Answers.Body: <p>After running the test for differential transcript usage you get a list with gene- and transcript-level p-values that you can use to filter which differences are significant. The gene-level p-value tells you if there is differential usage of transcripts within a gene and the transcript-level p-value tells you wich transcripts actually are changing.  Therefor you will want to select significant genes for further investigation. </p>
  
  <p>The plots are mainly used to visualize top hits as detailed in the vignette.</p>
  
  <pre><code>res &lt;- results(d)
  # The data is ordered by p-value
  res &lt;- res[order(res$pvalue, decreasing = FALSE), ]
  # The top hit is selected
  top_gene_id &lt;- res$gene_id[1]
  plotProportions(d, gene_id = top_gene_id, group_variable = "group")
  </code></pre>
  
  <p>However you should consider running the two-stage test that is described on page 16 of the vignette you are using to reduce the number of false positive hits. From this you will get another list of adjusted p-values that can be used as above.</p>
  
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Answers.CreationDate: 2019-08-19T15:39:15.357Z
Answers.Text: I'm very gratefull for your input. But what if I have more than one `group_variable` how do I know why p-values are related to that `group_variable`?
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Answers.Text: When you run the test (d <- dmTest(d, coef = "groupKD", verbose = 1)) you are specifying the contrast you are investigating (in this case "groupKD"). If you for example have two variables that you want to look at independently you will have to make two DRIMSeq data sets and run dMTest two times. It would be best if you can specify your model.matrix in the question so I can give you more specific answers.
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Answers.Text: Let's say I have a design matrix which is `~ treatment + celltype` and my null model is `~1` . After running the pipeline with this design, how do I get a list of the genes whose transcripts are differentially used for the treatment or for the celltype ? I don't want to use two different models to get the DTU for each variable as the above additive model gave better gene precision estimates than models with individual variable.
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Answers.Text: If you have two columns in your design matrix you can use the `coef` approach. for example `dmTest(d, coef = "treatmentB, verbose = 1)` will give you the p values for comparing treatment B vs A (in this example A is the reference level, if you want to change that you can refactor the columns in the column data). But please note that in this case the null model becomes `~ cellltype` which is what you need to isolate the treatment from the celltype effect.
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Body: <p>After running a DRIMSeq pipeline and obtaining the genes that are differently used between the null and full models, how do I select the genes that are differently used in the different conditions? </p>
  
  <p>According to <a href="https://bioconductor.org/packages/release/bioc/vignettes/DRIMSeq/inst/doc/DRIMSeq.pdf" rel="nofollow noreferrer">the vignette</a>, I would have to use the <code>plotProportions</code> function and say in the <code>group_variable</code> the variable of my interest. What if I want a list of the genes that are differently used specifically in the variables of my interest? Checking every gene manually sounds exhausting. Help would be much appreciated.</p>
  
Tags: <rna-seq><bioconductor>
Title: After running a DRIMSeq pipeline, how do I know which genes are upregulated in the different conditions?
ThreadText: # After running a DRIMSeq pipeline, how do I know which genes are upregulated in the different conditions?
  
  (Asked by: username_0 on 2019-08-19)
  
  After running a DRIMSeq pipeline and obtaining the genes that are differently used between the null and full models, how do I select the genes that are differently used in the different conditions?
  
  According to [the vignette](https://bioconductor.org/packages/release/bioc/vignettes/DRIMSeq/inst/doc/DRIMSeq.pdf), I would have to use the `plotProportions` function and say in the `group_variable` the variable of my interest. What if I want a list of the genes that are differently used specifically in the variables of my interest? Checking every gene manually sounds exhausting. Help would be much appreciated.
  
  **Tags:** rna-seq, bioconductor
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-08-19. Score: 0 | Accepted answer
  
  After running the test for differential transcript usage you get a list with gene- and transcript-level p-values that you can use to filter which differences are significant. The gene-level p-value tells you if there is differential usage of transcripts within a gene and the transcript-level p-value tells you wich transcripts actually are changing. Therefor you will want to select significant genes for further investigation.
  
  The plots are mainly used to visualize top hits as detailed in the vignette.
  
  ```
  res <- results(d)
  # The data is ordered by p-value
  res <- res[order(res$pvalue, decreasing = FALSE), ]
  # The top hit is selected
  top_gene_id <- res$gene_id[1]
  plotProportions(d, gene_id = top_gene_id, group_variable = "group")
  
  ```
  
  However you should consider running the two-stage test that is described on page 16 of the vignette you are using to reduce the number of false positive hits. From this you will get another list of adjusted p-values that can be used as above.
  
  > **Comments:**
  > **username_0**: I'm very gratefull for your input. But what if I have more than one `group_variable` how do I know why p-values are related to that `group_variable`?  
  > **username_1**: When you run the test (d <- dmTest(d, coef = "groupKD", verbose = 1)) you are specifying the contrast you are investigating (in this case "groupKD"). If you for example have two variables that you want to look at independently you will have to make two DRIMSeq data sets and run dMTest two times. It would be best if you can specify your model.matrix in the question so I can give you more specific answers.  
  > **username_0**: Let's say I have a design matrix which is `~ treatment + celltype` and my null model is `~1` . After running the pipeline with this design, how do I get a list of the genes whose transcripts are differentially used for the treatment or for the celltype ? I don't want to use two different models to get the DTU for each variable as the above additive model gave better gene precision estimates than models with individual variable.  
  > **username_1**: If you have two columns in your design matrix you can use the `coef` approach. for example `dmTest(d, coef = "treatmentB, verbose = 1)` will give you the p values for comparing treatment B vs A (in this example A is the reference level, if you want to change that you can refactor the columns in the column data). But please note that in this case the null model becomes `~ cellltype` which is what you need to isolate the treatment from the celltype effect.  
  
  ---
  
  