Seqanswers Leaderboard Ad

Collapse

Announcement

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

  • #16
    Ah, yes, that's correct. I always forget that it uses a "." rather than ":".

    Comment


    • #17
      Sorry to be so picky, but I still have some difficulties understanding it, even if it might not seems this way.

      What would be the difference between the two models:
      Code:
      ~condition + stimulation
      and
      Code:
      ~condition + stimulation + condition:stimulation
      in term of the question answered by the model?
      I know what the user guide says about interactions, but to be honest, I'm not sure what it really means.
      Is there a way to simplify it better?

      my try would be this:
      the first model try to identify the differences in the two groups of stimulation, buy accounting for the differences in the condition groups (this is reformulating the user guide). Does it means, that I sort of trying to discard all changes happening due to conditions and concentrate only on the effect causes by the stimulation?

      the second model adds an interaction. according to the user guide, it means that i am trying to calculate the differences of one condition (here my term condition) based on the second condition (here my term stimulation)?
      Does this means here that I'm trying to see the differences happening in my samples between the WT and KO based on the stimulation changes?

      This doesn't sound very simplified, but maybe someone can do it better

      Comment


      • #18
        No worries about being picky, this is important to get correct

        This boils down to, "so what's an interaction anyway?". Let's take your experiment as the example and consider the design:
        Code:
        ~condition + stimulation
        Let's just consider one example gene, called foo. Suppose in the WT unstimulated case, foo has a value of 1. Upon stimulation, that increases to 2. If don't stimulate but instead knock out some other gene, the value instead goes to 3. That covers 3 of the 4 groups. What this model says is that in the case of both stimulating AND knocking out some other gene, we expect the resulting value to be 2*3=6 (the fold changes multiply).

        But suppose the effect due to knocking out some gene is dependent upon stimulation (e.g., the stimulation activates a pathway that we've partly inactivated due to the knockout). We then would have what's referred to as an interaction between the stimulation and the condition. So we would no longer expect the knockout and stimulated group to have a value of 6, but something else entirely. The resulting interaction coefficient tells you the fold change from what you would expect if the knockout and stimulation effect don't interfere with each other (or have a synergistic effect). It could well be the case that instead of this group having a value of 6 we instead see no change from the baseline WT unstimulated group, so the value is 1, meaning that the fit coefficient is 1/6. Interactions always describe the additional change on top of what would be expected if your various conditions acted independently.

        Hope that helps.

        Comment


        • #19
          Yes, it helps a lot. I think I start understanding it.

          I have another question about the colData. Does the order of the samples here must be the same as in the count table.
          I am asking because I have done the analysis mentioned above and found no DE genes. When I than changed the colData file to that:

          Code:
          name	condition	stimulation
          Vav_KO_1	KO	no
          Vav_KO_2	KO	no
          Vav_KO_5	KO	no
          Vav_KO_2_C	KO	yes
          Vav_KO_4_C	KO	yes
          Vav_KO_5_C	KO	yes
          Vav_WT_1	wildtype	no
          Vav_WT_2	wildtype	no
          Vav_WT_4	wildtype	no
          Vav_WT_4_C	wildtype	yes
          Vav_WT_1_C	wildtype	yes
          Vav_WT_2_C	wildtype	yes
          I suddenly get 106 genes with an adjp<=0.1

          the sample names was not changed and is identical to the column names of the count table.
          Now I also get a different last comparison possiblity:
          Code:
          > resultsNames(dds)
          [1] "Intercept"                        "condition_wildtype_vs_KO"        
          [3] "stimulation_yes_vs_no"            "conditionwildtype.stimulation[B]yes[/B]"
          compared to "conditionwildtype.stimulationnone" from before.
          Is there an explanation for this kind of changes?

          thanks in advance

          Comment


          • #20
            That's...weird. I don't have a good explanation for that. Well, actually I don't know why you got "Curdlan" as part of a coefficient name before given that it wasn't in the original colData. The coefficient names at least make sense this time. Given that, my only guess is that something went wonky previously.

