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  • gringer
    replied
    You wouldn't happen to be trying to do a metagenomic analysis, would you? If you have a different sample population for each condition, then that might explain the differences that you're seeing.

    Leave a comment:


  • gringer
    replied
    No. You're expecting points looking like a triangle (or diamond) shaped wedge elongated along the Y axis, centered on the Y axis. I've attached an example based on DESeq results. The DESeq2 plot should look similar, but narrows down to a point for low expression values. If you're not getting that (and all other steps check out), then it suggests that your experimental conditions aren't appropriately controlled.
    Attached Files
    Last edited by gringer; 06-26-2014, 09:28 PM.

    Leave a comment:


  • alyamahmoud
    replied
    I tried

    Code:
    >cdsFilt = estimateDispersions(cdsFilt, method  = "blind", sharingMode="fit-only)
    and the MA plot is attached, does it look any better ?
    Attached Files

    Leave a comment:


  • alyamahmoud
    replied
    Code:
    > head (counts_table)
                  water_1 water_2 ph5_1 ph5_2 ph9_1 ph9_2 anaerobic_1 anaerobic_2
    FN649414.4579     500     243   133   647   141   114         222         106
    FN649414.7957      23      20    10    91    12    13          13           6
    FN649414.7767     135      50    55   321    52    43          96          53
    p948.168            5       0     0     0     0     0          66          28
    
                  aerobic_1 aerobic_2
    FN649414.4579        50       113
    FN649414.7957        16        34
    FN649414.7767        33       101
    p948.168              0         0

    Leave a comment:


  • gringer
    replied
    Can you show the first few lines of results (and/or your count table)? The rest of what you've got looks fine.

    Leave a comment:


  • alyamahmoud
    replied
    Am I formatting the conditions file wrongly:

    This is my exp_design file:

    Code:
    >exp_design
                condition
    water_1         water
    water_2         water
    ph5_1             ph5
    ph5_2             ph5
    ph9_1             ph9
    ph9_2             ph9
    anaerobic_1 anaerobic
    anaerobic_2 anaerobic
    aerobic_1     aerobic
    aerobic_2     aerobic
    and these are the commands in DESeq and DEseq2, respectively.
    Code:
    conds = exp_design$condition
    cds =  newCountDataSet(counts_table, conds)
    Code:
    ddsFullCountTable <- DESeqDataSetFromMatrix(countData = counts_table, colData = exp_design, design = ~condition)

    Leave a comment:


  • alyamahmoud
    replied
    I tried DESeq2 and the results are not very different. The p values distribution and the MA plot using DESeq2 are attached.


    Here are the formats for the counts table and design:

    Code:
    >conditions(cds)
        water_1     water_2       ph5_1       ph5_2       ph9_1       ph9_2 
          water       water         ph5         ph5         ph9         ph9 
    anaerobic_1 anaerobic_2   aerobic_1   aerobic_2 
      anaerobic   anaerobic     aerobic     aerobic 
    Levels: aerobic anaerobic ph5 ph9 water
    Code:
    > colnames(counts_table)
     [1] "water_1"     "water_2"     "ph5_1"       "ph5_2"       "ph9_1"      
     [6] "ph9_2"       "anaerobic_1" "anaerobic_2" "aerobic_1"   "aerobic_2"
    Code:
    >conds
     [1] water     water     ph5       ph5       ph9       ph9       anaerobic
     [8] anaerobic aerobic   aerobic  
    Levels: aerobic anaerobic ph5 ph9 water
    Attached Files

    Leave a comment:


  • gringer
    replied
    I am not subsampling, and I am using the raw counts as input to DESeq and not DESeq2.
    Oh, okay. Do you get the same results when using DESeq2?

    The only reason I can think of off the top of my head why increased expression in both groups would result in increased log2 fold change is if one of the groups had 0 expression for all genes. Can you show the first few lines of your results, i.e. "head(res)"? I think DESeq (v1, not v2) should report estimated/normalised expression for each group in that result.

    If that is the problem, then you may have chosen your condition names incorrectly in the nbinomTest command:
    Code:
    > conditions(cds)
    [1] "water" "aerobic" # should be something like this
    Or alternatively, the count table or condition list could be formatted incorrectly:
    Code:
    > colnames(counts_table)
    [1] "water_r1" "water_r2" "aerobic_r1" "aerobic_r2" # something like this
    > dim(counts_table)
    [1] 30215 4 # should be something like this
    > conds
    [1] "water" "water" "aerobic" "aerobic"  # should be something like this
    But please try DESeq2. It will complain a bit louder when you do things wrong, which will hopefully give you more insights into what went wrong.

    Leave a comment:


  • alyamahmoud
    replied
    Hi gringer

    Thanks for the prompt reply.

    I am not subsampling, and I am using the raw counts as input to DESeq and not DESeq2.

    I ran the following to generate the MA plot:

    Code:
    cds =  newCountDataSet(counts_table, conds)
    cds <- estimateSizeFactors(cds)
    cds <- estimateDispersions(cds)
    res = nbinomTest(cds, "water", "aerobic") # one of the conditions vs control
    plotMA(res)

    Leave a comment:


  • gringer
    replied
    Based on the MA plot, I'm guessing you're using DESeq2, rather than DESeq, which is good.

    However, your MA plot looks crazy. It should be distributed around the y axis (0 log2 fold change).

    I've got no idea what would do that, but it certainly indicates something screwy is going on. Are you sub-sampling genes prior to running them through DESeq2? Are you using normalised counts as input, instead of raw counts? What command did you run to produce this MA plot?

    Leave a comment:


  • non-typical p values distribution running DESeq

    Hi All

    I have 2 reps*5 conditions (4 + control). I ran followed the nature protocol to come to differentially expressed genes between each of the 4 conditions relative to the control.

    The distribution of p values looks is attached for one of the conditions as well as the corresponding MA plot. I am getting too many differentially expressed genes (below) and the p values distribution doesn't look like expected.

    Would you please advise what I could be missing ? or is such a distribution of p values expected in some cases and why ?

    Thank you very much
    Alyaa


    Code:
    > table (res$padj < 0.1)
    FALSE  TRUE 
     3245  3578 
    
    > table (res$padj < 0.01)
    FALSE  TRUE 
     4813  2010 
    
    > table (res$padj < 0.05)
    FALSE  TRUE 
     3862  2961
    Attached Files

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