Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • yangruialex
    Junior Member
    • Sep 2013
    • 2

    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!
  • TiborNagy
    Senior Member
    • Mar 2010
    • 329

    #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

    • yangruialex
      Junior Member
      • Sep 2013
      • 2

      #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

      • gareth.lim
        Junior Member
        • Feb 2014
        • 3

        #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

        • sghignone
          Junior Member
          • Nov 2008
          • 9

          #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

          • kwoolf
            Junior Member
            • Mar 2017
            • 1

            #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

            Latest Articles

            Collapse

            • SEQadmin2
              Nine Things a Sample Prep Scientist Thinks About Before Sequencing
              by SEQadmin2


              I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.

              Here are nine questions we think about, in roughly the order they matter, before...
              06-18-2026, 07:11 AM
            • SEQadmin2
              From Collection to Sequencing: Why Sample Preparation and Preservation Define Sequencing Data
              by SEQadmin2


              Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.


              The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
              ...
              06-02-2026, 10:05 AM

            ad_right_rmr

            Collapse

            News

            Collapse

            Topics Statistics Last Post
            Started by SEQadmin2, 06-17-2026, 06:09 AM
            0 responses
            39 views
            0 reactions
            Last Post SEQadmin2  
            Started by SEQadmin2, 06-09-2026, 11:58 AM
            0 responses
            102 views
            0 reactions
            Last Post SEQadmin2  
            Started by SEQadmin2, 06-05-2026, 10:09 AM
            0 responses
            123 views
            0 reactions
            Last Post SEQadmin2  
            Started by SEQadmin2, 06-04-2026, 08:59 AM
            0 responses
            114 views
            0 reactions
            Last Post SEQadmin2  
            Working...