Answers.Body: <p>I would be very hesitant to blindly exclude those samples based on the clustering. Check to see if the clusters actually denote some sort of batch effect, since it's not like all of the TCGA datasets were processed at the same place or time. Check for clinical covariates too. Also, do a PCA and check the genes with high loading on the relevant PC. If it appears that the genes are indicators for cancerous state, then it's likely the samples are just mislabeled and you can reasonably exclude them. Otherwise keep them in so your results better represent the underlying biological variability.</p>
  
  <p>BTW, your heatmap and MDS plots are what I'd expect from a cancer dataset. You occasionally see a tumour with the normal samples and vice versa, since the problematic genes aren't always in the top 10% and there is likely some heterogeneity in the cancer samples.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-06-20T13:28:51.657Z
Answers.Text: How do I check the batch effect of the samples? can I check that with edgeR or Deseq2?
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Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-06-20T13:29:39.960Z
Answers.Text: Go through the meta information and see if there are other things (sequencing data, processing center, etc.) that correlate with the clusters.
Answers.UserDisplayName: Devon Ryan
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Answers.CreationDate: 2018-06-20T13:16:28.423Z
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Answers.LastEditDate: 2018-06-20T13:46:10.880Z
Answers.UserDisplayName: Devon Ryan
Body: <p>I'm using TCGA Lung cancer data. I'm interested in doing differential analysis between Lung vs Normal. Before DEA, to check the distance between each pair of samples I plotted an MDS plot:</p>
  
  <p><a href="https://i.sstatic.net/85H6M.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/85H6M.png" alt="MDS plot"></a></p>
  
  <p>In this, I see some Tumor samples are clustered with Normals. As this is a two dimensional plot, it is a little hard to say. </p>
  
  <p>So, I made a clustering heatmap with the top 10% genes:</p>
  
  <p><a href="https://i.sstatic.net/x2s3E.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/x2s3E.jpg" alt="clustering heatmap"></a> (Where brown color is Normal and black color is Tumor). I also see some Normals clustering with Tumor here, and some Tumor are with the Normals. </p>
  
  <p>My question is: Should I exclude those tumor samples which are clustering with the normals for differential analysis? Is it a good idea to remove samples based on clustering? </p>
  
  <p>Note: I didn't see any technical bias of those samples</p>
  
Tags: <rna-seq><differential-expression><clustering>
Title: Is removing samples based on clustering for downstream analysis a right choice?
ThreadText: # Is removing samples based on clustering for downstream analysis a right choice?
  
  (Asked by: username_0 on 2018-06-20)
  
  I'm using TCGA Lung cancer data. I'm interested in doing differential analysis between Lung vs Normal. Before DEA, to check the distance between each pair of samples I plotted an MDS plot:
  
  [![MDS plot](https://i.sstatic.net/85H6M.png)](https://i.sstatic.net/85H6M.png)
  
  In this, I see some Tumor samples are clustered with Normals. As this is a two dimensional plot, it is a little hard to say.
  
  So, I made a clustering heatmap with the top 10% genes:
  
  [![clustering heatmap](https://i.sstatic.net/x2s3E.jpg)](https://i.sstatic.net/x2s3E.jpg) (Where brown color is Normal and black color is Tumor). I also see some Normals clustering with Tumor here, and some Tumor are with the Normals.
  
  My question is: Should I exclude those tumor samples which are clustering with the normals for differential analysis? Is it a good idea to remove samples based on clustering?
  
  Note: I didn't see any technical bias of those samples
  
  **Tags:** rna-seq, differential-expression, clustering
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2018-06-20. Score: 5 | Accepted answer
  
  I would be very hesitant to blindly exclude those samples based on the clustering. Check to see if the clusters actually denote some sort of batch effect, since it's not like all of the TCGA datasets were processed at the same place or time. Check for clinical covariates too. Also, do a PCA and check the genes with high loading on the relevant PC. If it appears that the genes are indicators for cancerous state, then it's likely the samples are just mislabeled and you can reasonably exclude them. Otherwise keep them in so your results better represent the underlying biological variability.
  
  BTW, your heatmap and MDS plots are what I'd expect from a cancer dataset. You occasionally see a tumour with the normal samples and vice versa, since the problematic genes aren't always in the top 10% and there is likely some heterogeneity in the cancer samples.
  
  > **Comments:**
  > **username_0**: How do I check the batch effect of the samples? can I check that with edgeR or Deseq2?  
  > **username_1**: Go through the meta information and see if there are other things (sequencing data, processing center, etc.) that correlate with the clusters.  
  
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