text: user1369: I have RNA-Seq data for LUNG cancer. 370 tumor and 50 Normal. For differential analysis initially I did some filtering and kept approx. 19k genes for further analysis. I used edgeR.
  With a FC > or < 2 and FDR < 0.05
  tr <- glmTreat(fit, contrast=contrast.matrix, lfc = log2(2))
  I got approx 3000 differentially expressed genes. 
  Among the 3000 differentially expressed genes I'm interested in a gene OIT3.
  GeneSymbol  logFC    unshrunk.logFC   logCPM          PValue      FDR
    OIT3  2.156487448  2.171599487    -0.437234244    0.000701037 0.005304312
  
  I see the above gene OIT3 is Upregulated. Why I see logCPM with negative value?
  And I plotted the logCPM expression of the above gene and did a t-test which gave pvalue 0.1 which means not significant.
  
  1) I see the gene is differentially expressed with Differential analysis using edgeR. Why I see logCPM with negative value?
  2) With t-test why it is not significant?
  
  user77: 
  The $log(CPM)$ of any low-moderately expressed gene will be negative. There is nothing unexpected there.
  Your statistics are inappropriate for a variety of reasons. Firstly, a CPM is not a robust value that's comparable between samples (this is why CPMs aren't used for statistics). edgeR performs more appropriate normalization and incorporates that into its model. Secondly, though you have a fair number of replicates, edgeR is pooling information from all of the genes to better estimate the variance.
  
  I encourage you to read the edgeR paper and likely that of Limma too.
  
  user174: There is no reason your t-test should reproduce edgeR. In fact, edgeR exists because t-test is inappropriate.
  edgeR does the tests by pooling information from all genes, because with the low number of replicates your t-test doesn't give sufficient statistical power.
  What you need to do is: check visually your gene and make a decision yourself based on graphical results and edgeR results if your gene shows biologically significant.
  
original_text: 1369: <p>I have RNA-Seq data for LUNG cancer. 370 tumor and 50 Normal. For differential analysis initially I did some filtering and kept approx. 19k genes for further analysis. I used edgeR.</p>
  
  <p>With a FC > or &lt; 2 and FDR &lt; 0.05<br>
  <code>tr &lt;- glmTreat(fit, contrast=contrast.matrix, lfc = log2(2))</code><br>
  I got approx 3000 differentially expressed genes. </p>
  
  <p>Among the 3000 differentially expressed genes I'm interested in a gene <code>OIT3</code>.</p>
  
  <pre><code>GeneSymbol  logFC    unshrunk.logFC   logCPM          PValue      FDR
    OIT3  2.156487448  2.171599487    -0.437234244    0.000701037 0.005304312
  </code></pre>
  
  <p>I see the above gene <code>OIT3</code> is Upregulated. Why I see logCPM with negative value?</p>
  
  <p>And I plotted the logCPM expression of the above gene and did a t-test which gave pvalue 0.1 which means not significant.</p>
  
  <p><a href="https://i.stack.imgur.com/RKxhR.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/RKxhR.png" alt="Boxplot showing OIT3 logCPM expression"></a></p>
  
  <p>1) I see the gene is differentially expressed with Differential analysis using edgeR. Why I see logCPM with negative value?</p>
  
  <p>2) With t-test why it is not significant?</p>
   77: <ol>
  <li>The <span class="math-container">$log(CPM)$</span> of any low-moderately expressed gene will be negative. There is nothing unexpected there.</li>
  <li>Your statistics are inappropriate for a variety of reasons. Firstly, a CPM is not a robust value that's comparable between samples (this is why CPMs aren't used for statistics). <code>edgeR</code> performs more appropriate normalization and incorporates that into its model. Secondly, though you have a fair number of replicates, edgeR is pooling information from all of the genes to better estimate the variance.</li>
  </ol>
  
  <p>I encourage you to <a href="https://www.ncbi.nlm.nih.gov/pubmed/17881408" rel="nofollow noreferrer">read the edgeR paper</a> and likely that of Limma too.</p>
   174: <p>There is no reason your t-test should reproduce edgeR. In fact, edgeR exists because t-test is inappropriate.</p>
  
  <p>edgeR does the tests by pooling information from all genes, because with the low number of replicates your t-test doesn't give sufficient statistical power.</p>
  
  <p>What you need to do is: <em>check visually your gene and make a decision yourself based on graphical results and edgeR results if your gene shows biologically significant</em>.</p>
  