<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>

CC BY-SA 4.0
2018-05-24T14:13:13.860Z
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?
gc5
CC BY-SA 4.0
2018-05-24T15:40:51.097Z
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.
Jared Andrews
CC BY-SA 4.0
2018-05-24T15:52:59.580Z
I think it may be the only solution in this case. Thanks
gc5
CC BY-SA 4.0
2018-05-24T14:08:47.887Z
2018-05-24T14:08:47.887Z
Jared Andrews
<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>

<cnv><copy-number><snp-chip>
Compute copy number from cases and controls
# 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?
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  

---

