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  • Filtering differentially expressed genes with CummeRbund

    Hi,

    I am new to RNA-seq analysis and I am trying to analysis some data by the tuxedo pipeline.

    My samples are control cells and cells with siRNA treatment, each with 4 replicates so 8 samples in total. There are ~40 million reads in each sample. After running these data through cuffdiff, it generates a list of ~9000 genes that are differentially expressed significantly. I went through the list and found that a lot of them have extremely low FPKM value (less than 0.1) and very low fold changes (less than 1.3).

    So I have been filtering these with an arbitrary threshold of FPKM>2 and fold change>1.5. I did that in excel and that got me about 900 genes left. My question is that how can I do that in cummeRbund so that I can plot heatmap and other plots with the filtered results?

    Thank you very much!

  • #2
    Code:
    cuff <- readCufflinks()
    gene.diff <- diffData(genes(cuff))
    filtered <- gene.diff[gene.diff$log2_fold_change > 1.5 & gene.diff$value_1 > 2,]
    value_1, value_2 and so on is your samples.

    Comment


    • #3
      Originally posted by TiborNagy View Post
      Code:
      cuff <- readCufflinks()
      gene.diff <- diffData(genes(cuff))
      filtered <- gene.diff[gene.diff$log2_fold_change > 1.5 & gene.diff$value_1 > 2,]
      value_1, value_2 and so on is your samples.
      That worked! Thank you so much!

      Comment


      • #4
        Quick question,
        If I have 6 conditions (C1-C6), with C1 being my control, do i type in "...gene.diff$ C1 > 2 ,3,4,5,6,]?

        Comment


        • #5
          hi!
          this thread is the closest I've found to my issue ...

          I have 2 conditions (a control and a treatment), each with 3 replicates. How can I do if I want to get those genes showing FPKM > 1, for each replicate, for control OR treatment?

          I mean that, for example, a gene must have in control at least fpkm = 1, for all replicates #1, #2 and #3, even if it doesn't happen for the treatment; and vice versa.

          This actually works:

          gene.diff.filtered <- gene.diff[gene.diff$value_1 > 1 & gene.diff$value_2 > 1,]

          but it's not what i want since the fpkm is calculated over the three replica together, and not separately.


          Thanks for any hint!
          Cheers

          stefano
          Last edited by sghignone; 03-13-2015, 08:47 AM. Reason: completeness

          Comment


          • #6
            I used this to filter my 10,000+ DEGs to a more reasonable number so I could plot heatmaps with the filtered results. Now I can't figure out how to use the filtered results to plot a heatmap. I have tried a few different ways, but none are successful - I cannot use getSig function or get the column of gene_ids from the filtered table into a list to use the getGenes function, so am not sure what other approach to take. I would appreciate if you could share how you did this. Thanks very much!

            Comment

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