            Comment


            • #21
              Originally posted by dpryan View Post
              No worries about being picky, this is important to get correct

              But suppose the effect due to knocking out some gene is dependent upon stimulation (e.g., the stimulation activates a pathway that we've partly inactivated due to the knockout). We then would have what's referred to as an interaction between the stimulation and the condition. So we would no longer expect the knockout and stimulated group to have a value of 6, but something else entirely. The resulting interaction coefficient tells you the fold change from what you would expect if the knockout and stimulation effect don't interfere with each other (or have a synergistic effect). It could well be the case that instead of this group having a value of 6 we instead see no change from the baseline WT unstimulated group, so the value is 1, meaning that the fit coefficient is 1/6. Interactions always describe the additional change on top of what would be expected if your various conditions acted independently.
              Hi Devon,

              you mentioned, being picky can be a virtue, so I'm being picky again.

              I was wondering about my first question for the comparison between the knock-out and the stimulation. I wanted to find the deregulated genes independently from the stimulation effect. What I did was a Wald test between the contrasts KO and wildtype. But with this comparison, I have taken both the stimulated and the non-stimulated samples in one go.
              Does it make sense to do it this way, or is it better to take only the not stimulated samples.

              I think that for the genes affected by the stimulation, the not-stimulated vs. stimulated samples might cancel each other, but it is a risk.
              Taking all samples though gives me a higher statistical power (six instead of only three samples).

              But this might also give me genes changing due to the combination of knock-out and stimulation, which I don't want to have in the first question.

              What do you think is a better way of doing this analysis?


              thanks
              Assa

              Comment


              • #22
                Yes, you'll get slightly better results taking everything at once and using the contrast like you did. The reason for this is simply due to the additional samples being used in dispersion estimation. If you include the interaction term in the design then you won't have that affecting things.

                Comment


                • #23
                  Hi,
                  Sorry for the resurrection of such old dialogues but I am quite confused.
                  I was thinking of doing a time-course analysis (as Assa did) having only 1 condition , 4 time points and 3 replicates for each time point. But what Ryan says here "not to use replicates as a factor" is contradictory to the answer that Michael Love gives in this link 4 months ago:


                  Should I finally consider replicates as a factor in my model or better to forget them?

                  Thanks for your answer in advance,
                  Eva
                  Last edited by EVELE; 02-18-2019, 03:34 PM. Reason: First time I sent only the first sentence of my message

                  Comment

                  Latest Articles

                  Collapse

                  • seqadmin
                    Strategies for Sequencing Challenging Samples
                    by seqadmin


                    Despite advancements in sequencing platforms and related sample preparation technologies, certain sample types continue to present significant challenges that can compromise sequencing results. Pedro Echave, Senior Manager of the Global Business Segment at Revvity, explained that the success of a sequencing experiment ultimately depends on the amount and integrity of the nucleic acid template (RNA or DNA) obtained from a sample. “The better the quality of the nucleic acid isolated...
                    03-22-2024, 06:39 AM
                  • seqadmin
                    Techniques and Challenges in Conservation Genomics
                    by seqadmin



                    The field of conservation genomics centers on applying genomics technologies in support of conservation efforts and the preservation of biodiversity. This article features interviews with two researchers who showcase their innovative work and highlight the current state and future of conservation genomics.

                    Avian Conservation
                    Matthew DeSaix, a recent doctoral graduate from Kristen Ruegg’s lab at The University of Colorado, shared that most of his research...
                    03-08-2024, 10:41 AM

                  ad_right_rmr

                  Collapse

                  News

                  Collapse

                  Topics Statistics Last Post
                  Started by seqadmin, Yesterday, 06:37 PM
                  0 responses
                  10 views
                  0 likes
                  Last Post seqadmin  
                  Started by seqadmin, Yesterday, 06:07 PM
                  0 responses
                  9 views
                  0 likes
                  Last Post seqadmin  
                  Started by seqadmin, 03-22-2024, 10:03 AM
                  0 responses
                  49 views
                  0 likes
                  Last Post seqadmin  
                  Started by seqadmin, 03-21-2024, 07:32 AM
                  0 responses
                  67 views
                  0 likes
                  Last Post seqadmin  
                  Working...
                  X