Answers.Body: <p><a href="http://portals.broadinstitute.org/cgi-bin/cancer/publications/pub_paper.cgi?mode=view&amp;paper_id=216&amp;p=t" rel="nofollow noreferrer">GISTIC</a> does exactly this, and is very possibly the tool used in your link above. </p>
  
  <p>The input files need to be in a different format than what you currently have, but if you're using a SNP6 array, there are guides out there that will tell you how to get the proper files from your <code>.cel</code> files.</p>
  
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Answers.CreationDate: 2018-05-24T14:13:13.860Z
Answers.Text: Thanks. Unfortunately the data has been presented to me in this format. I could try to get the original `.cel` files but I think they'll be unavailable. Do you know of any methodology to get the same result but with the data format I provided in the question?
Answers.UserDisplayName: gc5
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Answers.CreationDate: 2018-05-24T15:40:51.097Z
Answers.Text: Well, you could hack together something simple in python or R that takes all the controls, creates lists of amplified/deleted regions, then removes the CNVs in the cases that intersect those regions (maybe by a minimum amount, like 30%). It kind of depends on how perfect you want your analysis to be. Removing the regions that are "variable" in the controls is pretty easy.
Answers.UserDisplayName: Jared Andrews
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Answers.CreationDate: 2018-05-24T15:52:59.580Z
Answers.Text: I think it may be the only solution in this case. Thanks
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Answers.UserDisplayName: Jared Andrews
Body: <p>I have some data on Copy Number Variation (SNP chip) for a population of samples.</p>
  
  <p>In particular, I have a set of samples (considered as cases) which display a specific disease phenotype, and another set (considered as controls) which do not. The cases have not a matched-control. All the controls are taken as a random sample from the population that does not present the disease.</p>
  
  <p>For both cases and controls I have the copy number of some regions. How can I compute the copy number change in cases, such as in <a href="https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/CNV_Pipeline/" rel="noreferrer">GDC</a>?</p>
  
  <p>Should I use (as described in the previously linked page):</p>
  
  <blockquote>
    <p>performing tangent normalization, which subtracts variation that is found in a set of normal samples</p>
  </blockquote>
  
  <p>and do you know of any tool to perform this computation?</p>
  
  <p>The data I have is formatted in this way:</p>
  
  <pre><code>Sample_ID   chrom      start           end  CN
  Sample11       19   11991477      12133823   1
  Sample11        2   52260564      52431658   1
  Sample12        7    5721757       5896192   3
  Sample13       10    2269963       2473585   3
  </code></pre>
  
Tags: <cnv><copy-number><snp-chip>
Title: Compute copy number from cases and controls
ThreadText: # Compute copy number from cases and controls
  
  (Asked by: username_0 on 2018-05-14)
  
  I have some data on Copy Number Variation (SNP chip) for a population of samples.
  
  In particular, I have a set of samples (considered as cases) which display a specific disease phenotype, and another set (considered as controls) which do not. The cases have not a matched-control. All the controls are taken as a random sample from the population that does not present the disease.
  
  For both cases and controls I have the copy number of some regions. How can I compute the copy number change in cases, such as in [GDC](https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/CNV_Pipeline/)?
  
  Should I use (as described in the previously linked page):
  
  > performing tangent normalization, which subtracts variation that is found in a set of normal samples
  
  and do you know of any tool to perform this computation?
  
  The data I have is formatted in this way:
  
  ```
  Sample_ID   chrom      start           end  CN
  Sample11       19   11991477      12133823   1
  Sample11        2   52260564      52431658   1
  Sample12        7    5721757       5896192   3
  Sample13       10    2269963       2473585   3
  
  ```
  
  **Tags:** cnv, copy-number, snp-chip
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2018-05-24. Score: 3 | Accepted answer
  
  [GISTIC](http://portals.broadinstitute.org/cgi-bin/cancer/publications/pub_paper.cgi?mode=view&paper_id=216&p=t) does exactly this, and is very possibly the tool used in your link above.
  
  The input files need to be in a different format than what you currently have, but if you're using a SNP6 array, there are guides out there that will tell you how to get the proper files from your `.cel` files.
  
  > **Comments:**
  > **username_0**: Thanks. Unfortunately the data has been presented to me in this format. I could try to get the original `.cel` files but I think they'll be unavailable. Do you know of any methodology to get the same result but with the data format I provided in the question?  
  > **username_1**: Well, you could hack together something simple in python or R that takes all the controls, creates lists of amplified/deleted regions, then removes the CNVs in the cases that intersect those regions (maybe by a minimum amount, like 30%). It kind of depends on how perfect you want your analysis to be. Removing the regions that are "variable" in the controls is pretty easy.  
  > **username_0**: I think it may be the only solution in this case. Thanks  
  
  ---
  
  