responses
        40 views
        0 likes
        Last Post seqadmin  
        Started by seqadmin, 07-03-2024, 09:45 AM
        0 responses
        205 views
        0 likes
        Last Post seqadmin  
        Working...
        X