text: user14504: We have an RNA seq taken from a the same tissue in two different developmental stages. We have one gene, with the number of reads from stage A and stage B, and we want to see which genes are differentially expressed.
  After performing RNA-seq, I am left with a CSV file containing a list of genes and the amount of reads in two different cell types. We have one list of genes, with the number of reads in Stage A in one column and the number of reads in Stage B in another column. We are looking to see what types of genes are differentially expressed in the two stages.
  I have used DESeq2, but I'm not sure if this is the best/correct to analyze what I am looking for.
  What is the best package to analyze the data for significant differential gene expression?
  Thanks
  
  user2388: DESeq2 works for that.  Unless you literally have only two samples.
  There is no statistically sound way of analyzing DE genes with literally only two samples.  This is a bad experimental design, suitable for only the roughest of exploratory purposes.  Only the most obvious differences (that you probably knew already) are likely to be borne out in a legitimate experiment.
  
original_text: 14504: <p>We have an RNA seq taken from a the same tissue in two different developmental stages. We have one gene, with the number of reads from stage A and stage B, and we want to see which genes are differentially expressed.</p>
  <p>After performing RNA-seq, I am left with a CSV file containing a list of genes and the amount of reads in two different cell types. We have one list of genes, with the number of reads in Stage A in one column and the number of reads in Stage B in another column. We are looking to see what types of genes are differentially expressed in the two stages.</p>
  <p>I have used DESeq2, but I'm not sure if this is the best/correct to analyze what I am looking for.</p>
  <p>What is the best package to analyze the data for significant differential gene expression?</p>
  <p>Thanks</p>
   2388: <p>DESeq2 works for that.  Unless you literally have only two samples.</p>
  <p>There is <strong>no</strong> statistically sound way of analyzing DE genes with literally only two samples.  This is a bad experimental design, suitable for only the roughest of exploratory purposes.  Only the most obvious differences (that you probably knew already) are likely to be borne out in a legitimate experiment.</p>
  