text: user4307: I am analyzing some RNA sequencing data from a collaborator where the effect of some ligands on the transcriptome is being looked at. I am using DESeq2 for my analysis. I am looking to compare each of the ligand treatments vs the DMSO. I have attached the MDS plot with this post. There seems to be a lot of variation between the 3 replicates across all the treatments. Based on this plot, is it worth it to use this data to run a differential expression?
  It is also at this link below.
  https://imgur.com/a/HGHepl7
  
  user-1: You definitely see separation by treatment in PC1 and PC2, so that is an indication that the treatments are doing something. DMSO and TCDD cluster close, so these are probably similar. Still, you see a variation along PC1 and it seems that there is within-group separation in all groups that could indicate a batch effect. Check whether the samples per group that are more leftish have been processed as one batch, if so one could include that information into the design, see DESeq2 vignette on batch effects. Alternatively, check tools such as RUVseq or SVA to detect unknown batch effects to correct for this within-grup separation.
  
original_text: 4307: <p>I am analyzing some RNA sequencing data from a collaborator where the effect of some ligands on the transcriptome is being looked at. I am using DESeq2 for my analysis. I am looking to compare each of the ligand treatments vs the DMSO. I have attached the MDS plot with this post. There seems to be a lot of variation between the 3 replicates across all the treatments. Based on this plot, is it worth it to use this data to run a differential expression?</p>
  <p>It is also at this link below.
  <a href="https://imgur.com/a/HGHepl7" rel="nofollow noreferrer">https://imgur.com/a/HGHepl7</a></p>
   -1: <p>You definitely see separation by treatment in PC1 and PC2, so that is an indication that the treatments are doing something. DMSO and TCDD cluster close, so these are probably similar. Still, you see a variation along PC1 and it seems that there is within-group separation in all groups that could indicate a batch effect. Check whether the samples per group that are more leftish have been processed as one batch, if so one could include that information into the design, see DESeq2 vignette on batch effects. Alternatively, check tools such as RUVseq or SVA to detect unknown batch effects to correct for this within-grup separation.</p>
  