text: user1771: I am reading Smyth et al. (ref. 1). I want to run differential expression analysis on a bulk RNA-Seq dataset in which each group is composed by 2 samples. In the paper previously cited it is written that:
  
  Genes must be expressed in at least one group (or in at least three samples across the entire experiment, where three was chosen as this is the smallest group size) to be kept for downstream analysis.
  
  Is it possible to use limma DE analysis also with groups composed by only 2 samples? NB. It is possible that in this particular dataset the smallest group size is 3.
  If not, which alternative should I use?
  Update
  I have 17 samples. My idea is to test one group vs the rest of groups (all together). So at least the reference group would be composed by > 10 samples. In this case, what can be a feasible analysis for DE?
  
  Smyth, G. K., Law, C. W., Alhamdoosh, M., Su, S. & Ritchie, M. E. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. F1000Research 5, 1408 (2016).
  
  
  user48: The problem with most of the methods is that use the gene's variance for each group, which can't be calculated (reliable) when the sample is <= 2. Also, statistically, it would have extremely low power, so the conclusions couldn't be trusted much. 
  You could also calculate the "raw" fold change by yourself (ie without the variance estimation and adjusting). If you had one more sample per group you could use DESeq2 which was thought for such cases. But from the paper:
  
  However, if there are two or fewer replicates for a condition, these samples do not contribute to outlier detection, as there are insufficient replicates to determine outlier status.
  
  Which makes me unsure if it will work well for just two samples as it won't help to detect outliers genes. 
  
  user581: While I share Llopis' concern about estimating variance from 2 samples, the statement you quoted is about avoiding false positives from genes that are only expressed in a few samples. It's fairly common to exclude genes that expressed in fewer samples than the smallest group even if the number of samples per group is much higher than 3.
  
original_text: 1771: <p>I am reading Smyth et al. (ref. 1). I want to run differential expression analysis on a bulk RNA-Seq dataset in which each group is composed by 2 samples. In the paper previously cited it is written that:</p>
  
  <blockquote>
    <p>Genes must be expressed in at least one group (or in at least three samples across the entire experiment, where <strong>three was chosen as this is the smallest group size</strong>) to be kept for downstream analysis.</p>
  </blockquote>
  
  <p>Is it possible to use limma DE analysis also with groups composed by only 2 samples? NB. It is possible that in this particular dataset the smallest group size is 3.</p>
  
  <p>If not, which alternative should I use?</p>
  
  <h3>Update</h3>
  
  <p>I have 17 samples. My idea is to test one group vs the rest of groups (all together). So at least the reference group would be composed by > 10 samples. In this case, what can be a feasible analysis for DE?</p>
  
  <ol>
  <li>Smyth, G. K., Law, C. W., Alhamdoosh, M., Su, S. &amp; Ritchie, M. E. <a href="https://f1000research.com/articles/5-1408/v2" rel="noreferrer">RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR</a>. F1000Research 5, 1408 (2016).</li>
  </ol>
   48: <p>The problem with most of the methods is that use the gene's variance for each group, which can't be calculated (reliable) when the sample is &lt;= 2. Also, statistically, it would have extremely low power, so the conclusions couldn't be trusted much. </p>
  
  <p>You could also calculate the "raw" fold change by yourself (ie without the variance estimation and adjusting). If you had one more sample per group you could use <a href="https://bioconductor.org/packages/DESeq2" rel="nofollow noreferrer">DESeq2</a> which was <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8" rel="nofollow noreferrer">thought</a> for such cases. But from the paper:</p>
  
  <blockquote>
    <p>However, if there are two or fewer replicates for a condition, these samples do not contribute to outlier detection, as there are insufficient replicates to determine outlier status.</p>
  </blockquote>
  
  <p>Which makes me unsure if it will work well for just two samples as it won't help to detect outliers genes. </p>
   581: <p>While I share Llopis' concern about estimating variance from 2 samples, the statement you quoted is about avoiding false positives from genes that are only expressed in a few samples. It's fairly common to exclude genes that expressed in fewer samples than the smallest group even if the number of samples per group is much higher than 3.</p>
  