Header Leaderboard Ad


cuffdiff and limma, puzzled by the differences



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

  • cuffdiff and limma, puzzled by the differences

    Hi all, I've been using cuffdiff for a long time but recently I've given limma (voom) a try for RNA-seq data and compared the results.
    The experiment is quite simple, a triplicate in a treatment vs control experiment (60M read tags for each sample). Cuffdiff finds almost 300 DGE, where limma finds almost 900. Almost 100 are in common between the two.
    I have qPCR control for 3 genes and, according to the fold change, both the approaches are concordant with qPCR data. I understand that this is not an extensive test, but it is what can ben typically done within a lab (I mean, check few genes by qPCR). As a matter of facts, the FPKM quantification and the CPM quantification (cuffdiff vs limma) are quite consistent, the statistical test is making all the difference.
    I've tried some pathway analysis to see if the two approaches are similar from the ontologic point of view... well, this is not really the case (there are some overlapping pathways, of course, but all in all the experiments are saying something different)
    I see that others have tried such comparisons, with different results, now I realise I'm stuck in choosing the proper analysis approach... any hint?


  • #2

    Depending on what's downstream experiment(s), you may focus on the overlapping 100 genes. (I assume you used the same fold/FDR criteria for both programs.) You may also plot them on a volcano plot to see where most of the overlapping occurs. If it occurs at high folder change and/or low FDR, you may tighten your filtering criteria accordingly to improve the overlap pool.

    Best regards,


    Latest Articles


    • seqadmin
      A Brief Overview and Common Challenges in Single-cell Sequencing Analysis
      by seqadmin

      ​​​​​​The introduction of single-cell sequencing has advanced the ability to study cell-to-cell heterogeneity. Its use has improved our understanding of somatic mutations1, cell lineages2, cellular diversity and regulation3, and development in multicellular organisms4. Single-cell sequencing encompasses hundreds of techniques with different approaches to studying the genomes, transcriptomes, epigenomes, and other omics of individual cells. The analysis of single-cell sequencing data i...

      01-24-2023, 01:19 PM
    • seqadmin
      Introduction to Single-Cell Sequencing
      by seqadmin
      Single-cell sequencing is a technique used to investigate the genome, transcriptome, epigenome, and other omics of individual cells using high-throughput sequencing. This technology has provided many scientific breakthroughs and continues to be applied across many fields, including microbiology, oncology, immunology, neurobiology, precision medicine, and stem cell research.

      The advancement of single-cell sequencing began in 2009 when Tang et al. investigated the single-cell transcriptomes
      01-09-2023, 03:10 PM