Answers.Body: <p>See here for a Differential Expression guide which discusses carrying out differential expression using DESeq2:</p>
  <p><a href="https://www.bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html" rel="nofollow noreferrer">https://www.bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html</a></p>
  <p>It starts with a standard &quot;quick start&quot; workflow, and goes into quite a lot of detail explaining all the different things that can be done:</p>
  <pre><code>dds &lt;- DESeqDataSetFromMatrix(countData = cts,
                                colData = coldata,
                                design= ~ batch + condition)
  dds &lt;- DESeq(dds)
  resultsNames(dds) # lists the coefficients
  res &lt;- results(dds, name=&quot;condition_trt_vs_untrt&quot;)
  </code></pre>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-07-14T04:40:14.650Z
Answers.LastActivityDate: 2022-07-14T04:40:14.650Z
Answers.UserDisplayName: gringer
Answers.Body: <blockquote>
  <p>I'm thinking first normalize using TMM then compare the mean of the normalized values for treated vs. untreated?</p>
  </blockquote>
  <p>That is a bit too simple. You need to analyse your experiment as a paired comparison. You need to combine the treated and untreated count matrices into one and form factors for individual and treatment:</p>
  <pre><code>Counts &lt;- cbind(Counts.treated, Counts.untreated)
  Individual &lt;- gl(5,1,length=10)
  Treatment &lt;- gl(2,5,length=10,labels=c(&quot;Treated&quot;,&quot;Untreated&quot;))
  Treatment &lt;- relevel(Treatment, ref=&quot;Untreated&quot;)
  design &lt;- model.matrix(~Individual+Treatment)
  </code></pre>
  <p>Then follow a standard workflow:
  <a href="https://bioconductor.org/packages/release/workflows/vignettes/RnaSeqGeneEdgeRQL/inst/doc/edgeRQL.html" rel="nofollow noreferrer">https://bioconductor.org/packages/release/workflows/vignettes/RnaSeqGeneEdgeRQL/inst/doc/edgeRQL.html</a></p>
  <pre><code>library(edgeR)
  y &lt;- DGEList(counts=Counts)
  y &lt;- calcNormFactors(y)
  y &lt;- estimateDisp(y,design)
  fit &lt;- glmQLFit(y,design)
  test &lt;- glmQLFTest(fit)
  topTags(test)
  </code></pre>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-07-14T07:55:07.853Z
Answers.LastActivityDate: 2022-07-14T08:49:07.320Z
Answers.LastEditDate: 2022-07-14T08:49:07.320Z
Answers.UserDisplayName: Gordon Smyth
Body: <p>I have two matrices, one for individuals before treatment and one for the same individuals after treatment. Both matrices are raw read counts of RNA expression.</p>
  <pre><code>        Treated_1  Treated_2  Treated_3  Treated_4  Treated_5
  RNA_1   105        283        64         155        51
  RNA_2   359        27         47         348        84
  RNA_3   99         10         89         345        77
  RNA_4   48         100        77         74         83
  
          Untreated_1  Untreated_2  Untreated_3  Untreated_4  Untreated_5
  RNA_1   100          200          50           130          33
  RNA_2   200          10           30           300          75
  RNA_3   90           10           60           320          60
  RNA_4   66           50           33           60           55
  
  </code></pre>
  <p>How can I tell which RNA's are significantly differentially expressed in R using <code>edgeR</code> or <code>DESeq2</code>? I'm thinking first normalize using TMM then compare the mean of the normalized values for treated vs. untreated?</p>
  
Tags: <r><deseq2><differential-expression><rna><edger>
Title: Basic RNA Differential Expression in R
ThreadText: # Basic RNA Differential Expression in R
  
  (Asked by: username_0 on 2022-07-14)
  
  I have two matrices, one for individuals before treatment and one for the same individuals after treatment. Both matrices are raw read counts of RNA expression.
  
  ```
          Treated_1  Treated_2  Treated_3  Treated_4  Treated_5
  RNA_1   105        283        64         155        51
  RNA_2   359        27         47         348        84
  RNA_3   99         10         89         345        77
  RNA_4   48         100        77         74         83
  
          Untreated_1  Untreated_2  Untreated_3  Untreated_4  Untreated_5
  RNA_1   100          200          50           130          33
  RNA_2   200          10           30           300          75
  RNA_3   90           10           60           320          60
  RNA_4   66           50           33           60           55
  
  ```
  
  How can I tell which RNA's are significantly differentially expressed in R using `edgeR` or `DESeq2`? I'm thinking first normalize using TMM then compare the mean of the normalized values for treated vs. untreated?
  
  **Tags:** r, deseq2, differential-expression, rna, edger
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2022-07-14. Score: 3
  
  See here for a Differential Expression guide which discusses carrying out differential expression using DESeq2:
  
  <https://www.bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html>
  
  It starts with a standard "quick start" workflow, and goes into quite a lot of detail explaining all the different things that can be done:
  
  ```
  dds <- DESeqDataSetFromMatrix(countData = cts,
                                colData = coldata,
                                design= ~ batch + condition)
  dds <- DESeq(dds)
  resultsNames(dds) # lists the coefficients
  res <- results(dds, name="condition_trt_vs_untrt")
  
  ```
  
  
  ---
  
  ### Answer by username_2 on 2022-07-14. Score: 1
  
  > I'm thinking first normalize using TMM then compare the mean of the normalized values for treated vs. untreated?
  
  That is a bit too simple. You need to analyse your experiment as a paired comparison. You need to combine the treated and untreated count matrices into one and form factors for individual and treatment:
  
  ```
  Counts <- cbind(Counts.treated, Counts.untreated)
  Individual <- gl(5,1,length=10)
  Treatment <- gl(2,5,length=10,labels=c("Treated","Untreated"))
  Treatment <- relevel(Treatment, ref="Untreated")
  design <- model.matrix(~Individual+Treatment)
  
  ```
  
  Then follow a standard workflow:
  <https://bioconductor.org/packages/release/workflows/vignettes/RnaSeqGeneEdgeRQL/inst/doc/edgeRQL.html>
  
  ```
  library(edgeR)
  y <- DGEList(counts=Counts)
  y <- calcNormFactors(y)
  y <- estimateDisp(y,design)
  fit <- glmQLFit(y,design)
  test <- glmQLFTest(fit)
  topTags(test)
  
  ```
  
  
  ---
  
  